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Apr 8

Top 10 security categories where VC funding trails Gartner’s 2026 growth forecast, Crunchbase data

Top 10 security categories where VC funding trails Gartner’s 2026 growth forecast, Crunchbase data

Two of Gartner’s 10 fastest-growing security categories have zero venture-backed startups. Firewall equipment, a $26.7 billion market by 2030, and pure-play cloud access security brokers, projected at $7.1 billion, are controlled entirely by incumbent vendors. No startup has raised a dollar in either category since January 2025.

I cross-referenced Gartner’s 1Q26 Information Security forecast against CB Insights, Crunchbase, and PitchBook funding data for every one of the 10 fastest-growing security categories. The question: where is venture capital following Gartner’s growth signal, and where is it missing?

The answer is stark. $93.2 billion in projected 2030 spending across these 10 categories. $11.2 billion in total VC raised by 59 funded startups. That is an 8.3:1 gap between where enterprise demand is heading and where startup capital is flowing. In 5 of 10 categories, the gap exceeds 12:1. As I detailed in last week’s analysis of the 10 fastest-growing categories, growth is concentrating in cloud infrastructure, proactive intelligence, and privacy compliance. The VC data tells you whether anyone is building what CISOs need to buy.

“Cybersecurity leaders are navigating uncharted territory this year as these forces converge, testing the limits of their teams in an environment defined by constant change,” said Alex Michaels, Director at Gartner. The spending data confirms it. The startup funding data shows the supply side has not caught up.

Two of Gartner’s 10 fastest-growing security categories have zero venture-backed startups. Firewall equipment, a $26.7 billion market by 2030, and pure-play cloud access security brokers, projected at $7.1 billion, are controlled entirely by incumbent vendors. No startup has raised a dollar in either category since January 2025. I cross-referenced Gartner’s 1Q26 Information Security forecast against CB Insights, Crunchbase, and PitchBook funding data for every one of the 10 fastest-growing security categories. The question: where is venture capital following Gartner’s growth signal, and where is it missing? The answer is stark. $93.2 billion in projected 2030 spending across these 10 categories. $11.2 billion in total VC raised by 59 funded startups. That is an 8.3:1 gap between where enterprise demand is heading and where startup capital is flowing. In 5 of 10 categories, the gap exceeds 12:1. As I detailed in last week’s analysis of the 10 fastest-growing categories, growth is concentrating in cloud infrastructure, proactive intelligence, and privacy compliance. The VC data tells you whether anyone is building what CISOs need to buy. “Cybersecurity leaders are navigating uncharted territory this year as these forces converge, testing the limits of their teams in an environment defined by constant change,” said Alex Michaels, Director at Gartner. The spending data confirms it. The startup funding data shows the supply side has not caught up. ▼ GRAPHIC: GRAPHIC 2 — Paired bar chart: Gartner 2030 projection vs. VC raised (insert before master table) ▼ Figure 2: Gartner 2030 projections (dark) vs. total VC raised (light) for each of the 10 categories. The master table: Gartner forecast vs. startup funding by category I mapped each Gartner category against every cybersecurity startup that raised equity or debt since January 2025. Each company is assigned to one primary category to avoid double-counting. Gap Ratio is the Gartner 2030 market projection divided by total VC raised. Higher means wider gap. # Gartner Security Category 2025-26 GR 5yr CAGR 2030 Proj Startups Total VC Gap Ratio Verdict 1 Cloud Access Security Brokers (CASB) 27.2% 24.3% $7.1B 4 $182M 39:1 Critical Gap 2 Firewall Equipment (NGFW/FWaaS) 15.9% 9.1% $26.7B 0 $0 ∞ Incumbent Lock 3 Cloud Security Posture Mgmt (CSPM) 33.4% 27.6% $16.2B 6 $752M 21.5:1 Underfunded 4 Vulnerability Assessment 15.7% 12.0% $6.4B 6 $306M 20.9:1 Underfunded 5 Cloud Workload Protection (CWPP) 25.9% 21.0% $16.1B 8 $1.28B 12.6:1 Underfunded 6 Subject Rights Request Automation 16.2% 12.3% $2.3B 2 $240M 9.6:1 M&A Absorbed 7 Network Detection & Response (NDR) 15.6% 12.4% $4.1B 4 $701M 5.9:1 Moderate Gap 8 Zero Trust Network Access (ZTNA) 23.0% 20.9% $6.4B 10 $1.94B 3.3:1 VC Ahead 9 Threat Intelligence 27.3% 21.1% $6.9B 12 $3.16B 2.2:1 Oversupplied 10 Consent & Preference Mgmt 22.1% 18.6% $2.0B 7 $2.61B 0.8:1 Oversupplied Source: Gartner 1Q26 Information Security Market Current Outlook (G00846158, March 2026). Growth rates in constant currency. Funding data from CB Insights, Crunchbase, PitchBook. Analysis by Software Strategies Blog, April 2026. The table splits cleanly into three tiers. Five categories are underfunded or locked out (Gap Ratio above 9:1). Two sit in the middle. Three are oversupplied or ahead of the Gartner signal. I update this comparison every quarter as Gartner releases new forecast data. Get the next one in your inbox. The 3 widest gaps Gap #1: CASB — 39:1, and the category is disappearing Gartner projects cloud access security brokers reaching $7.1 billion by 2030 at a 24.3% CAGR. Total startup funding since January 2025: $182 million across just 4 companies. Company Total Funding Last Round Lead Investor HQ Founded Reco $85M $30M Series B Zeev Ventures New York 2020 Seraphic Security $44M $29M Series A GreatPoint Ventures Palo Alto / Israel 2020 Nudge Security $35M $22.5M Series A Cerberus Ventures Austin, TX 2021 Spin.AI $18M+ Undisclosed (K1) K1 Investment Mgmt Palo Alto 2017 The gap is structural, not cyclical. Pure-play CASB startups no longer exist as a standalone category. The buying motion has shifted to SASE platforms. Cato Networks raised $409 million in a Series G in June 2025, but that money funds a unified SASE platform spanning CASB, ZTNA, and SD-WAN. For CISOs, the implication is direct. If your CASB requirement is standalone, your vendor options are Netskope, Skyhigh, Forcepoint, and a handful of sub-$50 million startups. Expect fewer competitive bids and less pricing leverage than in categories where VC is abundant. Gap #2: CSPM — 21.5:1, the fastest-growing category is still starved Cloud security posture management is the single fastest-growing category in Gartner’s entire information security forecast. 33.4% growth in 2026. $16.2 billion by 2030 at a 27.6% five-year CAGR. Total startup funding: $752 million across 6 companies. Company Total Funding Last Round Lead Investor HQ Founded Upwind Security $430M $250M Series B Bessemer Venture Partners San Francisco 2022 Noma Security $132M $100M Series B Evolution Equity Partners New York / Tel Aviv 2023 Sentra $100M+ $50M Series B Key1 Capital New York / Tel Aviv 2021 Native Security $42M $31M Series A Ballistic Ventures Tel Aviv / Seattle 2024 Mondoo $32.5M $17.5M Series A Ext HV Capital San Francisco 2020 AccuKnox $15M $4M Venture DreamIt Ventures Menlo Park 2020 Upwind alone accounts for 57% of all CSPM startup capital. It hit unicorn status at a $1.5 billion valuation in January 2026. But one company cannot fill a $16.2 billion market. Alphabet’s $32 billion acquisition of Wiz in March 2026 removed the largest independent cloud security company from the startup market entirely. In my analysis of $3.6 billion in agentic AI security funding, I tracked how M&A is filling gaps that VC has not. CSPM is a category where that pattern is accelerating. Gap #3: Vulnerability Assessment — 20.9:1, the most active seed-stage category Gartner projects vulnerability assessment at $6.4 billion by 2030. Total VC: $306 million across 6 companies. Company Total Funding Last Round Lead Investor HQ Founded Zafran Security $130M $60M Series C Menlo Ventures New York 2022 Seemplicity $82M+ $50M Series B Sienna Venture Capital Tel Aviv 2020 Cogent Security $53M $42M Series A Bain Capital Ventures San Francisco 2024 Nucleus Security $20M+ $20M Series C Undisclosed Tampa, FL 2018 Onit Security $11M $11M Seed Hetz Ventures Tel Aviv 2025 ZAST.AI ~$10M $6M Pre-A Hillhouse Capital Seattle 2024 ▼ GRAPHIC: GRAPHIC 3 — Top funded startups in underfunded categories (insert after Vuln Assess table) ▼ Figure 3: Total funding by startup across the three underfunded categories (CSPM, CWPP, Vulnerability Assessment). This is the category with the most active early-stage investment. Cogent Security and Onit Security both use AI agents for autonomous vulnerability remediation. Zafran tripled ARR since its prior round. The agentic AI thesis is landing hardest in vulnerability management, and the funding trail shows it. Balbix, which had raised $98.6 million, was acquired in November 2025. For CISOs evaluating this category, the vendor field is young and fragmented. Half of the funded companies were founded in 2024 or later. Where VC is ahead of Gartner Three categories show the opposite pattern. In Consent & Preference Management, OneTrust alone has raised $2.1 billion against a $2.0 billion Gartner projection. In Threat Intelligence, $3.16 billion in VC against a $6.9 billion projection, but Dataminr ($1.24B) and ReliaQuest ($1.13B) account for 75% of the total. In ZTNA, Cato Networks’ $1.1 billion alone represents 57% of all category funding. ▼ GRAPHIC: GRAPHIC 4 — Concentration risk donut charts (insert after VC-ahead section) ▼ Figure 4: Single-company concentration in CWPP, ZTNA, and Threat Intelligence funding. The concentration risk matters. Strip out the single largest company in each oversupplied category and the gap ratios invert. Consent without OneTrust: $510 million, Gap Ratio 3.9:1. Threat Intelligence without Dataminr and ReliaQuest: $790 million, Gap Ratio 8.7:1. ZTNA without Cato: $835 million, Gap Ratio 7.7:1. M&A is filling the gaps VC won’t When startups cannot fill the gap, platform vendors acquire. The $3.6 billion in agentic AI security funding and $96 billion in M&A I tracked in March tells this story at scale. Palo Alto Networks assembled $29 billion in acquisitions. ServiceNow spent $11.6 billion. Alphabet closed $32 billion for Wiz. Veeam acquired Securiti.ai for $1.725 billion, removing the leading subject rights automation vendor from the independent market. Forrester’s 2026 cybersecurity budget data confirms the same pattern from the buyer side. Security budgets are growing, but the spend is concentrating in fewer, larger platform purchases. What this means for CISOs In underfunded categories, build internally or accept platform vendor lock-in. CSPM, vulnerability assessment, and CWPP all have Gap Ratios above 12:1. Fewer funded startups means fewer competitive alternatives. If your preferred vendor gets acquired, as Wiz, Securiti.ai, and Balbix all were, your roadmap depends on the acquirer’s priorities, not yours. In oversupplied categories, use the competition for better pricing. ZTNA, threat intelligence, and consent management have abundant VC-backed alternatives. Negotiate harder. Run competitive evaluations with three or more vendors. The funding data tells you which categories give you leverage. Watch for single-company concentration. Chainguard holds 70% of all CWPP startup funding. Cato holds 57% of ZTNA. OneTrust holds 80% of consent management. If any of these companies pivots, gets acquired, or fails, the category funding picture changes overnight. Bottom line Gartner projects $93.2 billion in 2030 spending across the 10 fastest-growing security categories. Venture capital has funded $11.2 billion in startups since January 2025. The 8.3:1 blended gap tells you the overall story. The category-level ratios tell you where to act. Cloud security posture management, vulnerability assessment, and cloud workload protection are growing at 2x to 3x the market average but remain underfunded relative to Gartner’s projections. Two categories, firewall equipment and pure-play CASB, have no startup investment at all. Platform vendors are filling gaps through acquisition at a pace that is reshaping every competitive evaluation. This is the third quarter I have tracked Gartner’s security forecast against independent funding data. The gap between enterprise demand and startup supply keeps widening. Gartner’s 2Q26 forecast lands in July. I will break down the updated Gap Ratios the week it drops. I wrote a shorter editorial take on what these gaps mean for CISO budgets on my Substack. Source: Gartner, Information Security Market Current Outlook, Worldwide, 1Q26 (G00846158), March 2026. Growth rates in constant currency. Dollar figures in current U.S. dollars. Funding data from CB Insights, Crunchbase, PitchBook, Statista. Cross-referenced against company press releases. Analysis by Software Strategies Blog.

The master table: Gartner forecast vs. startup funding by category

I mapped each Gartner category against every cybersecurity startup that raised equity or debt since January 2025. Each company is assigned to one primary category to avoid double-counting. Gap Ratio is the Gartner 2030 market projection divided by total VC raised. Higher means wider gap.

# Gartner Security Category 2025-26 GR 5yr CAGR 2030 Proj Startups Total VC Gap Ratio Verdict
1 Cloud Access Security Brokers (CASB) 27.2% 24.3% $7.1B 4 $182M 39:1 Critical Gap
2 Firewall Equipment (NGFW/FWaaS) 15.9% 9.1% $26.7B 0 $0 ∞ Incumbent Lock
3 Cloud Security Posture Mgmt (CSPM) 33.4% 27.6% $16.2B 6 $752M 21.5:1 Underfunded
4 Vulnerability Assessment 15.7% 12.0% $6.4B 6 $306M 20.9:1 Underfunded
5 Cloud Workload Protection (CWPP) 25.9% 21.0% $16.1B 8 $1.28B 12.6:1 Underfunded
6 Subject Rights Request Automation 16.2% 12.3% $2.3B 2 $240M 9.6:1 M&A Absorbed
7 Network Detection & Response (NDR) 15.6% 12.4% $4.1B 4 $701M 5.9:1 Moderate Gap
8 Zero Trust Network Access (ZTNA) 23.0% 20.9% $6.4B 10 $1.94B 3.3:1 VC Ahead
9 Threat Intelligence 27.3% 21.1% $6.9B 12 $3.16B 2.2:1 Oversupplied
10 Consent & Preference Mgmt 22.1% 18.6% $2.0B 7 $2.61B 0.8:1 Oversupplied

Source: Gartner 1Q26 Information Security Market Current Outlook (G00846158, March 2026). Growth rates in constant currency. Funding data from CB Insights, Crunchbase, PitchBook. Analysis by Software Strategies Blog, April 2026.

The table splits cleanly into three tiers. Five categories are underfunded or locked out (Gap Ratio above 9:1). Two sit in the middle. Three are oversupplied or ahead of the Gartner signal.

I update this comparison every quarter as Gartner releases new forecast data. Get the next one in your inbox.

The 3 widest gaps

Gap #1: CASB — 39:1, and the category is disappearing

Gartner projects cloud access security brokers reaching $7.1 billion by 2030 at a 24.3% CAGR. Total startup funding since January 2025: $182 million across just 4 companies.

Company Total Funding Last Round Lead Investor HQ Founded
Reco $85M $30M Series B Zeev Ventures New York 2020
Seraphic Security $44M $29M Series A GreatPoint Ventures Palo Alto / Israel 2020
Nudge Security $35M $22.5M Series A Cerberus Ventures Austin, TX 2021
Spin.AI $18M+ Undisclosed (K1) K1 Investment Mgmt Palo Alto 2017

The gap is structural, not cyclical. Pure-play CASB startups no longer exist as a standalone category. The buying motion has shifted to SASE platforms. Cato Networks raised $409 million in a Series G in June 2025, but that money funds a unified SASE platform spanning CASB, ZTNA, and SD-WAN.

For CISOs, the implication is direct. If your CASB requirement is standalone, your vendor options are Netskope, Skyhigh, Forcepoint, and a handful of sub-$50 million startups. Expect fewer competitive bids and less pricing leverage than in categories where VC is abundant.

Gap #2: CSPM — 21.5:1, the fastest-growing category is still starved

Cloud security posture management is the single fastest-growing category in Gartner’s entire information security forecast. 33.4% growth in 2026. $16.2 billion by 2030 at a 27.6% five-year CAGR. Total startup funding: $752 million across 6 companies.

Company Total Funding Last Round Lead Investor HQ Founded
Upwind Security $430M $250M Series B Bessemer Venture Partners San Francisco 2022
Noma Security $132M $100M Series B Evolution Equity Partners New York / Tel Aviv 2023
Sentra $100M+ $50M Series B Key1 Capital New York / Tel Aviv 2021
Native Security $42M $31M Series A Ballistic Ventures Tel Aviv / Seattle 2024
Mondoo $32.5M $17.5M Series A Ext HV Capital San Francisco 2020
AccuKnox $15M $4M Venture DreamIt Ventures Menlo Park 2020

Upwind alone accounts for 57% of all CSPM startup capital. It hit unicorn status at a $1.5 billion valuation in January 2026. But one company cannot fill a $16.2 billion market.

Alphabet’s $32 billion acquisition of Wiz in March 2026 removed the largest independent cloud security company from the startup market entirely. In my analysis of $3.6 billion in agentic AI security funding, I tracked how M&A is filling gaps that VC has not. CSPM is a category where that pattern is accelerating.

Gap #3: Vulnerability Assessment — 20.9:1, the most active seed-stage category

Gartner projects vulnerability assessment at $6.4 billion by 2030. Total VC: $306 million across 6 companies.

Company Total Funding Last Round Lead Investor HQ Founded
Zafran Security $130M $60M Series C Menlo Ventures New York 2022
Seemplicity $82M+ $50M Series B Sienna Venture Capital Tel Aviv 2020
Cogent Security $53M $42M Series A Bain Capital Ventures San Francisco 2024
Nucleus Security $20M+ $20M Series C Undisclosed Tampa, FL 2018
Onit Security $11M $11M Seed Hetz Ventures Tel Aviv 2025
ZAST.AI ~$10M $6M Pre-A Hillhouse Capital Seattle 2024

 

This is the category with the most active early-stage investment. Cogent Security and Onit Security both use AI agents for autonomous vulnerability remediation. Zafran tripled ARR since its prior round. The agentic AI thesis is landing hardest in vulnerability management, and the funding trail shows it.

Balbix, which had raised $98.6 million, was acquired in November 2025. For CISOs evaluating this category, the vendor field is young and fragmented. Half of the funded companies were founded in 2024 or later.

Where VC is ahead of Gartner

Three categories show the opposite pattern. In Consent & Preference Management, OneTrust alone has raised $2.1 billion against a $2.0 billion Gartner projection. In Threat Intelligence, $3.16 billion in VC against a $6.9 billion projection, but Dataminr ($1.24B) and ReliaQuest ($1.13B) account for 75% of the total. In ZTNA, Cato Networks’ $1.1 billion alone represents 57% of all category funding.

The concentration risk matters. Strip out the single largest company in each oversupplied category and the gap ratios invert. Consent without OneTrust: $510 million, Gap Ratio 3.9:1. Threat Intelligence without Dataminr and ReliaQuest: $790 million, Gap Ratio 8.7:1. ZTNA without Cato: $835 million, Gap Ratio 7.7:1.

M&A is filling the gaps VC won’t

When startups cannot fill the gap, platform vendors acquire. The $3.6 billion in agentic AI security funding and $96 billion in M&A I tracked in March tells this story at scale. Palo Alto Networks assembled $29 billion in acquisitions. ServiceNow spent $11.6 billion. Alphabet closed $32 billion for Wiz. Veeam acquired Securiti.ai for $1.725 billion, removing the leading subject rights automation vendor from the independent market.

Forrester’s 2026 cybersecurity budget data confirms the same pattern from the buyer side. Security budgets are growing, but the spend is concentrating in fewer, larger platform purchases.

What this means for CISOs

In underfunded categories, build internally or accept platform vendor lock-in. CSPM, vulnerability assessment, and CWPP all have Gap Ratios above 12:1. Fewer funded startups means fewer competitive alternatives. If your preferred vendor gets acquired, as Wiz, Securiti.ai, and Balbix all were, your roadmap depends on the acquirer’s priorities, not yours.

In oversupplied categories, use the competition for better pricing. ZTNA, threat intelligence, and consent management have abundant VC-backed alternatives. Negotiate harder. Run competitive evaluations with three or more vendors. The funding data tells you which categories give you leverage.

Watch for single-company concentration. Chainguard holds 70% of all CWPP startup funding. Cato holds 57% of ZTNA. OneTrust holds 80% of consent management. If any of these companies pivots, gets acquired, or fails, the category funding picture changes overnight.

Bottom line

Gartner projects $93.2 billion in 2030 spending across the 10 fastest-growing security categories. Venture capital has funded $11.2 billion in startups since January 2025. The 8.3:1 blended gap tells you the overall story. The category-level ratios tell you where to act.

Cloud security posture management, vulnerability assessment, and cloud workload protection are growing at 2x to 3x the market average but remain underfunded relative to Gartner’s projections. Two categories, firewall equipment and pure-play CASB, have no startup investment at all. Platform vendors are filling gaps through acquisition at a pace that is reshaping every competitive evaluation.

This is the third quarter I have tracked Gartner’s security forecast against independent funding data. The gap between enterprise demand and startup supply keeps widening. Gartner’s 2Q26 forecast lands in July. I will break down the updated Gap Ratios the week it drops. I wrote a shorter editorial take on what these gaps mean for CISO budgets on my Substack.

Source: Gartner, Information Security Market Current Outlook, Worldwide, 1Q26 (G00846158), March 2026. Growth rates in constant currency. Dollar figures in current U.S. dollars. Funding data from CB Insights, Crunchbase, PitchBook, Statista. Cross-referenced against company press releases. Analysis by Software Strategies Blog.

 

Mar 28

$3.6 Billion in Crunchbase funding, $96 Billion in M&A, and 10 Agentic AI security startups Reshaping 2026

Palo Alto Networks spent $29 billion acquiring three companies. ServiceNow spent $11.6 billion on three more. Alphabet paid $32 billion for Wiz. The startups building agentic AI defenses raised $3.6 billion. Total MCP security funding for 17,000+ deployed servers: $40 million. Then RSAC 2026 happened.

Read more

Feb 26

Roundup of agentic AI forecasts and market estimates, 2026

Roundup of agentic AI forecasts and market estimates, 2026

Agentic AI spending is projected to reach $201.9 billion in 2026 (Gartner), overtaking chatbot spending by 2027.  Four independent firms size the standalone market at $7–8 billion with 40%+ CAGRs. But adoption lags the money: only 23% of organizations have scaled agent deployments (McKinsey), and 40% of projects face cancellation by 2027 (Gartner).

Fortune Business Insights projects $7.29 billion in 2025, reaching $139.19 billion by 2034 at 40.5% CAGR. Precedence Research sizes it at $7.55 billion in 2025, growing to $199.05 billion by 2034 at 43.84% CAGR. MarketsandMarkets puts the figure at $7.06 billion in 2025, reaching $93.20 billion by 2032 at 44.6% CAGR. Deloitte’s TMT Predictions 2025 estimates $8.5 billion in 2026, growing to $35 to $45 billion by 2030.

Every major forecast agrees on direction. None agrees on scale. The standalone agentic AI market lands between $7 billion and $8.5 billion. Gartner’s broader view, counting agentic capabilities embedded across enterprise software, reaches $201.9 billion in 2026. That 25x gap is not a contradiction. It is a measurement problem, and the takeaways below reflect both realities. The following are the key takeaways from agentic AI forecasts published in 2026 so far:

Key takeaways

Worldwide AI spending will reach $2.52 trillion in 2026, growing 44% year-over-year. That number jumped roughly $500 billion from the September forecast, which had pegged the market just above $2 trillion. Infrastructure takes $1.37 trillion, 54% of total spend. AI software follows at $452.5 billion, up 60%. AI services add $588.6 billion. AI-optimized servers alone account for $421.6 billion, growing to 49%. Gartner expects spending to grow by another 30% in 2027 and surpass $3 trillion. I have tracked these forecasts through multiple iterations. The revisions keep going in one direction. Source: Gartner press release, January 15, 2026

 

Gartner projects $4.71 trillion in global AI spending by 2029. The fastest growth isn’t in infrastructure. Synthetic data generation leads all categories at 178% CAGR, followed by the broader AI Data market at 155%. Agentic AI compounds at 119%, expanding from $15 billion to $753 billion by 2029. AI Infrastructure, the largest category by dollars, grows at just 29%. The money is following the bottlenecks. Source:  Gartner 4Q25: $4.71T AI Market Proves Agentic AI and Data Readiness Are the Only Race That Matters, Software Strategies Blog, January 22, 2026 Link: https://softwarestrategiesblog.com/2026/01/22/gartner-4q25-agentic-ai-data-readiness-4-71t-market/

 

The AI cybersecurity market is predicted to hit $51.3 billion in 2026, nearly doubling from $25.9 billion in 2025. But the category masks a structural imbalance. AI-amplified security, where AI defends the enterprise, captures 94.5% of spending at $48.5 billion. Securing AI, where the enterprise defends its own AI systems, gets $2.8 billion. Enterprises are investing 17x more in using AI as a security tool than in protecting the AI itself. Both sub-segments grow at similar CAGRs (74% vs. 72%), which means the dollar gap widens every year. By 2029, AI-amplified security reaches $160.4 billion, while securing AI hits just $11.6 billion. One is a tool. The other is the thing that needs protecting. Source: Gartner Forecasts Agentic AI Will Overtake Chatbot Spending by 2027, Software Strategies Blog, February 16, 2026 Link: https://softwarestrategiesblog.com/2026/02/16/gartner-forecasts-agentic-ai-overtakes-chatbot-spending-2027/

 

AI Data sits alone in the upper-right quadrant of Gartner’s spending map, compounding at 155% CAGR with 277% growth in 2026. AI Cybersecurity and AI Models cluster above 67% CAGR. AI Infrastructure anchors the chart as the largest bubble, but grows at just 29%. Global AI spending reaches $1.8 trillion in 2025 and $4.7 trillion by 2029. The acceleration is not in compute. It is in data readiness, security architecture, and agentic capabilities. By 2028, software with agentic capabilities crosses 50% of total application software spend, up from 2% in 2024. Non-agentic software spending starts declining in 2027. Source:Data Readiness and Security Are Driving AI’s $4.7 Trillion Run, Software Strategies Blog, December 22, 2025 Link: https://softwarestrategiesblog.com/2025/12/22/data-readiness-security-driving-ai-4-7-trillion/

Gartner’s AI spending forecast reaches $2.53 trillion in 2026 and $4.71 trillion by 2029. Eight markets. One pattern. AI Infrastructure dominates absolute dollars at $1.37 trillion in 2026 but grows at just 29% CAGR. AI Data, the smallest segment at $3.1 billion, compounds at 155%. AI Cybersecurity nearly doubles to $51.3 billion. AI Software hits $452.5 billion, growing 60% year-over-year as agentic capabilities reshape the category. The growth rates tell you where the bottlenecks are breaking. Source: Data Readiness and Security Are Driving AI’s $4.7 Trillion Run, Software Strategies Blog, December 22, 2025 Link: https://softwarestrategiesblog.com/2025/12/22/data-readiness-security-driving-ai-4-7-trillion/

Nearly nine in ten organizations now use AI in at least one business function, up from 78% a year ago, but nearly two-thirds have not begun scaling it across the enterprise. Only 6% qualify as high performers where AI contributes more than 5% to EBIT. Sixty-two percent of organizations are at least experimenting with AI agents, yet in no individual business function are more than 10% scaling them. High performers are three times more likely than peers to fundamentally redesign workflows and three times more likely to have senior leaders demonstrating ownership of AI initiatives. More than one-third of high performers commit over 20% of their digital budgets to AI, and about three-quarters have reached the scaling phase, versus one-third of other organizations. Source: McKinsey / QuantumBlack, The state of AI in 2025: Agents, innovation, and transformation, November 2025

Valued at $638.23 billion in 2024, the global AI market is projected to reach $3,680.47 billion by 2034, expanding to a CAGR of 19.20%. North America holds 31.80% market share. The software segment dominates at 51.40%, while machine learning leads by technology at 36.70%. Healthcare is expected to record the highest CAGR of 36.50% across end-use segments. Among regions, Asia-Pacific is expected to grow at 19.8% CAGR from 2025 to 2034, with AI projected to add up to $3 trillion to the region’s GDP by 2030, driven by national AI strategies in China, India, and Japan. Source: Precedence Research, AI Market Size, Growth & Trends, September 2025

Nearly $7 trillion. That’s the capital outlay data centers will require by 2030 to keep pace with demand for compute power. Of that, $5.2 trillion goes toward AI-ready facilities and $1.5 trillion toward traditional IT workloads. Global demand for data center capacity could almost triple by 2030, with about 70% of new demand coming from AI workloads. Three investment scenarios range from $3.7 trillion (constrained demand) to $7.9 trillion (accelerated demand, adding 205 incremental GW). The 60% majority of investment—$3.1 trillion—flows to technology developers and designers producing chips and computing hardware. Source: McKinsey, The cost of compute: A $7 trillion race to scale data centers, April 2025

Inference already consumed half of all AI compute in 2025. That number will grow to two-thirds in 2026 and reach 75% of all AI compute needs by 2030. Global data center capacity is projected to nearly double from 103 gigawatts to 200 GW by 2030, yet U.S. data centers already face a capacity shortfall exceeding 11 GW, with the cumulative gap expected to exceed 40 GW by 2028. North American data center capacity alone will increase eightfold, from 5.6 GW in 2024 to 44 GW by 2030. Operators are increasingly deploying edge facilities closer to end users to reduce latency as inference-dominated workloads drive a fundamental redesign of data center architectures. Source: Avid Solutions, 13 Data Center Growth Projections, January 2026

 

Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually to the global economy, increasing the projected impact of all AI by 15 to 40%. About 75% of the value falls across four areas: customer operations, marketing and sales, software engineering, and R&D. Half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045—roughly a decade earlier than previously estimated. When embedding effects in existing software are included, the total economic benefit rises to $6.1 trillion to $7.9 trillion annually. Source: McKinsey, The economic potential of generative AI, June 2023

The global AI market hit $294.16 billion in 2025 and is projected to grow to $2,480.05 billion by 2034, at a CAGR of 26.60%. The Banking, financial services and insurance (BFSI) segment holds 18.90% market share, while healthcare is expected to record the highest CAGR of 36.50%. In the U.S. alone, the AI market was estimated at $146.09 billion in 2024 and is predicted to reach $851.46 billion by 2034. The number of AI companies funded globally in 2024 totaled 2,049, with U.S.-funded companies accounting for 1,143, signaling strong investor confidence in the sector’s expansion potential. Source: Fortune Business Insights, AI Market Size, Growth & Trends by 2034

Big Tech’s AI capex hit $405 billion in 2025, up from a $250 billion estimate at the start of the year. Sell-side analysts have underestimated AI spending every quarter for two years running. A decade ago, Big Tech’s trailing-twelve-month capex was $24 billion—15x less than today. AI data center costs are projected at $3 trillion to $8 trillion, with gigawatt capacity expected to grow 3.5x by 2030. Source: IO Fund, Big Tech’s $405B Bet, November 2025

The global AI market was valued at $371.71 billion in 2025 and is projected to reach $2,407.02 billion by 2032, growing at a CAGR of 30.6%. Hyperscalers accounted for 53% of chip purchases in 2023, spurring 156% market growth from 2023 to 2024. While demand from hyperscalers is expected to moderate, growth of 41% is still forecast from 2025 to 2026. Enterprises are moving from cloud reliance to in-house AI infrastructure investments, particularly for cost-effective inference solutions, as edge AI gains traction through AI-enabled PCs and mobile devices. Source: Markets and Markets, AI Market Report 2025-2032

At $602 billion projected for 2026, hyperscaler capex has entered uncharted territory. Amazon, Microsoft, Google, and Meta will each exceed $100 billion individually, pushing capital intensity to 45-57% of revenue. Total hyperscaler capex from 2025-2027 is projected at $1.15 trillion, more than double the $477 billion spent from 2022-2024. Morgan Stanley and JP Morgan suggest the technology sector may need to issue $1.5 trillion in new debt over the next few years to finance AI infrastructure construction. The sheer scale of debt issuance mirrors patterns seen during the fiber-optic buildout of the late 1990s. Source: Multiple sources compiled by Introl, January 2026

The number of software companies using consumption-based pricing more than doubled between 2015 and 2024, as AI introduces new variable costs that make traditional perpetual licenses obsolete. SaaS remains dominant, but the next wave is outcome-aligned pricing that scales with actual AI usage. Software businesses that successfully adopt consumption-based pricing aligned with usage and outcomes may be better positioned to capture AI-driven value and differentiate themselves in a rapidly evolving market where the cost of each AI inference adds a new variable to the P&L. Source: McKinsey, AI adjusts the software bill, January 27, 2026

Data center capacity needs for AI and non-AI workloads could almost triple by 2030, with AI capacity increasing 3.5 times and making up roughly 70% of the total. Under a continued-momentum scenario, total capacity demand rises from 82 GW in 2025 to 219 GW by 2030, with incremental AI capacity ranging from 13 GW in 2025 to 31 GW in 2030, totaling 124 GW of new AI capacity. Non-AI workloads grow from 38 GW to 64 GW over the same period. Average power densities in AI-ready data centers have more than doubled in just two years and are expected to rise nearly four times by 2027. Source: McKinsey, Data center demands (Week in Charts), May 2025

U.S. data-center spending exceeded half a trillion dollars in 2025. The U.S. and China drove a massive expansion in AI-related computing capacity through 2024, with the U.S. pulling further ahead in the first half of 2025. AI-related trade accounted for nearly half of all merchandise trade growth in that period, despite representing only 15% of total trade volume. The infrastructure boom is reshaping international commerce, with surging demand for servers, graphics cards, and related components essential to AI training and inference now a dominant force in global supply chains. Source: Federal Reserve Board, FEDS Notes: The Global Trade Effects of the AI Infrastructure Boom, February 2026

The generative AI market is expanding from $71.36 billion in 2025 to $890.59 billion by 2032, at a CAGR of 43.4%. North America accounted for 43.05% of global revenue in 2025. Text remains the dominant data modality due to its foundational role in enterprise workflows, while the services segment is gaining traction for scalability and cost-effectiveness. Foundation model delivery platforms verticalized adoption across industries, and the rapid scaling of AI-native infrastructure are the three key forces driving the market as of 2025. The 43.4% CAGR makes this one of the fastest-expanding technology subsegments in history. Source: MarketsandMarkets, Generative AI Market Report, Global Forecast to 2032

The generative AI market reached $37.89 billion in 2025 and is projected to hit $1.2 trillion by 2035, a 37% compound annual growth rate. Transformer architectures account for more than 42% of technology revenue, driven by text-to-image and text-to-video applications. Software captures over 65% of total revenue. North America holds 41% of the market. Asia-Pacific is the fastest-growing region at a 27.6% CAGR through 2035. Financial services is expected to lead sector growth at 36.4%, fueled by fraud detection, risk management, and regulatory compliance demands. Source: Precedence Research, Generative AI Market Size, January 2026

GPUs captured 89% of AI processor revenue in 2025, but FPGA and ASIC alternatives are growing at a 17% CAGR through 2031. Hardware accounted for 68% of all AI infrastructure spending last year. North America held 40% of the market, backed by $52.7 billion in CHIPS Act grants and hyperscalers operating roughly 60% of global AI compute capacity. Liquid cooling reached 18% of AI server racks as power densities crossed 100 kilowatts per rack, the threshold where air cooling fails. Asia-Pacific is projected to grow fastest at 16.4% CAGR through 2031, driven by China’s $50 billion semiconductor fund and $15 billion in hyperscaler commitments across India. Source: Mordor Intelligence, AI Infrastructure Market Size, Trends & Growth Drivers 2031

Nearly one in four Americans has already made a purchase through AI. Morgan Stanley Research estimates agentic shoppers will drive $190 billion to $385 billion in U.S. e-commerce spending by 2030, capturing 10% to 20% of market share. Grocery and consumer packaged goods lead adoption, with 49% of AI-assisted buyers transacting in those categories. AI shopping agent users are projected to reach 126 million by 2030, up from near zero today, while traditional e-commerce users decline from 264 million to 149 million over the same period. Source: Morgan Stanley Research, Agentic Commerce Market Impact Outlook, December 2025 Link: https://www.morganstanley.com/insights/articles/agentic-commerce-market-impact-outlook

Feb 16

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Gartner forecasts agentic AI will overtake chatbot spending by 2027

 

Agentic AI spending grows 141% in 2026 to $201.9 billion. By 2027, it will overtake chatbot and assistant spending for the first time. Then chatbot spending starts declining. I’ve tracked Gartner’s AI forecasts through multiple iterations. This crossover changes where security risk concentrates for every security professional reading this.

The crossover is in the segment-level data tables of Gartner’s Forecast: AI Spending, Worldwide, 2024–2029, 4Q25. The headline number is well known: $2.53 trillion in 2026, $4.7 trillion by 2029 at 33% CAGR. The segment breakdowns are not. Eight markets. Nineteen sub-segments. The sub-segment data tells a different story than the top line.

This is Gartner’s first dedicated AI spending forecast, and I’ve been waiting for it. Gartner states that comparisons to previous AI estimates are not meaningful because the scope widened, adding AI cybersecurity, agentic AI as a separate segment from chatbots, AI data technology, and expanded infrastructure coverage. Gartner writes, “This is the first iteration of the forecast on AI spending that Gartner has published. Gartner has significantly expanded and modified its AI forecast coverage. Spending comparisons to previous iterations are therefore not meaningful as the scope has widened. This includes both coverage of new markets and broadened definitions of the types of AI spending that are reflected in some market segments.”

Forrester’s Predictions 2026: Cybersecurity and Risk arrives at the same warning from a different angle: an agentic AI deployment will cause a publicly disclosed breach in 2026, leading to employee dismissals. Two firms. Same conclusion. The spending data explains why.

CAPTION: Total worldwide AI spending, 2024–2029. $1.14T to $4.71T. 33% CAGR. Growth decelerates from 54% (2025) to 16% (2029) as the base expands. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025).

The full market breakdown

AI infrastructure dominates at $1.37 trillion, 54% of the total. AI software follows at $452.5 billion, growing 60% year-over-year. AI services add $588.6 billion. AI cybersecurity and AI data are the outliers: growing at 74% and 155% CAGR, respectively, rates that dwarf everything else in the forecast.

Source: Gartner Forecast: AI Spending, Worldwide, 2024–2029, 4Q25 (December 19, 2025). All figures in U.S. dollars. CAGR = 2024–2029. Gartner press release: https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026

Infrastructure takes 54% of every AI dollar

AI-optimized servers alone account for $421.6 billion in 2026, growing to $699.7 billion by 2029. AI processing semiconductors add $289.4 billion. AI-optimized IaaS hits $38.3 billion at 71% CAGR, the fastest-growing infrastructure sub-segment. AI network fabric, a new category in this forecast, reaches $28.7 billion.

Infrastructure’s share drops from 54% to 48% by 2029 as software and services scale faster. The capital-intensive build-out phase is not over.

The agentic crossover nobody is planning for

Gartner now splits AI software into chatbots/assistants and agentic AI. The spending lines cross in 2027.

CAPTION: Agentic AI spending overtakes chatbot/assistant spending by 2027. Chatbots peak at $264.7B then decline. Agentic AI grows at 119% CAGR to $752.7B by 2029. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). AI Software segment, Table 1-2.

Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). CAGR = 2024–2029.

Chatbots talk to people. Agents act on behalf of people. They access databases, execute transactions, chain multi-step workflows without human approval at each step. The attack surface has moved well beyond conversation windows. Agents are autonomous decision engines with production access.

Gartner’s Top Trends in Cybersecurity for 2026 lists agentic AI oversight as the number-one trend. Forrester’s Predictions 2026: Cybersecurity and Risk goes further: an agentic AI deployment will cause a public breach this year, and employees will lose their jobs for it. Forrester senior analyst Paddy Harrington calls it a “cascade of failures,” not a single point of error. Two analyst firms. Different methodologies. Same conclusion. Security strategies built for chatbot-era risk have a shelf life measured in quarters, not years.

AI cybersecurity is two markets, not one

Gartner created a dedicated AI cybersecurity market for the first time in this forecast. It nearly doubles in 2026. But the category name hides a structural split that matters more than the growth rate.

Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). CAGR = 2024–2029.

Two sub-segments. Two very different problems.

AI-amplified security ($48.5 billion, 94.5% of the market) is what most enterprises mean when they say “AI cybersecurity.” This is AI working for your security team. Machine learning models that analyze network traffic patterns and flag anomalies faster than a human analyst can. Natural language processing that reads threat intelligence feeds and correlates indicators of compromise across millions of data points in seconds. Automated triage systems that prioritize which of the 11,000 daily alerts actually need a human response. AI-powered endpoint detection that identifies malware variants that signature-based tools miss. Behavioral analytics that learn what normal looks like for each user and flag deviations. Security orchestration platforms that automate incident response playbooks, reducing mean time to containment from hours to minutes.

This is the category where enterprises are spending aggressively. And for good reason. The math on analyst workloads demands it. Security operations centers are drowning in alerts, facing a persistent talent shortage, and defending attack surfaces that expand every quarter. AI-amplified tools address all three.

Securing AI ($2.8 billion, 5.5% of the market) is the other problem. AI-amplified security puts AI to work defending the enterprise. Securing AI reverses the relationship entirely — defending the AI itself. Protecting the models, the training data, the inference pipelines, the agent workflows, and the decision outputs that enterprises are deploying at $2.53 trillion in 2026. Prompt injection defenses. Model access controls. Training data poisoning detection. Output validation. Agent permission boundaries. Audit trails for autonomous decisions.

The distinction matters because they protect different things. AI-amplified security protects your enterprise using AI. Securing AI protects the AI itself. One is a tool. The other is the thing that needs protecting. Enterprises are investing 17 times more in the tool than in protecting the thing the tool runs on.

Shadow AI is not just employees using ChatGPT

Gartner names the mechanism driving AI software growth: vendor push. Software providers are integrating GenAI and agentic AI into existing product lines. AI software grows from $143 billion in 2024 to $981 billion by 2029 at 47% CAGR.

For CISOs, vendor push changes the equation. AI capabilities are being added to tools already in production. Often without explicit procurement decisions. The AI features embedded in your existing ERP, CRM, and developer platforms may already exceed what your security team has inventoried. Shadow AI is vendors activating AI inside products you already own.

The smallest market with the biggest growth rate

AI data technology: $134 million in 2024. $3.1 billion in 2026. $14.6 billion by 2029. The 155% CAGR is the highest in the forecast. The 277% year-over-year growth in 2026 is the steepest single-year jump of any segment.

Synthetic data generation is the standout sub-segment, going from $41 million to $6.8 billion by 2029. Gartner is direct: enterprises need AI-ready data with proper labeling, quality checks, and compliance. For organizations running AI projects on ungoverned data, the readiness gap compounds every quarter.

CAPTION: AI spending markets ranked by five-year CAGR. AI Data (155%) and AI Cybersecurity (74%) lead. AI Infrastructure is the largest by absolute dollars. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025).

Indirect services are the governance blind spot

Indirect AI services, where AI is a supporting component in a larger project, grow from $78.4 billion in 2024 to $255.9 billion in 2026 at 50% CAGR. Direct AI services hit $332.8 billion. By 2028, indirect overtakes direct.

Indirect AI means capabilities embedded in consulting and implementation projects that procurement does not classify as AI. If you cannot see it in your AI inventory, you cannot govern it.

Servers are a bigger market than AI software

AI-optimized servers alone hit $421.6 billion in 2026, just below the entire AI software market at $452.5 billion. By 2029, servers reach $699.7 billion. Cloud providers are building capacity for AI workloads that have not materialized at scale. The infrastructure is ahead of the applications.

The enterprise agentic stack is showing up in spending data

Gartner’s DSML segment includes a dedicated agent builder platforms sub-segment at $5.0 billion in 2026, reaching $13.7 billion by 2029. AI observability and governance adds $1.3 billion, growing to $4.0 billion. The xOps sub-segment (MLOps, DataOps, ModelOps) is the largest at $15.0 billion.

Together, these form the tooling layer for building, monitoring, and governing agents in production. The enterprise agentic stack is materializing in the spending data. Most organizations have not formalized it in their architecture.

The numbers that belong in your next board deck

If you take one thing from this forecast into a budget meeting, take this table. I built it from the raw spreadsheet data. Six years of AI deployment spending next to AI security spending. The bottom row is the one that gets the questions.

Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). All percentages derived from Gartner’s published data tables (Tables 1-1 and 1-2).

The ratio improves over time. Securing AI goes from 0.07% in 2024 to 0.25% by 2029. But watch the absolute numbers. In 2029, enterprises will spend $4.71 trillion deploying AI and $11.6 billion securing it. The percentage gets better. The dollar gap gets wider. Every year, the market grows its way into a larger exposure.

Where I think this lands

Three things worth tracking from the segment data:

The agentic crossover. Agentic AI overtakes chatbot spending in 2027. The enterprise risk profile shifts from conversational data leakage to autonomous decision-making at scale. CISOs who build agentic governance frameworks in 2026 position themselves before the inflection. The spending curve says the window is narrowing.

The securing-AI gap. $2.8 billion to protect AI systems in a year when $2.53 trillion deploys them. Enterprises are enthusiastic about using AI for defense. The investment in defending AI itself has not caught up.

Data readiness is the bottleneck. The 277% growth in AI data spending confirms that AI without governed data delivers diminished returns. Data classification investments directly enable or constrain AI ROI.

If your security budget is growing at 12% and AI deployment inside your enterprise is growing at 44%, the gap compounds every quarter. You cannot close it by holding steady. The organizations getting this right treat AI security as a proportion of AI deployment, not a fixed line item.

—

Sources

Gartner, Forecast: AI Spending, Worldwide, 2024–2029, 4Q25, December 19, 2025, ID G00843179.

Gartner press release (January 15, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026

Gartner, Top Trends in Cybersecurity for 2026 (February 5, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-02-05-gartner-identifies-the-top-cybersecurity-trends-for-2026

Gartner, IT Spending Forecast 1Q26 (February 3, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-02-03-gartner-forecasts-worldwide-it-spending-to-grow-10-point-8-percent-in-2026-totaling-6-point-15-trillion-dollars

Forrester, Predictions 2026: Cybersecurity and Risk (October 2025): https://www.forrester.com/blogs/predictions-2026-cybersecurity-and-risk/

All dollar figures in U.S. dollars. Growth rates and CAGR derived from Gartner’s published data tables (Tables 1-1 and 1-2).

Feb 10

Top 6 cybersecurity trends from Gartner’s 2026 Security Forecast

Over 57% of employees are using personal GenAI accounts for work. A third of them admit to uploading sensitive data into tools their security teams haven’t approved. Meanwhile, agentic AI is proliferating through no-code platforms and vibe coding, creating attack surfaces most CISOs can’t see, let alone govern. And quantum computing? No longer a 10-year planning horizon. It’s a 2030 action deadline.

Gartner’s Top Trends in Cybersecurity for 2026 report, released February 5, 2026, identifies six forces reshaping how CISOs must operate. These cut across governance, AI adoption, identity, workforce, and cryptographic strategy simultaneously. None of them is incremental.

The trends report lands alongside Gartner’s updated Forecast: Information Security, Worldwide, 2023–2029, 4Q25 (G00843183, December 18, 2025) and the Forecast Analysis: Information Security, Worldwide, 2026 (G00838442, February 5, 2026), which together project global information security spending reaching $244.2 billion in 2026, up 13.3% in current U.S. dollars. I’ve tracked this forecast through multiple quarterly updates. The trajectory keeps steepening. The six trends below explain where that money is going and why.

“Cybersecurity leaders are navigating uncharted territory this year as these forces converge, testing the limits of their teams in an environment defined by constant change,” said Alex Michaels, Director Analyst at Gartner. “This demands new approaches to cyber risk management, resilience, and resource allocation.”

The spending backdrop: $244 billion and accelerating

Before getting into the six trends, context matters. Gartner’s 4Q25 forecast shows the three major security segments all growing at double-digit constant currency rates in 2026:

Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183). Constant currency rates.

Cloud security remains the fastest-growing subsegment at 28.8% growth in 2026. Nothing else comes close. The combined cloud security market (cloud security posture management, cloud access security brokers, and cloud workload protection platforms) is projected to reach $32.4 billion by 2029, with a 25% CAGR in constant currency. I’ve been watching this subsegment accelerate for three quarters straight. CSPM alone is growing at a 31.30% CAGR.

 

Cloud security spending reaches $32.4 billion by 2029. CSPM leads at 31.30% CAGR. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)

Trend 1: Agentic AI demands cybersecurity oversight

This is the trend that touches everything else on this list. Employees and developers are deploying AI agents through no-code/low-code platforms and “vibe coding” at a pace that outstrips security governance. Unmanaged AI agent proliferation. Unsecured code. Compliance violations that most security teams don’t even have visibility into yet. That’s the picture Gartner is painting.

Gartner’s recommendation is blunt: cybersecurity leaders must identify both sanctioned and unsanctioned AI agents operating within their environments, enforce access controls and data guardrails, and develop incident response playbooks specific to agent-driven threats.

“While AI agents and automation tools are becoming increasingly accessible and practical for organizations to adopt, strategic cybersecurity planning for these technologies is essential,” said Michaels. Cybersecurity leaders must work cross-functionally to manage agentic AI adoption, identifying sanctioned and unsanctioned AI agents, enforcing data access controls, and developing incident response playbooks.

The spending data backs this up. Gartner’s 4Q25 forecast projects the AI-amplified security market reaching $160 billion by 2029, up from $49 billion in 2025. Gartner is clear that this isn’t additive spending. It represents the portion of existing security products that now embed AI capabilities. But the expectation tells the story: over 75% of enterprises will use AI-amplified cybersecurity products by 2028, up from less than 25% in 2025. Vendors that don’t embed AI will lose shelf space. (For more on AI security platforms, see Gartner’s Top Strategic Technology Trends for 2026, which predicts that over 50% of enterprises will use AI security platforms to protect their AI investments by 2028.)

Trend 2: Global regulatory volatility drives cyber resilience efforts

Regulators are getting personal. Boards and executives now face direct liability for compliance failures. Not just organizational fines, but individual accountability. The penalties for inaction have moved from theoretical to career-ending. Across multiple jurisdictions simultaneously.

Gartner advises cybersecurity leaders to formalize collaboration across legal, business, and procurement teams to establish clear accountability for cyber risk. Align control frameworks to recognized standards. Address data sovereignty concerns before they become enforcement actions. The organizations doing this well are treating regulatory preparedness as a core security function, not an annual compliance checkbox.

This is where the spending data gets interesting. Gartner’s forecast shows security consulting services growing from $24.2 billion (2024) to $36.6 billion (2029), adding $12.4 billion in five years. Security professional services follow a similar trajectory: $27.3 billion to $40.8 billion, adding $13.5 billion. Organizations are buying outside expertise because they can’t build regulatory competence fast enough in-house. I’ve been covering these numbers for three quarters, and the services growth is the part of the forecast that keeps surprising me.

Infrastructure protection adds $26.4 billion between 2024 and 2029, the largest absolute growth of any subsegment. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)

Trend 3: Post-quantum computing moves into action plans

Gartner predicts advances in quantum computing will render the asymmetric cryptography that organizations rely on unsafe by 2030. Four years. That’s the window to adopt post-quantum cryptography alternatives before “harvest now, decrypt later” attacks start cashing in on data that adversaries are collecting today.

Organizations need to identify their cryptographic deployments, assess data sensitivity and lifespan, and prioritize cryptographic agility. That last phrase keeps coming up in my conversations with CISOs. The ability to swap encryption methods without re-architecting entire systems. Swapping an algorithm is one thing. Doing it across a production environment without downtime is an entirely different problem.

“Post-quantum cryptography is reshaping cybersecurity strategies by prompting organizations to identify, manage, and replace traditional encryption methods, while prioritizing cryptographic agility,” said Michaels. “By investing in these capabilities and prioritizing migration now, assets will be secured when quantum threats become a reality.“

The encryption market in Gartner’s 4Q25 forecast grows from $1.04 billion in 2023 to $2.04 billion by 2029 at an 11.95% CAGR. A 2.0x increase. For what has historically been one of the slower-growing security subsegments, that’s a significant acceleration. Quantum urgency is changing the math.

Trend 4: Identity and access management adapts to AI agents

AI agents are breaking traditional IAM models. Plain and simple. Identity registration and governance, credential automation, and policy-driven authorization weren’t designed for autonomous machine actors that can initiate actions, access data, and interact with systems without human intervention. The scale problem compounds fast: when every employee can deploy dozens of AI agents, the identity surface area explodes.

Gartner recommends a targeted, risk-based approach. Invest where gaps and risks are greatest. Leverage automation where possible. The practical starting point is understanding which AI agents carry the most privilege and the least oversight. Those are your highest-risk identities right now, and most organizations haven’t inventoried them.

The identity market is already significant. Gartner’s 4Q25 forecast shows identity access management growing from $18.7 billion (2024) to $29.0 billion (2029), adding $10.3 billion in five years. That’s before the full scale of agentic AI identity requirements hits the market. IAM vendors that solve machine-actor identity at scale will capture a disproportionate share of that $10.3 billion growth.

Trend 5: AI-driven SOC solutions destabilize operational norms

AI-enabled security operations centers are enhancing alert triage and investigation workflows. The technology works. But deploying AI into a SOC doesn’t automatically reduce headcount needs. It changes the skill mix. Analysts who excelled at manual triage need different capabilities to oversee AI-driven workflows. Organizations are discovering this the hard way. That’s an organizational transformation challenge, and throwing more technology at it doesn’t help.

“To realize the full potential of AI in security operations, cybersecurity leaders must prioritize people as much as technology,” said Michaels. “Strengthening workforce capabilities, implementing human-in-the-loop frameworks into AI-supported processes and aligning adoption with clear strategic objectives will be critical to maintaining resilience as SOCs evolve.”

The talent dimension makes this harder than it already sounds. ISC2’s 2024 Cybersecurity Workforce Study, published in October 2024, documented a global workforce gap of 4.8 million professionals, a 19% year-over-year increase. The active workforce flatlined at 5.5 million (up just 0.1%). The numbers are brutal: 25% of organizations reported cybersecurity layoffs in 2024. 37% faced budget cuts. 90% report skills shortages. 58% believe the shortage puts their organization at significant risk. On the spending side, managed security services are growing at 11.1% in 2026, the fastest rate in the services segment. Organizations can’t hire fast enough, so they’re buying managed SOC capacity instead.

Trend 6: GenAI breaks traditional cybersecurity awareness tactics

Existing security awareness programs are failing. Full stop. A Gartner survey of 175 employees conducted between May and November 2025 found that 57% use personal GenAI accounts for work purposes, while 33% admit to uploading sensitive information to tools their organizations haven’t sanctioned. Those numbers should alarm every CISO reading this. A third of your workforce is actively feeding proprietary data into tools you can’t audit.

Gartner recommends shifting from general awareness training to adaptive behavioral programs that include AI-specific tasks. Generic compliance videos won’t cut it here. The organizations getting this right are making approved GenAI tools easy to access and unsanctioned tools hard to justify. Trying to ban GenAI outright just drives usage underground and costs you talent.

Strengthening governance, embedding secure practices, and establishing clear policies for authorized GenAI use will reduce exposure to privacy breaches and intellectual property loss. The governance gap on GenAI usage is, in my view, the most underestimated risk on this entire list. Every other trend has a spending line item attached to it. This one requires behavioral change, which is harder to buy.

Total market trajectory: $173.5 billion to $323.5 billion

Gartner’s year-by-year spending trajectory shows the acceleration curve these six trends are riding:

Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183, December 18, 2025). Current U.S. dollars.

 

CSPM and CASB lead all security categories with 31% and 26% CAGR through 2029. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)

What this means for CISOs

Three of the six trends (agentic AI oversight, IAM for machine actors, and GenAI awareness) are fundamentally about the same problem: autonomous AI systems operating inside enterprise environments without adequate governance. The other three (regulatory volatility, post-quantum readiness, and AI-driven SOCs) are the structural forces those governance failures will collide with. That convergence is the signal about where 2026 budgets need to go.

The organizations that will navigate this environment successfully are doing three things simultaneously:

Mapping their AI agent footprint now. If you don’t know how many AI agents are operating across your environment, sanctioned and unsanctioned, you can’t govern what you can’t see. Gartner’s 75% AI-amplified product adoption projection by 2028 means this window for establishing control is narrow.

Building cryptographic agility into their architecture. The 2030 quantum deadline means migration planning starts in 2026, not 2028. The encryption market’s 2.0x growth reflects early movers. Late movers face rip-and-replace costs that compound every quarter they wait.

Investing in people alongside AI tooling. AI-enabled SOCs work when human operators have the skills to oversee them. The ISC2 data is unambiguous: a 4.8 million professional gap growing at 19% year-over-year. Managed security services growth at 11.1% tells you where CISOs are finding capacity.

Gartner’s numbers aren’t projections anymore. They’re procurement trends already hitting finance systems. The $244.2 billion flowing into information security this year will fund agentic AI governance, quantum migration, and SOC transformation, whether your organization participates or not.

Bottom line: CISOs planning for 2027 are watching their competitors buy the tools they’ll be scrambling for in 18 months. The data says move now.

Nov 26

15 fastest-growing security categories in Gartner’s 3Q25 Information Security Forecast

15 fastest-growing security categories in Gartner's 3Q25 Information Security Forecast

Cloud Security Posture Management is growing at a 31.23% CAGR. Zero Trust Network Access at 23.25%. Threat Intelligence at 22.17%. The overall security market? Just 10.55%. Fifteen categories are outpacing the market by two to three times, collectively capturing $106 billion in new spending by 2029. Enterprise security budgets aren’t just expanding. They’re being redirected.

And the driver? Brutally simple.

Gartner estimates 99% of cloud security failures through 2025 will be the customer’s fault, primarily due to misconfigurations. Organizations are responding by investing aggressively in technologies that automate what humans simply can’t manage manually across hundreds of cloud accounts, thousands of APIs, and millions of potential attack vectors.

What these growth rates say about Gartner’s view of the market 

These fifteen categories represent $106.4 billion in new spending by 2029, growing from today’s baseline. What do they have in common? Three characteristics that explain why enterprises are pouring money into them:

  • Automation at Scale. Every high-growth category automates processes that break when done manually, whether it’s scanning cloud configurations, managing consent across jurisdictions, or detecting behavioral anomalies in network traffic. There’s no other way to keep pace.
  • Proactive vs. Reactive. These technologies prevent problems rather than clean up after them. CSPM catches misconfigurations before breaches. ZTNA eliminates the attack surface that VPNs create. Tokenization protects data even if systems are compromised. Security teams are finally getting ahead of the threat curve instead of playing catch-up.
  • Measurable ROI. IBM’s 2025 Cost of a Data Breach Report shows organizations using AI and automation extensively save $1.9 million per breach and reduce breach lifecycle by 80 days. With U.S. breach costs hitting $10.22 million, these investments pay for themselves with a single prevented incident.

15 fastest-growing security categories in Gartner's 3Q25 Information Security Forecast

The 15 categories reshaping security architecture

1. Cloud Security Posture Management (CSPM) | 31.23% CAGR | $2.5B → $13.0B

CSPM tools continuously scan infrastructure across AWS, Azure, and Google Cloud. With 82% of misconfigurations caused by human error and organizations managing 100+ cloud accounts, CSPM automates what’s mathematically impossible to do manually. The market will reach $15.6 billion by 2032.

2. Cloud Access Security Brokers (CASB) | 25.82% CAGR | $1.5B → $5.8B

Here’s a reality check. Enterprises average 112 SaaS applications, but shadow IT, or unauthorized apps, accounts for 42% of all applications. IT remains unaware of one-third of the apps on its networks. The damage? 65% of shadow IT companies suffer data loss, and 52% experience breaches. CASBs transform this chaos into visibility and control.

3. Zero Trust Network Access (ZTNA) | 23.25% CAGR | $1.6B → $5.6B

ZTNA kills the VPN model. Instead of network access, it provides application-specific connections verified for every request. Gartner predicts 70% of new remote access deployments will use ZTNA by 2025. With 65% of companies planning to replace VPNs, this shift represents a wholesale rethinking of secure access. The perimeter-based model is dying. Good riddance.

4. Cloud Workload Protection Platforms (CWPP) | 22.78% CAGR | $3.9B → $13.5B

CWPP platforms secure everything from traditional VMs to containers that exist for milliseconds. Legacy endpoint security can’t protect ephemeral containers or serverless functions—it wasn’t designed for workloads that appear and disappear in seconds. The shift to microservices demands purpose-built security.

5. Consent and Preference Management | 22.39% CAGR | $0.5B → $1.7B

GDPR fines reached €5.88 billion by January 2025, according to the DLA Piper GDPR Fines and Data Breach Survey. California’s CCPA penalties continue climbing; the California Privacy Protection Agency fined Todd Snyder $345,178 for inadequate opt-out and privacy request processes. Manual handling can’t meet regulatory deadlines. Automation prevents massive fines.

6. Threat Intelligence | 22.17% CAGR | $1.8B → $5.8B

IBM data shows threat intelligence reduces detection and escalation costs by $1.63 million while cutting incidents by 30%. Modern platforms aggregate data about bad actors and vulnerabilities, transforming raw threat data into automated responses across security stacks. The days of threat feeds sitting in dashboards, unused, are over.

7. Subject Rights Request Automation | 16.53% CAGR | $0.8B → $2.1B

When users demand “delete my data,” these platforms automate the process across all systems. Manual handling doesn’t scale, not when you’re managing requests across multiple jurisdictions with different requirements and tight deadlines.

8. Tokenization | 14.26% CAGR | $1.0B → $2.2B

Tokenization replaces sensitive data with meaningless tokens that can’t be mathematically reversed. Why the urgency now? NIST standardized quantum-resistant algorithms, including ML-KEM (formerly CRYSTALS-Kyber), in August 2024. Organizations are preparing for quantum threats expected within five to ten years.

9. Network Detection and Response (NDR) | 14.05% CAGR | $1.6B → $3.5B

NDR platforms use AI to establish behavioral baselines and detect anomalies signaling compromise. Here’s the mindset shift: rather than hoping to prevent all attacks, innovative organizations invest in rapid detection that minimizes damage when sophisticated attackers inevitably get through. Prevention isn’t enough anymore.

10. Vulnerability Assessment | 13.98% CAGR | $2.6B → $5.7B

Cloud infrastructure changes constantly. Quarterly scans are obsolete before they finish. Modern platforms provide continuous scanning in CI/CD pipelines, prioritizing based on real-world exploit data. DevOps teams deploying daily need vulnerability detection that keeps pace. Anything less is theater.

11. Endpoint Protection Platform (EPP) | 13.61% CAGR | $13.5B → $29.1B

The largest category doubles to $29.1 billion as ransomware attacks surge. According to Cyble analysis cited by TechTarget, U.S. ransomware attacks increased by 149% year-over-year in the first five weeks of 2025. Manufacturing led targets with 638 attacks in 2023, per Statista data compiled by Fortinet. Next-gen EPP uses behavioral analytics to stop ransomware before encryption begins—catching what traditional antivirus misses.

12. Secure Web Gateway (SWG) | 13.26% CAGR | $3.3B → $7.0B

Malicious sites appear and disappear in hours. Cloud-delivered SWGs update threat intelligence in real-time, protecting remote workers wherever they connect. Integration with ZTNA creates comprehensive security that follows users across devices and locations. The old perimeter? It no longer exists.

13. Web Application Firewalls (WAF) | 11.93% CAGR | $2.0B → $3.8B

Organizations expose hundreds of APIs, each a potential attack vector. Traditional network firewalls can’t inspect application-layer attacks. Modern WAFs use machine learning to distinguish legitimate users from attackers without blocking customers. Getting that balance right is harder than it sounds.

14. Encryption | 11.90% CAGR | $1.0B → $2.0B

NIST’s standardization of quantum-resistant algorithms signals urgency. Attackers already practice “harvest now, decrypt later”—collecting encrypted data for future quantum decryption. Organizations must transition to post-quantum cryptography now, as full integration takes years. This isn’t theoretical risk anymore.

15. Security Information and Event Management (SIEM) | 11.74% CAGR | $5.8B → $11.3B

AI transforms SIEM from reactive to proactive. Organizations using AI-powered automation save $1.9 million per breach, according to IBM’s newsroom. Machine learning models identify attack patterns and detect zero-day threats before signatures exist, turning security operations into a competitive advantage.

The Investment Thesis behind the numbers

These growth rates reflect three converging realities:

  • Cloud Complexity Is Exponential. With 79% of organizations using multiple cloud providers and managing hundreds of accounts, manual security is mathematically impossible. The 31.23% CAGR for CSPM isn’t optimism, it’s survival.
  • AI Changes Everything. Shadow AI breaches cost $4.63 million, $670,000 more than standard incidents. But AI also powers the defense, with automated security tools reducing breach lifecycles by 80 days. The same technology that creates vulnerabilities offers the best defense.
  • Compliance Costs Are Skyrocketing. Between GDPR, CCPA, and emerging regulations, manual compliance is a liability that grows daily. Automation platforms turn regulatory requirements into competitive advantages.

The Bottom Line

The organizations winning this race aren’t those with the most significant security budgets; they’re those investing in the right categories at the right time. These fifteen segments aren’t just growing fast; they’re defining what modern security architecture looks like.

The message from Gartner’s data is unambiguous: security spending is shifting from reactive to proactive, from manual to automated, from perimeter-based to zero-trust. Organizations still relying on legacy approaches aren’t just falling behind; they’re accepting risks that the market has already priced as unacceptable.

Source: Gartner Information Security Forecast 3Q25 Update (Document G00839334), showing overall market growth from $215.8B (2025) to $322.2B (2029) at 10.55% CAGR

Nov 3

Top 10 insights from Forrester’s 2026 Cybersecurity Budget Report

Top 10 Insights from Forrester’s 2026 Cybersecurity Budget Report

“With volatility now the norm, security and risk leaders need practical guidance on managing existing spending and new budgetary necessities,” states Forrester’s 2026 Budget Planning Guide.

The research firm’s planning guide for next year provides security leaders with new insights into how their clients are allocating budgets, which gives a helpful overview of the next 12 months of cybersecurity spending.

Implicit in the guide is the need for new technologies that enable organizations to be more adaptive to threats and take action on them before they become breaches. There’s also a strong focus on getting a head start on new technologies, anticipating the severity of threats new developments in AI, generative AI (genAI), deepfakes, and all other forms of weaponized technologies can pose to an organization.

Software is a solid 40% of cybersecurity spending, exceeding hardware at 15.8%, outsourcing at 15% and surpassing personnel costs at 29% by 11 percentage points. Meanwhile, security leaders face escalating threats, with generative AI attacks executing in milliseconds, a stark contrast to the average Mean Time to Identify (MTTI) of 181 days, according to IBM’s latest Cost of a Data Breach Report.

A fast-changing threatscape is changing spending priorities

Three converging threats are flipping cybersecurity on its head. What once protected organizations is now working against them. Generative AI (gen AI) is enabling attackers to craft 10,000 personalized phishing emails per minute using scraped LinkedIn profiles and corporate communications. NIST’s 2030 quantum deadline threatens retroactive decryption of $425 billion in currently protected data. Deepfake fraud that surged 3,000% in 2024 now bypasses biometric authentication in 97% of attempts, forcing security leaders to reimagine defensive architectures fundamentally.

Top ten insights from Forrester’s 2026 cybersecurity budget benchmarks

1.     Software now claims 40% of cybersecurity budgets, surpassing personnel spend. Forrester’s budget planning guide reports that software now accounts for approximately 40.2% of cybersecurity spending, eclipsing combined hardware and outsourcing budgets. It’s noteworthy that software spending is surpassing personnel costs by 11 percentage points.

Top 10 insights from Forrester’s 2026 Cybersecurity Budget Report
Source: Forrester Budget Planning Guide 2026: Security and Risk

2. Security budgets are accelerating, with 55% of global security and tech leaders forecasting significant increases next year. A robust 15% anticipate their budgets jumping more than 10%, and another 40% project hikes between 5% and 10%. Regional outlooks vary sharply: APAC is most bullish, with 22% expecting double-digit growth, compared to a cautious 9% in North America and just 12% in EMEA. However, nearly half (45%) remain reserved; 30% predict minimal budget bumps of 1%–4% or barely keeping pace with inflation, while another 10% expectSource: Forrester Budget Planning Guide 2026: Security and Risk no change, and 5% foresee cuts.

Top 10 insights from Forrester’s 2026 Cybersecurity Budget Report
Source: Forrester Budget Planning Guide 2026: Security and Risk

3. Cloud security, on-prem tech, and security awareness training are set to lead cybersecurity spending in 2026. Decision-makers are doubling down on cloud security, with 12% boosting budgets in this area by 10% or more, 11% doing the same for new on-premises solutions, and another 10% ramping up security awareness programs. Notably, investments in on-premises security technology appear twice among the top priorities, as 36% plan at least a 5% increase for both new deployments and upgrades to existing infrastructure. The numbers reflect an uneven global adoption of cloud strategies, driven by persistent concerns around cost, security, and data sovereignty. APAC is exceptionally bullish. 78% of companies there plan increased spending on new on-prem security, outpacing EMEA by 10% and North America by 8%.

Top 10 insights from Forrester’s 2026 Cybersecurity Budget Report
Source: Forrester Budget Planning Guide 2026: Security and Risk

4. Forrester recommends that security leaders broaden AI and ML security throughout the enterprise in 2026 as generative AI moves from standalone apps to essential business systems. Productivity suites, CRM platforms, and service tools now embed genAI natively, transforming workflows and widening potential attack surfaces. Enterprises urgently need comprehensive protection across AI models, data, applications, and user identities to counter risks such as model vulnerabilities, data leakage, and prompt jailbreaking. Hyperscalers like Google Cloud and Microsoft are responding quickly, while cybersecurity incumbents, notably Palo Alto Networks with its Protect AI acquisition, actively expand their footprint. Meanwhile, innovative startups, including Knostic and CalypsoAI, both featured at RSA’s Innovation Sandbox, target niche but critical genAI security gaps. Enterprises investing strategically now will securely scale genAI deployments and establish a clear competitive advantage.

5. Standalone SSE spending will sharply decline in 2026 as enterprises shift to unified SASE platforms, streamlining security operations and accelerating Zero Trust initiatives. Initially positioned to fill security gaps left by SD-WAN deployments and the surge in remote work, standalone SSE and isolated ZTNA solutions have now reached their functional limits. Leading companies increasingly adopt integrated platforms like Cato Networks’ cloud-native SASE, which consolidates SD-WAN, ZTNA, SWG, CASB, and firewall capabilities within a single, unified framework. As I’ve noted in VentureBeat, CISOs who pivot to unified SASE platforms benefit from simpler integration, superior AI-driven threat detection, and significant operational efficiencies that isolated solutions cannot deliver. Organizations proactively embracing integrated SASE from providers like Cato Networks will immediately enhance security resilience, improve operational agility, and significantly reduce vendor complexity.

6. Forrester predicts that by 2026, security leaders will seize a critical advantage by accelerating the adoption of post-quantum cryptography (PQC). With NIST’s landmark release of three core PQC standards in August 2024, organizations now have clear guidance to protect their data and applications against emerging quantum threats. Most governments align with NIST timelines, targeting legacy encryption deprecation by 2030, while Australia’s ASD urges adoption of approved PQC algorithms even sooner. Enterprises should immediately focus efforts on securing their most sensitive asymmetric cryptography, covering data at rest, data in transit, and data actively used within applications. Comprehensive cryptographic discovery and inventory tools provide the visibility required to assess readiness. Strategic partnerships with cryptoagility innovators, including Entrust, IBM, Keyfactor, Palo Alto Networks, QuSecure, SandboxAQ, and Thales, enable organizations to define a clear, secure migration path. Organizations acting decisively now will confidently navigate the quantum transition and fortify their competitive edge.

7. Machine identity management will become essential by 2026 as automated identities multiply rapidly across the IT infrastructure. Apps, AI agents, IoT devices, containers, cloud environments, and infrastructure scripts now generate identities faster than humans can manually track or manage. Enterprises urgently require solutions capable of managing these identities throughout their lifecycle, automating key rotations, and enforcing role-based access. Leading vendors, including Akeyless, BeyondTrust, CyberArk, Delinea, HashiCorp, Keyfactor, AppViewX, and emerging startups like Aembit, Astrix, Clutch, Entro, and Oasis Security, offer robust platforms to meet this challenge.

8. There will be a significant reallocation away from standalone interactive application security testing (IAST) in 2026, as operational hurdles continue to limit adoption. Originally designed to blend the runtime accuracy of dynamic application security testing (DAST) with static application security testing’s (SAST) code-level insights, standalone IAST has proven overly complex. Forrester recommends shifting budgets toward integrated IAST and DAST platforms, such as those from Invicti and HCLSoftware, that simplify deployment. Alternatively, APIs, microservices, and containers provide more transparent and consistent returns.

9. Consolidation of endpoint security and SIEM tools will accelerate in 2026. As extended detection and response (XDR) platforms gain momentum, security leaders have a clear opportunity to reduce agent sprawl, improve analyst efficiency, and lower the total cost of ownership. Vendors, including Microsoft, CrowdStrike, and Palo Alto Networks, now embed critical SIEM functions such as detection, correlation, third-party data ingestion (particularly from cloud, identity, and email), and response directly within their XDR offerings. While these integrated solutions currently don’t fully match standalone security analytics platforms, they deliver compelling advantages: simplified deployments, centralized threat context, and measurable operational savings. Organizations consolidating around unified XDR solutions today will streamline security operations and achieve faster, higher-quality threat detection.

10. By 2026, rapidly evolving generative AI will make deepfakes virtually indistinguishable from authentic media, rendering simplistic identity checks obsolete. Enterprises must proactively deploy sophisticated detection platforms using advanced ensemble modeling—spectral analysis, image artifacts, skin tone consistency, lighting anomalies, audio echo patterns, and device reputation, to ensure trusted employee verification and transaction authentication. Vendors such as GetReal Security, Sensity, and Reality Defender already offer real-time risk scoring, transparent reasoning, and integrated case management. Early adopters will safeguard identity security, sustain customer trust, and remain resilient against future deepfake threats.

Oct 29

Gartner: 60% of CISOs are piloting GenAI, but only 20% see results

Made with Imagen

The global threatscape is becoming dominated by all forms of weaponized LLMs, AI, and conversational agents, all aimed at launching lethal attacks that cripple companies and entire supply chains in minutes.

Nation‑state actors and organized eCrime groups now use artificial intelligence, including generative AI (GenAI), to automate reconnaissance, weaponize access, and strike faster than most defenses can respond. To keep pace, enterprises and the CISOs leading them are turning to GenAI as a defensive multiplier.

 CISOs are remaining optimistic

Gartner’s latest research quantifies that adoption is accelerating, but measurable results remain elusive. Approximately 60 % of organizations are piloting or planning GenAI cybersecurity initiatives. Only 20% of security leaders say these programs have delivered beneficial outcomes so far. These figures are from the research firm’s recent research note, What GenAI Use Cases Are Organizations Pursuing Within Cybersecurity? published earlier this month. Forrester predicts that the first agentic AI breach will happen in 2026.

Yet, despite early hurdles, cybersecurity leaders remain optimistic. Nearly every CISO I’ve spoken with sees GenAI as pivotal for transforming threat detection, proactive hunting, rapid incident response, and extracting actionable insights from terabytes of telemetry data streaming from endpoints and events. They recognize GenAI as crucial to decoding adversary tradecraft, particularly as identity-based threats and weaponized machine-learning attacks accelerate, reshaping the global threatscape in real time.

Key takeaways

  • Code Analysis leads the pack. GenAI‑assisted code analysis is the most mature use case: 22% of enterprises use it today, and another 30% are piloting it. It addresses a persistent gap, as 69% of software‑engineering leaders cite insecure code remediation as a critical skills bottleneck.
  • GenAI shows potential in helping SOC teams spot vulnerabilities faster. Currently, 21% of organizations actively leverage GenAI to enhance vulnerability detection and remediation, with another 26% piloting these capabilities. Adoption is driven by GenAI’s ability to automate vulnerability identification and prioritize remediation workflows, addressing longstanding security bottlenecks and resource constraints. Despite intense interest, widespread implementation remains challenged by integration complexity and skepticism about AI-generated accuracy, emphasizing the need for incremental deployment aligned with existing cybersecurity metrics.
  • CISOs Shift from Ambition to Execution Gartner finds that the leaders gaining traction are those adopting “bite‑sized” implementations or use cases that fit into current processes, deliver quantifiable ROI, and build trust among analysts and engineers.

CISOs are dealing with a threatscape moving at machine speed

Given how lethal machine-driven attacks are becoming, exacerbated by the growing sophistication of weaponized AI, going on the offensive with GenAI is a choice more CISOs are considering.

  • Nearly every cybersecurity team wants to have a Gen AI pilot either complete or in process to see how it integrates with their planned arsenal for 2026. Most CISOs want some form of AI in their arsenals going into the new year, as many expect the intensity, ingenuity, and lethal impact of automated attacks will reach new levels next year. One told me confidentially she fully expects machine-on-machine breach attempts to grow six times over in 2026 as her financial services firm handles highly speculative assets, including cryptocurrency ETFs and investment products.
  • Breakout speed hits critical mass. CrowdStrike’s 2025 Global Threat Report reveals the alarming acceleration of attacks: the fastest observed eCrime intrusion took just 51 seconds to escalate from initial access to lateral movement, virtually eliminating defenders’ window to respond.
  • Living-off-the-Land tactics dominate and often evade legacy cyberdefense systems: Malware-free intrusions surged significantly, now comprising 81% of interactive attacks in 2025. This trend is corroborated by findings from Mandiant and IBM X-Force, indicating adversaries are bypassing traditional signature-based controls by exploiting legitimate tools native to the enterprise environment.
  • Nation-state activity reaching new record levels as weaponized tradecraft gains stealth and sophistication: CrowdStrike, Mandiant have documented triple-digit increases in operations linked to China, Iran, and North Korea. These attacks predominantly target telecommunications and critical infrastructure, reflecting geopolitical tensions and nation-states’ strategic prioritization of cyber-espionage.
  • Global threat consensus is clear and compelling: ENISA’s Threat Landscape 2025 report aligns precisely with intelligence from CrowdStrike, Mandiant, and IBM X-Force, verifying that nation-state actors now leverage AI-driven automation to execute attacks faster than enterprises can detect, let alone defend.

CrowdStrike Founder and CEO George Kurtz underscored the urgency clearly in a recent CNBC interview on October 23rd, stating, “Well, this is something that we’ve really been focused on for the last number of years is being able to protect agentic AI. And if you think about agentic AI, it has the capabilities to interact with data. It has the capabilities to interact with Compute. It has identities, non-human identities, but it operates at superhuman speed. So all of the challenges that we’ve seen over the many years of humans getting themselves into trouble is only going to be exasperated by agentic AI, and we need security like CrowdStrike is delivering to protect it”.

Practical guidance from CISOs adding GenAI to their arsenals

Gartner’s latest research, combined with interviews and discussions with CISOs, security leaders, and SOC leaders who are piloting and in some cases using GenAI-based platforms today, offers this advice:

  • Go deep on integration on pilots to see how strong the GenAI solution is as a contributor to your security tech stack: CISOs and SOC leaders tell me that this is the most reliable test of whether a GenAI platform or app will make the cut and get to production on their tech stack. Solid APIs that have been battle-tested by vendors who have a strong API management history have the inside track.
  • Outcome-driven use cases are a must-have:At its core, cybersecurity is a business decision. And in a digital-first world, protecting your brand is essential. Any Gen AI pilot needs to contribute to a use case that makes a solid contribution to solidifying a business’s ability to compete.
  • Start with time-tested, established metrics: Getting to a level of trust in GenAI is core to seeing if it is ready to progress from pilot into production. Evaluating GenAI effectiveness using established KPIs, including mean time to detect (MTTD) and mean time to respond (MTTR), at table stakes. CISOs and others running pilots caution about creating entirely new metrics just for GenAI. It obfuscates the total business impact of the technology.
  • Parallel human trust and governance: Gartner emphasizes investing in employee enablement and robust governance frameworks like NIST’s AI Risk Management Framework to foster confidence in GenAI adoption. Human oversight remains a vital layer of control. Human-in-the-middle is essential for any workflow.

Bottom Line

Nation-state adversaries measure their innovation in how lethal their attacks are, how stealth their tradecraft is, and how easily they can evade legacy security techniques. It’s a full cyberwar just a few steps away from a full-on kinetic war. Research from CrowdStrike, IBM, Mandiant, and many other companies shows machine-to-machine attacks orchestrated with Gen AI are accelerating, so much so that Forrester predicts an imminent AI breach next year. GenAI’s ability to identify new threats and stop them makes the technology work a look.

Mar 23

What Enterprises Need To Plan For In 2021 When It Comes To Endpoint Security

What Enterprises Need to Plan for In 2021 When It Comes to Endpoint Security

Bottom Line: Today’s largely-distributed enterprises need to make sure they are putting endpoint security first in 2021– which includes closely managing every stage of the device lifecycle, from deployment to decommission, and ensuring all sensitive data remains protected.

There’s a looming paradox facing nearly every organization today of how they’ll secure thousands of remote endpoints without having physical access to devices, and without disrupting worker productivity. Whether there’s the need to retire hardware as part of down-sizing or cost-cutting measures, or the need to equip virtual teams with newer equipment more suitable for long term work-from-home scenarios, this is one of the most pressing issues facing CISOs and CIOs today.

Wanting to learn more about how their customers are tackling their endpoint security challenges and how their companies are helping to solve it, I sat down (virtually) with Absolute Software’s President and CEO Christy Wyatt and Matthew Zielinski, President of North America Intelligent Devices Group at Lenovo. The following is my interview with both of them:

Louis Columbus: Christy and Matt, thanks so much for your time today. To get started, I would like each of you to share what you’re hearing from your customers regarding their plans to refresh laptops and other endpoint devices in 2021.

Christy Wyatt: We’re seeing a strong desire from organizations to ensure that every individual is digitally enabled, and has access to a screen. In some cases, that means refreshing the hardware they already have in the field, and in other cases, that means buying or adding devices. From the endpoint security standpoint, there’s been a shift in focus around which tools matter the most. When laptops were primarily being used on campus, there was a certain set of solutions to monitor those devices and ensure they remained secure. Now that 90% of devices are out of the building, an entirely different set of capabilities is required – and delivering those has been our focus.

Matt Zielinski: We are seeing historic levels of demand from consumers, as many are transitioning from having maybe one or two devices per household to at least one device per person. We’re also seeing the same levels of demand on both the education and enterprise side. The new dynamic of work-from-anywhere, learn-from-anywhere, collaborate-from-anywhere underscores that the device hardware and software need to be current in order to support both the productivity and security needs of hugely distributed workforces. That’s our highest priority.

Louis:  Where are CISOs in their understanding, evaluation, and adoption of endpoint security technologies?

Christy: The journey has been different for the education market than for the enterprise market. Most enterprise organizations were already on the digital path, with some percentage of their population already working remotely. And because of this, they typically have a more complex security stack to manage; our data shows that the total number of unique applications and versions installed on enterprise devices is nearly 1.5 million. What they’ve seen is a trifecta of vulnerabilities: employees taking data home with them, accessing it on unsecured connections, and not being aware of how their devices are protected beyond the WiFi connection and the network traffic.

In the education space, the challenges – and the amount of complexity – are completely different; they’re managing just a small fraction of that total number of apps and versions. That said, as the pandemic unfolded, education was hit harder because they were not yet at a point where every individual was digitally connected. There was a lot of reliance on being on campus, or being in a classroom. So, schools had to tackle digital and mobile transformation at the same time – and to their credit, they made multiple years of progress in a matter of weeks or months. This rapid rate of change will have a profound effect on how schools approach technology deployments going forward.

Matt: Whether in enterprise or education, our customers are looking to protect three things: their assets, their data, and their users’ productivity. It’s a daunting mission. But, the simplest way to accomplish it is to recognize the main control point has changed. It’s no longer the server sitting behind the firewall of your company’s or school’s IT environment. The vulnerability of the endpoint is that the network is now in the user’s hands; the edge is now the primary attack surface. I think CISOs realize this, and they are asking the right questions… I just don’t know if everyone understands the magnitude or the scale of the challenge. Because the problem is so critical, though, people are taking the time to make the right decisions and identify all the various components needed to be successful.

Louis:   It seems like completing a laptop refresh during the conditions of a pandemic could be especially challenging, given how entire IT teams are remote. What do you anticipate will be the most challenging aspects of completing a hardware refresh this year (2021)?

Matt:  The PC has always been a critical device for productivity. But now, without access to that technology, you are completely paralyzed; you can’t collaborate, you can’t engage, you can’t connect. Lenovo has always been focused on pushing intelligent transformation as far as possible to get the best devices into the hands of our customers. Beyond designing and building the device, we have the ability to distribute asset tags and to provide a 24/7 help desk for our customers whether you’re a consumer, a school, or a large institution. We can also decommission those devices at the end, so we’re able to support the entire journey or lifecycle.

The question has really become, how do you deliver secure devices to the masses? And, we’re fully equipped to do that. For example, every Lenovo X1 Carbon laptop comes out of the box with Lenovo Security Assurance, which is actually powered by Absolute; it is in our hardware. Our customers can open a Lenovo PC, and know that it is completely secure, right out of the box. Every one of our laptops is fortified with Absolute’s Persistence technology and self-healing capabilities that live in the BIOS. It’s that unbreakable, secure connection that makes it possible for us to serve our customers throughout the entire lifecycle of device ownership.

Louis: Why are the legacy approaches to decommissioning assets falling short / failing today? How would you redesign IT asset-decommissioning approaches to make them more automated, less dependent on centralized IT teams?

Christy: There have been a few very visible cases over the past year of highly regulated organizations, experiencing vulnerabilities because of how they decommissioned – or did not properly decommission – their assets. But, I don’t want anyone to believe that that this is a problem that is unique to regulated industries, like financial services. The move to the cloud has given many organizations a false sense of security, and it seems that the more data running in the cloud, the more pronounced this false sense of security becomes. It’s a mistaken assumption to think that when hardware goes missing, the security problem is solved by shutting down password access and that all the data is protected because it is stored in the cloud. That’s just not true. When devices aren’t calling in anymore, it’s a major vulnerability – and the longer the device sits without being properly wiped or decommissioned, the greater the opportunity for bad actors to take advantage of those assets.

The other piece that should be top of mind is that once a device is decommissioned, it’s often sold. We want to ensure that nothing on that device gets passed on to the next owner, especially if it’s going to a service or leasing program. So, we’ve concentrated on making asset decommissioning as precise as possible and something that can be done at scale, anytime and anywhere.

Matt:  Historically, reclaiming and decommissioning devices has required physical interaction. The pandemic has limited face-to-face encounters, so , we’re leveraging many different software solutions to give our customers the ability to wipe the device clean if they aren’t able to get the asset back in their possession, so that at least they know it is secure. Since we’re all now distributed, we’re looking at several different solutions that will help with decommissioning, several of which are promising and scale well given today’s constraints. Our goal is to provide our enterprise customers with decommissioning flexibility, from ten units to several thousand.

Louis:  Paradoxically, having everyone remote has made the business case for improving endpoint security more compelling too. What do you hear from enterprises about accelerating digital transformation initiatives that include the latest-generation endpoint devices?

Christy:  The same acceleration that I spoke about on the education side, we absolutely see on the enterprise side as well, and with rapid transformation comes increased complexity. There has been a lot of conversation about moving to Zero Trust, moving more services to the cloud and putting more controls on the endpoint – and not having these sort of layers in between. Our data tells us that the average enterprise device today has 96 unique applications, and at least 10 of them are security applications. That is a massive amount of complexity to manage. So, we don’t believe that adding more controls to the endpoint is the answer; we believe that what’s most important is knowing the security controls you have are actually working. And we need to help devices and applications become more intelligent, self-aware, and capable of fixing themselves. This concept of resiliency is the cornerstone of effective endpoint security, and a critical part of the shift to a more modern security architecture.

Matt: I think there are two major forcing functions: connection and security. Because we are all now remote, there’s a huge desire to feel connected to one another even though we aren’t sitting in the same room together. We’re modifying our products in real-time with the goal of removing shared pain points and optimizing for the new reality in which we’re all living and working. Things like microphone noise suppression and multiple far field microphones, so that if the dog barks or kids run into a room, the system will mute before you’ve even pressed the mute button. We’re improving camera technology from a processing standpoint to make things look better. Ultimately, our goal is to provide an immersive and connected experience.

Security, however, transcends specific features that deliver customer experiences – security is the experience. The features that make hardware more secure are those that lie beneath the operating system, in the firmware. That is why we have such a deep network of partners, including Absolute. Because you need to have a full ecosystem, and a program that takes advantage of all the best capabilities, in order to deliver the best security solution possible.

Louis: How is Absolute helping enterprise customers ensure greater endpoint security and resiliency in 2021 and beyond?

Christy: We spend a lot of time sitting with customers to understand their needs and how and where we can extend our endpoint security solutions to fit. We believe in taking a layered approach – which is the framework for defense in-depth, and an effective endpoint security strategy. The foundational piece, which we are able to deliver, is a permanent digital tether to every device; this is the lifeline. Not having an undeletable connection to every endpoint means you have a very large security gap, which must be closed fast. A layered, persistence-driven approach ensures our customers know their security controls are actually working and delivering business value. It enables our customers to pinpoint where a vulnerability is and take quick action to mitigate it.

Lenovo’s unique, high value-add approach to integrated security has both helped drive innovation at Absolute, while also providing Lenovo customers the strongest endpoint security possible. Their multilayer approach to their endpoint strategy capitalizes on Absolute’s many BIOS-level strengths to help their customers secure every endpoint they have. As our companies work together, we are both benefitting from a collaboration that seeks to strengthen and enrich all layers of endpoint security. Best of all, our shared customers are the benefactors of this collaboration and the results we are driving at the forefront of endpoint security.

Louis:  How has the heightened focus on enterprise cybersecurity in general, and endpoint security specifically, influenced Lenovo’s product strategy in 2021 and beyond?

Matt:  We have always been focused on our unique cybersecurity strengths from the device side and making sure we have all of the control points in manufacturing to ensure we build a secure platform. So, we’ve had to be open-minded about endpoint security, and diligent in envisioning how potential vulnerabilities and attack strategies can be thwarted before they impact our customers. Because of this mindset, we’re fortunate to have a very active partner community. We’re always scouring the earth for the next hot cybersecurity technology and potential partner with unique capabilities and the ability to scale with our model. This is a key reason we’ve standardized on Absolute for endpoint security, as it can accommodate a wide breadth of deployment scenarios. It’s a constant and very iterative process with a team of very smart people constantly looking at how we can excel at cybersecurity. It is this strategy that is driving us to fortify our Lenovo Security Assurance architecture over the long-term, while also seeking new ways of providing insights from existing and potentially new security applications.

Louis: What advice are you giving CISOs to strengthen endpoint security in 2021 and beyond?

Christy: One of our advisors is the former Global Head of Information Security at Citi Group, and former CISO of JP Morgan and Deutsche Bank. He talks a lot about his shared experiences of enabling business operations, while defending organizations from ever-evolving threats, and the question that more IT and security leaders need to be asking – which is, “Is it working?” Included in his expert opinion is that cybersecurity needs to be integral to business strategy – and endpoint security is essential for creating a broader secure ecosystem that can adapt as a company’s needs change.

I believe there needs to be more boardroom-level conversations around how compliance frameworks can be best used to achieve a balance between cybersecurity and business operations. A big part of that is identifying resiliency as a critical KPI for measuring the strength of endpoint controls.

 

Aug 23

5 Ways Machine Learning Can Thwart Phishing Attacks

5 Ways Machine Learning Can Thwart Phishing Attacks

Mobile devices are popular with hackers because they’re designed for quick responses based on minimal contextual information. Verizon’s 2020 Data Breach Investigations Report (DBIR) found that hackers are succeeding with integrated email, SMS and link-based attacks across social media aimed at stealing passwords and privileged access credentials. And with a growing number of breaches originating on mobile devices according to Verizon’s Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle’s Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs’ priority list today.

How Machine Learning Is Helping To Thwart Phishing Attacks

Google’s use of machine learning to thwart the skyrocketing number of phishing attacks occurring during the Covid-19 pandemic provides insights into the scale of these threats. On a typical day, G-Mail blocks 100 million phishing emails. During a typical week in April of this year, Google’s G-Mail Security team saw 18M daily malware and phishing emails related to Covid-19. Google’s machine learning models are evolving to understand and filter phishing threats, successfully blocking more than 99.9% of spam, phishing and malware from reaching G-Mail users. Microsoft thwarts billions of phishing attempts a year on Office365 alone by relying on heuristics, detonation and machine learning strengthened by Microsoft Threat Protection Services.

42% of the U.S. labor force is now working from home, according to a recent study by the Stanford Institute for Economic Policy Research (SIEPR). The majority of those working from home are in professional, technical and managerial roles who rely on multiple mobile devices to get their work done. The proliferating number of threat surfaces all businesses have to contend with today is the perfect use case for thwarting phishing attempts at scale.

What’s needed is a machine learning engine capable of analyzing and interpreting system data in real-time to identify malicious behavior. Using supervised machine learning algorithms that factor in device detection, location, user behavior patterns and more to anticipate and thwart phishing attacks is what’s needed today. It’s a given that any machine learning engine and its supporting platform needs to be cloud-based, capable of scaling to analyze millions of data points. Building the cloud platform on high-performing computing clusters is a must-have, as is the ability to iterative machine learning models on the fly, in milliseconds, to keep learning new patterns of potential phishing breaches. The resulting architecture would be able to learn over time and reside on the device recursively. Protecting every endpoint if it’s connected to WiFi or a network or not is a key design goal that needs to be accomplished as well. MobileIron recently launched one of the most forward-thinking approaches to solving this challenge and its architecture is shown below:

5 Ways Machine Learning Can Thwart Phishing Attacks

Five Ways Machine Learning Can Thwart Phishing Attacks 

The one point of failure machine learning-based anti-phishing apps continue to have is lack of adoption. CIOs and CISOs I’ve spoken with know there is a gap between endpoints secured and the total endpoint population. No one knows for sure how big that gap is because new mobile endpoints get added daily. The best solution to closing the gap is by enabling on-device machine learning protection. The following are five ways machine learning can thwart phishing attacks using an on-device approach:

1.    Have machine learning algorithms resident on every mobile device to detect threats in real-time even when a device is offline.  Creating mobile apps that include supervised machine learning algorithms that can assess a potential phishing risk in less than a second is what’s needed. Angular, Python, Java, native JavaScript and C++ are efficient programming languages to provide detection and remediation, so ongoing visibility into any malicious threat across all Android and iOS mobile devices can be tracked, providing detailed analyses of phishing patterns. The following is an example of how this could be accomplished:

5 Ways Machine Learning Can Thwart Phishing Attacks

2.    Using machine learning to glean new insights out of the massive amount of data and organizations’ entire population of mobile devices creates a must-have.  There are machine learning-based systems capable of scanning across an enterprise of connected endpoints today. What’s needed is an enterprise-level approach to seeing all devices, even those disconnected from the network.

3.    Machine learning algorithms can help strengthen the security on every mobile device, making them suitable as employees’ IDs, alleviating the need for easily-hackable passwords. According to Verizon, stolen passwords cause 81% of data breaches and 86% of security leaders would do away with passwords, if they could, according to a recent IDG Research survey. Hardening endpoint security to the mobile device level needs to be part of any organizations’ Zero Trust Security initiative today. The good news is machine learning algorithms can thwart hacking attempts that get in the way making mobile devise employees’ IDs, streamlining system access to the resources they need to get work done while staying secure.

4.    Keeping enterprise-wide cybersecurity efforts focused takes more than after-the-fact analytics and metrics; what’s needed is look-ahead predictive modeling based machine learning data captured at the device endpoint.  The future of endpoint resiliency and cybersecurity needs to start at the device level. Capturing data at the device level in real-time and using it to train algorithms, combined with phishing URL lookup, and Zero Sign-On (ZSO) and a designed-in Zero Trust approach to security are essential for thwarting the increasingly sophisticated breach attempts happening today.

5.    Cybersecurity strategies and the CISOs leading them will increasingly be evaluated on how well they anticipate and excel at compliance and threat deterrence, making machine learning indispensable to accomplishing these tasks. CISOs and their teams say compliance is another area of unknowns they need greater predictive, quantified insights into. No one wants to do a compliance or security audit manually today as the lack of staff due to stay-at-home orders makes it nearly impossible and no one wants to jeopardize employee’s health to get it done.  CISOs and teams of security architects also need to put as many impediments in front of threat actors as possible to deter them, because the threat actor only has to be successful one time, while the CISO/security architect have to be correct 100% of the time. The answer is to combine real-time endpoint monitoring and machine learning to thwart threat actors while achieving greater compliance.

Conclusion

For machine learning to reach its full potential at blocking phishing attempts today and more advanced threats tomorrow, every device needs to have the ability to know if an email, text or SMS message, instant message, or social media post is a phishing attempt or not. Achieving this at the device level is possible today, as MobileIron’s recently announced cloud-based Mobile Threat Defense architecture illustrates. What’s needed is a further build-out of machine learning-based platforms that can adapt fast to new threats while protecting devices that are sporadically connected to a company’s network.

Machine learning has long been able to provide threat assessment scores as well. What’s needed today is greater insights into how risk scores relate to compliance. Also, there needs to be a greater focus on how machine learning, risk scores, IT infrastructure and the always-growing base of mobile devices can be audited. A key goal that needs to be achieved is having compliance actions and threat notifications performed on the device to shorten the “kill chain” and improve data loss prevention.

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