Skip to content

Mar 24

Four numbers that expose where cybersecurity spending is really going in 2026

Inside the $244.2 billion security market: agentic AI adoption outpaces defenses 8 to 1, cloud security grows at 28.8%, and enterprises spend 17x more on AI tools than on securing the AI itself

Gartner forecasts worldwide AI spending will reach $2.52 trillion in 2026, a 44% increase year-over-year. Worldwide IT spending will hit $6.15 trillion. Within that massive build-out, information security spending accelerates to $244.2 billion, up 13.3%.

The headline looks healthy. Look closer, and it isn’t. I’ve been tracking Gartner’s information security forecast through multiple quarterly updates, and the trajectory keeps steepening. But the spending acceleration is masking a deeper problem: enterprises are deploying AI agents into production far faster than they are securing them.

  1. The 40% / 6% gap

Gartner predicts 40% of enterprise applications will include task-specific AI agents by the end of 2026. Up from less than 5% in January. These are not chatbots. Gartner’s examples include autonomous cybersecurity response agents that scan network traffic, analyze system logs, and initiate responses without human intervention.

Only roughly 6% of organizations report having an advanced AI security strategy in place, according to vendor-sourced research from BigID’s 2025 AI Risk and Readiness study. Even adjusting for methodology differences between vendor and analyst research, the gap is stark. Agents are entering production at roughly 7-8x the rate organizations are building governance around them.

Gartner’s 4Q25 AI spending forecast created a dedicated agentic AI market segment for the first time. The spending lines are dramatic. Agentic AI overtakes chatbot and assistant spending by 2027. By 2029, agentic AI will reach $752.7 billion at a 119% compound annual growth rate. Chatbot spending peaks at $264.7 billion, then declines. That crossover point is where the security model breaks, because chatbots operate within human-supervised sessions. Agents don’t.

Gartner named agentic AI oversight the number-one cybersecurity trend for 2026 in its February report (my breakdown of all six trends here). A separate Gartner poll of 147 CIOs found 24% had already deployed AI agents and 50% were actively experimenting. Guardian agents, AI systems designed to monitor and govern other AI agents, are projected to capture 10-15% of the agentic AI market by 2030.

Forrester’s 2026 cybersecurity predictions go further: an agentic AI deployment will cause a publicly disclosed data breach this year, leading to employee dismissals. Senior analyst Paddy Harrington frames it as a cascade of failures, not a single point of error. That prediction landed in October 2025. Nothing since has made it less likely.

  1. $244.2 billion, and where it goes

Gartner’s 4Q25 information security forecast projects global spending reaching $244.2 billion in 2026, up 13.3% year-over-year. That is acceleration, not continuation. Gartner’s forecast trajectory has been steepening for multiple quarters. It follows a year where many CISOs focused on consolidating tools rather than buying new ones.

The allocation matters more than the total (please click on the graphic to expand for easier reading):

Cloud security at 28.8% growth is the fastest subsegment by a wide margin. CSPM alone carries a 31.3% CAGR. These represent organizations reacting to attack surfaces that expanded when workloads moved to the cloud faster than security controls followed.

Managed security services at 11.1% tells a workforce story the spending headline misses. The ISC2 documented a global cybersecurity workforce gap of 4.8 million professionals in October 2024. That gap grew 19% year-over-year while the active workforce flatlined at 5.5 million. A quarter of organizations reported cybersecurity layoffs. So they’re buying SOC capacity from managed providers instead. The spending growth in managed services is a staffing problem wearing a procurement mask.

The 17:1 spending asymmetry

Gartner’s 4Q25 AI spending forecast splits the AI cybersecurity market into two sub-segments for the first time. AI-amplified security, using AI to defend the enterprise, reached $49 billion in 2025. Securing AI itself, protecting the models, training data, inference pipelines, agent workflows, and decision outputs, stood at $2.8 billion. That is 5.5% of the AI cybersecurity market.

Enterprises are investing 17 times more in AI-powered security tools than in securing the AI on which those tools run. Gartner projects over 75% of enterprises will use AI-amplified cybersecurity products by 2028, up from less than 25% in 2025. The tools are getting funded. What the tools actually depend on to function is not.

  1. Quantum crosses the 5% budget threshold

Forrester predicts quantum security spending will exceed 5% of overall IT security budgets in 2026. Five percent sounds modest until you consider what it represents: the shift from research line items to actual procurement.

That means consulting engagements for quantum migration planning. Cryptographic discovery tools to figure out which systems need replacing first. Post-quantum algorithm testing across live production environments. Gartner calls post-quantum cryptography a force that demands organizations identify, manage, and replace traditional encryption methods now. Not eventually. The encryption market is growing at 2.0x according to the 4Q25 forecast, and the planning horizon is 2030. Starting migration in 2028 means compounding rip-and-replace costs every quarter of delay.

Forrester also predicts the EU will establish its own known exploited vulnerability database in 2026. Regulatory fragmentation adds cost. For enterprises operating across jurisdictions, quantum migration planning cannot be separated from compliance architecture.

  1. 57% of employees are already using shadow AI

A smaller Gartner survey of 175 employees conducted between May and November 2025 found that 57% use personal GenAI accounts for work. A third admitted to uploading sensitive information to tools their organizations have not sanctioned.

I keep coming back to this stat because it reframes the entire agentic AI security conversation. The firewalls most enterprises rely on were built for human-to-application communication. Protocols like MCP now enable agent-to-agent interaction at a scale and speed those tools were never designed to see. Machine identities outnumber human employees by more than 80 to 1 in most enterprises, according to CyberArk. Traditional IAM was not built for nonhuman actors operating autonomously.

Gartner’s cybersecurity trends report identifies IAM adaptation for AI agents as a top-six trend for 2026, specifically calling out identity registration, credential automation, and policy-driven authorization for machine actors. Failure to address these issues will lead to greater access-related cybersecurity incidents as autonomous agents become more prevalent.

The investment context: AI in the trough, security in the gap

Gartner places AI in the Trough of Disillusionment throughout 2026. AI will most often be sold by incumbent software providers rather than bought as part of new moonshot projects. ROI predictability has to improve before enterprises scale their deployments.

Forrester’s 2026 predictions reinforce this: enterprises will defer 25% of planned AI spending into 2027 as financial rigor slows production deployments and kills proofs of concept. Fewer than one-third of decision-makers can tie AI value to their organization’s financial growth.

Yet Gartner’s IT spending forecast shows server spending accelerating at 36.9% year-over-year and data center spending surging 31.7% past $650 billion. GenAI model spending grows at 80.8%. The infrastructure build-out is not slowing even as enterprise application adoption pauses.

Infrastructure spending runs hot. Application-layer AI spending cools. Security spending accelerates into the gap between adoption speed and governance readiness. The $244.2 billion flowing into information security is the cost of operating in an environment where AI agents are proliferating faster than the controls designed to govern them.

What these numbers add up to

For two decades, enterprise security assumed a human on the other end of every session, every credential request, every decision. That assumption is collapsing. The autonomous agent accessing your production database at 3 AM doesn’t authenticate the way your SOC analyst does, doesn’t respect the same governance boundaries, and operates at speeds no human reviewer can match.

What makes this moment different from previous security inflection points is the speed asymmetry. When cloud migration created new attack surfaces, enterprises had years to adapt. The shift from on-prem to cloud took a decade. The shift from human-operated to agent-operated environments is measured in quarters. Gartner didn’t even have a dedicated agentic AI spending segment until this forecast cycle. By the next one, the crossover will have already happened.

The practical question for 2026 is not whether to invest in AI security. That decision has been made by the spending trajectory. It is whether to govern AI agents proactively, before the first publicly disclosed agentic breach forces a reactive scramble, or to wait and pay the premium that every late mover in cybersecurity history has paid. Forrester has already predicted which outcome is more likely this year. The 17:1 ratio suggests most enterprises are betting on the wrong side of that question.

Sources

Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 (December 18, 2025)

Gartner Forecast Analysis: Information Security, Worldwide, 2026 (February 5, 2026)

Gartner Forecast: AI Spending, Worldwide, 2024–2029, 4Q25 (December 2025)

Gartner, Top Trends in Cybersecurity for 2026 (February 5, 2026)

Gartner, Worldwide AI Spending Will Total $2.52 Trillion in 2026 (January 15, 2026)

Gartner, Worldwide IT Spending to Grow 10.8% in 2026 (March 2026)

Gartner, 40% of Enterprise Apps Will Feature AI Agents by 2026 (August 26, 2025)

Gartner, Guardian Agents Will Capture 10-15% of Agentic AI Market by 2030 (June 11, 2025)

Forrester Predictions 2026: Cybersecurity and Risk (October 28, 2025)

Forrester, Global Tech Spend Will Grow 7.8% in 2026 (February 2, 2026)

Forrester, 2026 Technology & Security Predictions (October 28, 2025)

ISC2, 2024 Cybersecurity Workforce Study (October 2024)

CyberArk, Machine Identities Report (April 2025)

BigID, AI Risk & Readiness in the Enterprise (2025)

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

Jan 22

1 Comment

Gartner 4Q25: $4.71T AI market proves agentic AI and data readiness are the only race that matters

Only 43% of organizations say their data is ready for AI. Meanwhile, AI Data spending is compounding at 155% annually. That’s six times faster than the infrastructure buildouts grabbing headlines. That disconnect defines the enterprise AI landscape in 2025.

Gartner’s 4Q25 AI Spending Forecast (December 17, 2025) projects $4.71 trillion by 2029. But I’ve been digging through the segment data, and the story isn’t the topline number. Four subsegments within Gartner’s AI Data market are growing between 136% and 178% CAGR. AI Infrastructure? Just 29.25%. The money is following the bottlenecks.

“Nearly everything today, from the way we work to how we make decisions, is directly or indirectly influenced by AI,” says Carlie Idoine, VP Analyst at Gartner. “But it doesn’t deliver value on its own. AI needs to be tightly aligned with data, analytics, and governance to enable intelligent, adaptive decisions and actions across the organization.”

McKinsey’s 2025 State of AI survey (1,993 participants, 105 countries) found 88% of organizations now use AI in at least one business function. But two-thirds remain stuck in pilot mode. Just 6% qualify as “AI high performers,” meaning organizations where more than 5% of EBIT comes from AI. The gap between adoption and value creation is where the real spending story unfolds.

Where the bottlenecks are breaking

Every high-growth segment in the forecast eliminates a constraint that stalls production of AI.

Synthetic data generation addresses the labeled data shortage. You can’t train models without it, and real world data comes with privacy constraints, bias problems, and collection costs that don’t scale. Data governance enforces quality standards because ungoverned data produces ungoverned outputs. Hallucinations, compliance violations, and bias incidents trace directly back to data quality failures. Data integration software connects fragmented sources. Most enterprise data sits across dozens of systems that don’t communicate.

“With AI investment remaining strong this year, a sharper emphasis is being placed on using AI for operational scalability and real-time intelligence,” says Haritha Khandabattu, Senior Director Analyst at Gartner. “This has led to a gradual pivot from generative AI as a central focus toward the foundational enablers that support sustainable AI delivery, such as AI-ready data and AI agents.” Infrastructure enables these capabilities. Data readiness and agentic AI determine whether they generate returns.

The $14.6 billion data readiness bet

Gartner tracks AI Data as a unified market with four subsegments. The aggregate grows from $134.35 million in 2024 to $14.59 billion by 2029. That’s 109x, making it the fastest-growing major category in the forecast.

Synthetic Data Generation: 178.29% CAGR, $40.71M to $6.80B. The fastest-growing subsegment adds $6.76 billion in new spending by 2029. A 167x increase from a small 2024 base. Gartner predicts 60% of data and analytics leaders will encounter failures in managing synthetic data by 2027, which explains why governance spending is accelerating in parallel.

AI Data Governance: 163.75% CAGR, $14.82M to $1.89B. Starting from just $14.82 million in 2024, this subsegment grows 128x by 2029. Legal and compliance teams won’t accept the alternative. When AI systems produce ungoverned outputs, the liability exposure is unacceptable.

AI Data Integration Software: 137.13% CAGR, $71.73M to $5.38B. The largest AI Data subsegment by 2029. Connects fragmented data sources, delivering context that transforms generic models into systems that understand specific business operations.

AI Ready Datasets: 136.16% CAGR, $7.09M to $520.45M. These are prepackaged, curated datasets structured for AI and ML workflows. Think labeled image libraries for computer vision, cleaned financial datasets for forecasting, and domain-specific corpora for fine-tuning LLMs. Organizations buy them to skip the months of data collection, cleaning, and annotation that delay projects. Smallest subsegment by revenue, but 73x growth signals enterprises are willing to pay for time to production shortcuts.

The 2027 crossover: When agents overtake chatbots

Agentic AI: 118.73% CAGR, $15.04B to $752.73B. This is the single most dramatic dollar growth in the forecast. Agentic AI expands from $15 billion to $753 billion by 2029. That’s 50x. Nothing else comes close.

Gartner predicts the crossover will happen in 2027. Chatbots peak at $264.75 billion that year, while Agentic AI surges to $371.40 billion. By 2029, Agentic AI is 3.3x larger ($752.73B vs. $228.50B).

McKinsey’s data reinforces the trajectory: 62% of organizations are experimenting with AI agents, 23% report scaling them in at least one function. But scaling remains limited. Most organizations deploying agents are only doing so in one or two functions, primarily IT service desk and knowledge management.

Organizations building chatbot-only strategies should note that the category dominating 2025 and 2026 is projected to decline after 2027.

The Security Tax on Agentic AI

AI Cybersecurity: 73.90% CAGR, $10.82B to $172.01B. AI agents introduce attack surfaces that traditional security architectures weren’t built for. Gartner’s Hype Cycle for Application Security, 2025 (July 2025) projects that through 2029, over 50% of successful attacks against AI agents will exploit access control issues via direct or indirect prompt injection. The 16x growth in AI Cybersecurity spending reflects enterprises grappling with that exposure.

Production AI deployment requires security architectures designed for agentic systems. That’s a capability most organizations don’t have yet.

Infrastructure: Dominant but decelerating

AI Infrastructure remains the largest absolute spending category: $624.76 billion in 2024, growing to $2.25 trillion by 2029. McKinsey (August 2025) projects hyperscalers alone will spend $300 billion in capex over 2025. Their April 2025 analysis projects $5.2 trillion in data center investment by 2030.

But at 29.25% CAGR, infrastructure grows slower than every other major AI market except Services (26.93%). Market share drops from 54.6% of total AI spending in 2024 to 47.8% by 2029. The buildout is real. Differentiation happens elsewhere.

The 6% problem

Only 6% of organizations qualify as AI high performers despite 88% adoption. McKinsey’s analysis shows high performers are 3x more likely to redesign workflows around AI rather than layering it onto existing processes. They’re also 3x more likely to have committed executive leadership driving AI as a strategic priority.

The 155% CAGR for AI Data reflects organizations investing to close that gap. The 2027 chatbot-to-agent crossover marks the inflection point when autonomous capabilities surpass conversational interfaces in market size.

Gareth Herschel, VP Analyst at Gartner, frames the pressure: “D&A is going from the domain of the few to ubiquity. At the same time, D&A leaders are under pressure not to do more with less, but to do a lot more with a lot more, and that can be even more challenging because the stakes are being raised.”

Where the value accrues

Organizations positioned to capture value from this transformation may not be the ones building the biggest data centers. The Gartner data suggests they’re investing in capabilities that make AI systems work at enterprise scale: data readiness, governance, integration, and security.

AI Data Market (aggregate): 155% CAGR, $134M to $14.6B (109x)

  • Synthetic Data Generation: 178% CAGR, $41M to $6.8B (167x)
  • AI Data Governance: 164% CAGR, $15M to $1.9B (128x)
  • AI Data Integration: 137% CAGR, $72M to $5.4B (75x)
  • AI Ready Datasets: 136% CAGR, $7M to $520M (73x)

Other High-Growth Segments:

  • Agentic AI: 119% CAGR, $15B to $753B (50x)
  • AI Cybersecurity: 74% CAGR, $11B to $172B (16x)
  • AI Infrastructure: 29% CAGR, $625B to $2.25T (4x)

Gartner’s 4Q25 data points to a directional shift: AI spending is moving from infrastructure-first to data and capabilities-first architectures. The organizations treating data readiness as an afterthought are the ones most likely to stay stuck in the 94% that never make it past pilot.

Jan 13

Gartner’s 4Q25 Information Security forecast shows 15 categories capturing half of all new security spending through 2029

Gartner's 4Q25 Information Security forecast shows 15 categories capturing half of all new security spending through 2029

Fifteen cybersecurity categories are growing up to three times faster than the overall market, capturing $48.7 billion in new spending by 2029.

That’s nearly half of the $98.4 billion the entire security market will add over the next four years. Cloud Security Posture Management leads the pack at 29.36% CAGR. Cloud Access Security Brokers follow at 24.81%.

Enterprises are fundamentally restructuring their security budgets, and the driver is brutal in its simplicity. Organizations now manage an average of 112 SaaS applications across multiple cloud providers. 82% of misconfigurations are caused by human error, according to Exabeam’s analysis. And Gartner estimates 99% of cloud security failures through 2025 will be the customer’s fault, primarily from these misconfigurations. Manual oversight breaks under this kind of scale. Enterprises are responding by investing in automation that manages what people can’t across hundreds of cloud accounts, thousands of APIs, and millions of attack vectors.

Gartner’s 4Q25 update delivers the clearest signal yet about where enterprise security budgets are heading. The overall information security market grows from $213.5 billion in 2025 to $311.9 billion by 2029 at 10.03% CAGR. These fifteen high-growth categories are expanding at 10.30% to 29.36% CAGR, capturing investment dollars at rates that dwarf legacy security spending patterns.

What makes these categories different

Every high-growth category eliminates manual bottlenecks that break under cloud-native workloads. CSPM scans configurations continuously. CASB provides visibility into unauthorized SaaS usage. ZTNA verifies every connection rather than trusting the network location. With 79% of organizations using multiple cloud providers, according to Spacelift’s research, manual processes create mathematical impossibilities.

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 when systems get compromised. Security teams are finally getting ahead of threats instead of constantly playing catch-up.

And the ROI is quantifiable. 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 lifecycles by 80 days. U.S. breach costs average $10.22 million. These investments pay for themselves with a single prevented incident—a calculation CFOs understand.

Gartner's 4Q25 Information Security forecast shows 15 categories capturing half of all new security spending through 2029

The 15 categories reshaping enterprise security

1. Cloud Security Posture Management (CSPM) — 29.36% CAGR — $4.68B → $12.76B

CSPM platforms scan infrastructure continuously across AWS, Azure, and Google Cloud, automatically remediating misconfigurations before they become breaches. The 82% human error rate isn’t going to improve through training. Organizations managing 100+ cloud accounts need automation. CSPM adds $8.09 billion in new spending by 2029, the single largest dollar contribution among high-growth segments.

2. Cloud Access Security Brokers (CASB) — 24.81% CAGR — $2.30B → $5.58B

Here’s the brutal reality. Enterprises average 112 SaaS applications, but shadow IT accounts for 42% of all applications per JumpCloud’s data. IT stays blind to roughly 78 apps out of an average 187-app environment. The damage? 65% of shadow IT deployments result in data loss, and 52% lead to breaches, according to Mimecast research. CASBs restore visibility and control, growing to $5.58 billion by 2029.

3. Zero Trust Network Access (ZTNA) — 21.95% CAGR — $2.48B → $5.43B

ZTNA replaces the VPN model with application-specific access controls. Instead of network-level access, it provides application-specific connections verified for every request. Gartner predicts 70% of new remote access deployments will use ZTNA by 2025, up from less than 10% at the end of 2021. And 65% of companies plan to retire VPNs within one year per Cybersecurity Insiders data. This represents a wholesale rethinking of secure access. The perimeter-based model is dying. Good riddance.

4. Threat Intelligence — 21.73% CAGR — $2.58B → $5.69B

Modern threat intelligence platforms fuse telemetry from open-source intelligence, dark-web monitoring, vendor feeds, and internal logs. Machine learning prioritizes indicators based on organizational relevance. IBM data shows organizations integrating threat intelligence reduce detection and escalation costs while cutting incidents by 30%. The market reaches $5.69 billion by 2029 as enterprises shift from passive threat feeds to automated response integration.

5. Cloud Workload Protection Platforms (CWPP) — 21.53% CAGR — $5.98B → $13.11B

Traditional endpoint security can’t protect containers that spin up and vanish in seconds. Serverless functions executing for milliseconds? Legacy tools weren’t designed for that. CWPP solutions instrument workloads directly at the kernel or hypervisor level, monitoring system calls, file access, and network connections in real-time. The 21.53% CAGR reflects the rapid shift toward microservices and Kubernetes. As workloads migrate into container clusters, protecting them becomes a survival-level priority.

6. Consent and Preference Management — 20.22% CAGR — $0.81B → $1.64B

GDPR fines surpassed €5.88 billion by January 2025, according to DLA Piper’s annual survey. California’s CCPA penalties keep climbing. The California Privacy Protection Agency recently fined Todd Snyder $345,178 for inadequate opt-out and privacy request processes. Manual consent workflows can’t meet regulatory deadlines across jurisdictions. Automated platforms centralize preferences across web, mobile, and API endpoints while providing auditable logs for regulators.

7. Subject Rights Request (SRR) Automation — 14.26% CAGR — $1.24B → $2.01B

When users demand “delete my data,” these platforms automate orchestration across internal systems and third-party vendors. Privacy laws grant individuals rights to access, correct, and delete personal data with strict compliance timelines. SRR automation prevents the penalties that result from manual processing failures at scale, especially as more jurisdictions implement data privacy regulations.

8. Network Detection and Response (NDR) — 13.44% CAGR — $2.15B → $3.37B

NDR platforms establish behavioral baselines using statistical analysis and machine learning. When anomalies appear (unusual lateral movement, data exfiltration attempts, command-and-control traffic), they raise alerts or automatically isolate systems. The mindset shift matters here. Rather than hoping to prevent all attacks, sophisticated organizations invest in rapid detection that minimizes damage when attackers inevitably breach perimeters. Prevention alone isn’t sufficient anymore.

9. Vulnerability Assessment — 13.02% CAGR — $3.48B → $5.60B

Quarterly vulnerability scans are obsolete in CI/CD pipelines deploying multiple times daily. Modern assessment platforms provide continuous scanning integrated with exploit intelligence to prioritize patches based on real-world risk. DevOps teams need vulnerability detection that keeps pace with their deployment cadence. Anything less creates unacceptable exposure windows.

10. Tokenization — 12.68% CAGR — $1.34B → $2.11B

Tokenization replaces sensitive data with non-reversible tokens that can’t be mathematically decoded. The urgency comes from quantum computing advances. NIST finalized post-quantum encryption standards in August 2024, including ML-KEM (formerly CRYSTALS-Kyber) and ML-DSA (formerly CRYSTALS-Dilithium). Attackers already practice “harvest now, decrypt later”—collecting encrypted data today for quantum decryption within five to ten years. Organizations must begin quantum-safe transitions now.

11. Endpoint Protection Platform (EPP) — 12.51% CAGR — $17.68B → $28.36B

The largest single category adds $10.68 billion in new spending as ransomware attacks surge. U.S. ransomware attacks increased 149% year-over-year—from 152 incidents in early 2024 to 378 in the same period of 2025, according to Cyble analysis. Next-generation EPP platforms use behavioral analytics and signatureless detection to stop ransomware before encryption begins, catching what traditional antivirus misses.

12. Secure Web Gateway (SWG) — 11.63% CAGR — $4.44B → $6.74B

Malicious sites appear and disappear in hours. Cloud-delivered SWGs update threat intelligence in real-time, protecting remote and hybrid workforces wherever they connect. Integration with ZTNA creates comprehensive security that follows users across devices and locations without relying on network perimeters that no longer exist.

13. Web Application Firewalls (WAF) — 10.92% CAGR — $2.48B → $3.74B

Organizations expose hundreds of APIs and microservices—each a potential attack vector. Traditional network firewalls can’t inspect application-layer attacks like SQL injection, cross-site scripting, or API abuse. Modern WAFs use machine learning to differentiate legitimate user behavior from attack traffic without blocking customers. Getting that balance right is harder than it sounds.

14. Encryption — 10.64% CAGR — $1.35B → $1.98B

NIST’s standardization of quantum-resistant algorithms signals the urgency that organizations can no longer ignore. With quantum computing advances accelerating, encrypted data collected today faces decryption within a decade. Enterprises must transition to post-quantum cryptography now because full integration across complex environments takes years. This isn’t theoretical risk anymore.

15. Security Information and Event Management (SIEM) — 10.30% CAGR — $7.60B → $11.15B

AI transforms SIEM from reactive log collection to proactive threat hunting. The latest platforms embed unsupervised machine learning to detect zero-day attacks and automatically enrich alerts with context. Organizations using AI-powered automation save $1.9 million per breach and cut incident lifecycles by 80 days—turning security operations into a competitive advantage rather than a cost center.

Why this matters

Cloud complexity has proven exponential. With 79% of organizations using multiple cloud providers and managing hundreds of accounts, manual security processes break under the load. The 29.36% CAGR for CSPM isn’t market optimism. It’s organizational survival.

Shadow AI joins shadow IT as a core threatscape element. Shadow AI breaches cost $4.63 million—$670,000 more than standard incidents, according to IBM data. But AI also powers the best defenses, with automated security tools reducing breach lifecycles by 80 days. The same technology that creates vulnerabilities offers the most effective countermeasures.

Compliance costs keep accelerating. Between GDPR, CCPA, and emerging global regulations, manual compliance processes create escalating liability. Automated platforms turn regulatory requirements into competitive advantages by reducing fine exposure and accelerating data subject request responses.

Bottom Line

The organizations winning this transformation aren’t those with the largest security budgets. They’re the ones investing in the right categories at the right time. These fifteen segments define what modern security architecture looks like and capture nearly half of all new security spending through 2029.

Gartner’s 4Q25 data delivers a clear message. Security spending is shifting to automation-driven, zero-trust, cloud-native architectures. Organizations still relying on legacy approaches aren’t just falling behind. They’re accepting risks the market has already priced as unacceptable.

Source: Gartner Forecast: Information Security, Worldwide, 2023-2029, 4Q25 Update (Document G00843183, published December 18, 2025), showing overall market growth from $213.5B (2025) to $311.9B (2029) at 10.03% CAGR in constant currency.

 

 

Dec 22

Data readiness and security are driving AI’s $4.7 trillion run

Gartner Projects $4.7 Trillion AI Market by 2029 as Security and Data Drive Growth

Gartner’s most comprehensive AI spending forecast reveals the fundamental growth catalysts. AI-ready data predicted to deliver a 155% CAGR. Cybersecurity at 74%. Agentic capabilities crossing 50% of software spend by 2028.

Infrastructure gets the headlines. Hyperscalers are spending over $300 billion on data centers in 2025. McKinsey projects $5.2 trillion in data center investment by 2030. NVIDIA Blackwell deployments are driving 76% growth in accelerated server spending.

Gartner’s newly released Forecast Analysis: AI Spending, 4Q25 (December 17, 2025) tells a different story about where the acceleration is happening. Global AI spending reaches $1.8 trillion in 2025 and $4.7 trillion by 2029 at 33% CAGR. The growth catalysts:

  • AI Data. 155.4% CAGR. Spending increases 7x as enterprises recognize AI-ready data is non-negotiable for scaling.
  • AI Cybersecurity. 73.9% CAGR. From $26 billion to $172 billion. Over 50% of successful AI agent attacks will exploit prompt injection through 2029.
  • AI Models. 67.7% CAGR. Reasoning models underpin 70%+ of agentic AI applications by 2029.
  • AI Software. 47.0% CAGR. Agentic capabilities cross 50% of application software spend by the end of 2028. Non-agentic spending declines starting in 2027.

Infrastructure dominates absolute spending ($965 billion in 2025, growing to $2.25 trillion by 2029). At 29.2% CAGR, it’s the slower-growth segment. The acceleration is in data, security, and agentic capabilities.

The infrastructure buildout in context

The hyperscalers are building at a pace that strains global power grids. Dell’Oro Group’s Q2 2025 analysis shows worldwide data center capex up 43% year-over-year, with accelerated server spending surging 76% on NVIDIA Blackwell deployments. Amazon, Google, Meta, and Microsoft are collectively spending over $300 billion on data center infrastructure in 2025. CreditSights estimates aggregate hyperscaler capex reaches $602 billion in 2026, with approximately 75% earmarked for AI.

Gartner’s forecast aligns with infrastructure volume. AI-optimized server spending jumps 49% in 2026, representing 17% of total AI spending. GPUs account for over 90% of AI-optimized server spending on training throughout the forecast period. Infrastructure is table stakes. The differentiation is elsewhere.

Gartner’s bubble chart mapping 2026 growth rate (X-axis) against 2024-2029 CAGR (Y-axis), with bubble size representing 2025 spending. AI Data sits alone in the upper right quadrant. AI Cybersecurity and AI Models cluster at 70%+ CAGR. AI Infrastructure anchors the center as the dominant bubble. Source: Gartner Forecast Analysis: AI Spending, 4Q25, December 2025.

Gartner’s AI spending forecast by market, 2024-2029

The maturity gap

McKinsey’s 2025 State of AI survey explains why growth rates matter more than absolute spending for most organizations. 88% of organizations now use AI in at least one business function, up from 78% a year ago. Only 6% qualify as “AI high performers”, capturing meaningful enterprise-wide financial impact. Only 1% describe themselves as “mature” in AI deployment. Gartner’s CFO survey found just 11% of finance leaders from organizations implementing AI reported seeing actual financial returns.

The bottleneck is rarely compute. Gartner identifies three categories of readiness: infrastructure, data, and human. For every 100 days of AI implementation, 25 or more days may be consumed solely by change management and workforce resistance. Sharing work tasks with an AI agent, trusting results, and managing handoffs. That’s a fundamental shift in how employees work.

What the growth rates signal

AI cybersecurity’s 73.9% CAGR reflects a threat model shift. Security teams are spending because AI agents introduce attack surfaces that traditional security architectures weren’t designed to address. Gartner projects that over 50% of successful attacks against AI agents will exploit access control issues via prompt injection through 2029. By 2028, over 75% of enterprises will use AI-amplified cybersecurity products for most use cases, up from less than 25% in 2025.

AI data’s 155.4% CAGR signals enterprises are finally investing in foundations. The smallest segment by absolute spending is the fastest-growing because organizations scaling beyond pilots are discovering that AI-ready data isn’t optional. Labeled, annotated, quality-checked. By 2029, 61% of data integration software spend will focus on delivering GenAI-ready data, up from 8% in 2025. Synthetic data becomes dominant. 77% of data used for LLM training will be synthetic by 2029, up from 4% in 2025.

Agentic AI is reshaping software economics. By the end of 2028, software with agentic capabilities crosses 50% of total application software spend, up from 2% in 2024. Starting in 2027, non-agentic software spending declines. Investment in reasoning models underpins 70%+ of agentic AI applications by 2029. Open-source agentic frameworks will power more than 75% of enterprise AI agent deployments by 2028, eroding proprietary platform pricing power.

The inference shift is underway. By 2029, 66% of AI-optimized IaaS spending supports inference, not training. The balance shifts as embedded fine-tuned models become the norm in production applications.

Forecast assumptions by segment

AI Services. By 2029, 50% of all AI projects moving into production will be GenAI-centric, up from 12% in 2025. POC abandonment rates improve from 60% in 2024 to 35% in 2029. Specialized AI services command 20-30% price premiums.

AI Software. From 2027, spending on software without agentic capabilities starts declining. By 2027, one-third of agentic AI implementations will use combinations of agents with different skills for complex tasks.

AI Models. Starting in 2027, the shift toward in-house domain-specific language models constrains new spending in the specialized model market. Open-source model adoption erodes proprietary pricing power through 2029.

AI Platforms. By 2029, over 60% of enterprises will adopt AI agent development platforms to automate complex workflows. By 2030, enterprise application portfolios will include 40% custom applications built using AI-native development platforms, up from 2% in 2025.

AI Infrastructure. Export restrictions keep Chinese ASPs at about 50% of North American levels throughout the forecast. By 2026, NVL72 will become the de facto standard for large clusters. By the end of 2027, all hyperscalers will have reaffirmed Ethernet as their primary networking choice for AI workloads.

Devices. By 2029, more than 99% of PC microprocessors will have integrated on-device AI functionality, up from 15% in 2024. By 2027, efficient small language models will enable advanced GenAI to run locally on smartphones without cloud reliance.

The capital flow

The 2026 Gartner CIO Survey found GenAI and traditional AI among the most common technology areas selected for funding increases. 84% and 81% respectively. Nearly two-thirds of U.S. VC deal value went to AI companies in the first three quarters of 2025.

By 2027, the majority of AI buyers will define business outcomes from project launch. The market matures from technology-first experimentation to outcome-driven deployment. That shift from supply-push to demand-pull separates organizations capturing value from those still running pilots.

The infrastructure buildout continues. The growth signal is clear. Data readiness, security architecture, and agentic capabilities are where the acceleration is happening.

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 10

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

Bottom line: Identity security stands at an unprecedented crossroads, with machine identities creating greater complexity and potential chaos every security professional needs to plan for.

At Forrester’s 2025 Security & Risk Summit, Merritt Maxim, VP and Research Director at Forrester, delivered critical insights highlighting the escalating threats shaping identity security’s evolution. CISOs and security leaders find themselves navigating surging threats driven by generative AI, the rapid proliferation of non-human identities, and outdated IAM infrastructures originally designed solely for compliance.  Maxim emphasized a pressing urgency: identity strategies must adapt or risk catastrophic breaches and compliance failures.

Here’s a detailed breakdown of the top 10 insights from Forrester’s Summit, including the specific slides from Maxim’s presentation and deeper insights from Forrester’s latest data:

1. Identity Security Budgets Accelerate Toward $27.5B by 2029

IAM investment is growing explosively, set to nearly double from $13.4 billion in 2024 to $27.5 billion by 2029, driven by the escalating complexity and severity of identity-related threats such as AI-driven deepfakes, sophisticated supply-chain attacks, and rampant cloud misconfigurations. This positions IAM as cybersecurity’s third fastest-growing segment, underscoring identity security as a business-critical imperative.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

2. Hybrid IAM Still Dominates—77% Keep On-Premise Components

Despite the relentless push to the cloud, 77% of organizations continue relying on hybrid IAM deployments due to legacy infrastructure and regulatory constraints. Fully cloud-based identity management remains a distant reality, with only 9% fully transitioned. Maxim stressed hybrid IAM’s persistence, highlighting the necessity for seamless integration capabilities between on-premises systems and cloud IAM platforms.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

3. Third-party Risk Matches Compliance as a Top IAM Driver

Forrester revealed a pivotal shift: managing third-party identities (32%) is now equally critical as regulatory compliance (32%) in driving IAM investments. High-profile breaches at Okta and CyberArk underscore vulnerabilities introduced by third-party identities, necessitating robust governance models that go beyond basic compliance checklists.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

4. Static Entitlements Are Obsolete; Zero Standing Privilege Is Now Mandatory

The static entitlement model—assigning privileges during onboarding—is officially outdated. Forrester highlighted Zero Standing Privilege (ZSP) architectures as the definitive new standard, utilizing the Continuous Access Evaluation Protocol (CAEP) to dynamically assign permissions at runtime. This strategy mitigates rampant privilege sprawl, dramatically reducing attack surfaces.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

5. Identity Management Converges Across Security, Marketing, and CX

Enterprises are rapidly integrating fragmented identity management systems across marketing, customer experience (CX), fraud prevention, and security. Maxim emphasized that businesses consolidating these functions significantly improve detection speed, minimize breaches, and enhance end-user experience. Leveraging customer preference and security data together is becoming a strategic advantage.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

6. Vendor Consolidation Radically Reshapes IAM Markets

IAM vendor consolidation accelerated significantly, highlighted by major moves such as Palo Alto Networks acquiring CyberArk, Ping Identity merging with ForgeRock, and CrowdStrike purchasing Adaptive Shield. Enterprises increasingly demand integrated identity platforms combining PAM, IGA, and Identity Threat Detection & Response (ITDR), driving these high-profile acquisitions.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

7. Generative AI Exacerbates Identity Threats but Offers Transformational Defenses

Generative AI escalates identity threats dramatically through enhanced phishing and sophisticated deepfake impersonations. Conversely, GenAI’s defensive capabilities are equally transformative, enabling automated identity threat detection, rapid response, and real-time entitlement adjustments. Maxim described these dual dynamics as essential to future IAM strategies.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

8. Machine Identities Are a Critical Emerging Attack Vector

The explosive growth in non-human identities (IoT, APIs, AI agents) vastly expands attack surfaces. Enterprises urgently need automated platforms from vendors like CyberArk, Venafi, and HashiCorp to manage this surge. Forrester highlighted machine identities as a rapidly intensifying risk requiring immediate attention and robust governance.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

9. Phishing-Resistant MFA Is Dangerously Under-Deployed

Alarmingly, only 21% of companies deploy phishing-resistant MFA after breaches, despite the increasing sophistication of MFA-bypass attacks. Forrester insists enterprises must urgently adopt solutions like FIDO2 and WebAuthn. Maxim warned that neglecting these standards leaves companies dangerously exposed to credential-based compromises.

Top 10 Identity Security Insights from Forrester’s 2025 Security & Risk Summit

10. Context-Aware IAM Becomes a Real-time Security Necessity

Static IAM fails against machine-speed threats. Context-aware IAM, powered by dynamic authorization, continuously assesses real-time user behavior, device posture, and threat intel. Forrester identifies this adaptive approach as critical, turning identity from a passive gatekeeper to a proactive defender, which is essential for stopping attacks before damage occurs

10. Context‑Aware IAM Defines the Future of Access Control Best Slide: Slide 21 – Runtime Context and Adaptive IAM Model The next generation of IAM is contextual, continuous, and AI‑assisted  Convergence, Consolidation, And… . Static permissions are being replaced with adaptive models that evaluate risk in real time — factoring in behavioral biometrics, device posture, and environmental signals. This “runtime context” turns identity from a passive gatekeeper into an active defender capable of making split‑second decisions as threats unfold.

Bottom Line: Adaptive identity security defines enterprise survival

Identity security has become synonymous with enterprise survival. Merritt Maxim’s compelling insights from Forrester’s 2025 Security & Risk Summit underscore a new identity imperative: convergence, consolidation, and context must drive strategic identity transformations. Following Forrester’s lead, enterprises must prioritize investment in dynamic Zero Standing Privilege architectures, integrated identity platforms, generative AI-enabled threat response, robust machine identity management, and phishing-resistant MFA immediately.  The future of enterprise resilience hinges directly on evolving identity security today.

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.

Oct 28

Top ten cybersecurity startups to watch in 2025 according to $3.21B in investor bets

Top Ten Cybersecurity Startups to Watch in 2025 According to $3.21B in Investor Bets

While the industry still debates whether AI will transform cybersecurity, investors have already made up their minds.

Based on an analysis of the latest Crunchbase data compiled recently that spans January 2024 to October 2025, ten standout startups captured $1.41 billion in new funding, signaling that machine-speed defense against AI-driven threats is no longer optional; it’s an operational reality. Together, these ten startups have raised $3.21 billion, which represents one of the heaviest capital concentrations in cybersecurity startups to date.

Investors are gravitating to cybersecurity startups that solve complex problems

CrowdStrike’s Falcon 2025 event, held earlier this year in Las Vegas, showcased a series of new agentic AI developments that, taken together, reflect how cross-platform and cross-competitor collaboration aimed at shutting down increasingly complex weaponized AI threats leads to faster innovation. VentureBeat’s analysis of the many announcements there explains how the cybersecurity company is betting on agentic AI to defeat adversaries.

Interested in quantifying how AI is impacting investors’ decisions, I completed an analysis using Crunchbase data covering 342 verified cybersecurity startups with active funding. Selection was weighted toward recent momentum, total funding scale, stage maturity, AI integration, and proof through multiple rounds.

The key takeaway: Institutional capital is consolidating around companies that make autonomous security practical, and agentic AI is at the core of that direction. But AI is not enough; investors are looking for the ability to scale in enterprises once they have AI integrated into their core platforms.

AI in cybersecurity: Tablestakes, not a ticket to premium valuation

Sixty percent of startups integrate AI into their core technology. Yet contrary to hype, that hasn’t bought them higher valuations.

  • AI-integrated startups average $283M in funding.
  • Non-AI specialists average $378M.

Crunchbase data shows investors reward defensible specialization as much as AI capability. Quantinuum’s $925M for post-quantum cryptography and Zama’s $139M for homomorphic encryption prove that solving foundational security problems often supersedes AI as a differentiator.

Still, AI holds weight in investment decisions. Six AI-driven startups pulled $1.70B (52.8%), while four non-AI companies captured $1.51B (47.2%). Both models earn trust by underscoring AI for operational speed and deep tech for architectural resilience. And with seven of ten now at Series B maturity, investors are backing platforms that have already demonstrated enterprise traction, not experiments.

1. Quantinuum ($925M, Series B) Post-Quantum Defense. Closed a $600M Series B in August 2025. The company is building the only mathematical safeguard against the inevitable collapse of RSA and ECC encryption under quantum computing.

2. Saronic ($845M, Series B) Autonomous Maritime Security, Raised $175M in July 2024 for AI-powered unmanned surface vessels. With 90% of trade moving across exposed waterways, Saronic brings AI defense to the physical infrastructure that most enterprises overlook.

3. Auradine ($314M, Series B) AI Silicon for Security. Raised $80M to expand custom silicon that accelerates cryptographic workloads 10x faster than general-purpose hardware, eliminating bottlenecks in AI-driven security deployments.

4. Tines ($271M, Series B) No-Code Automation. Secured $50M Series B. Turns analysts into automation builders, saving 40+ hours weekly with drag-and-drop workflows that are proving critical for overextended SOC teams.

5. Dream Security ($198M, Series B) Critical Infrastructure Defense. Closed $100M in 2025. Their sovereign AI platform equips critical infrastructure with defenses calibrated to nation-state-level threats, providing a layer that traditional enterprise tools cannot reach.

6. Upwind Security ($180M, Series A)  Runtime Cloud Visibility. Raised $100M in December 2024. Focused on runtime intelligence, detecting abnormal behavior live rather than flagging static misconfigurations. Reduces false positives, elevates real threats.

7. Zama ($139M, Series B)  Homomorphic Encryption. Raised $57M in June 2025 after a $73M Series A in March 2024. Provides production-ready fully homomorphic encryption, enabling AI models to compute securely on encrypted data.

8. Noma Security ($132M, Series B)  Securing AI Agents. Closed $100M in 2025. Built to harden AI systems against prompt injection and model poisoning as enterprises push decision-making into autonomous agents.

9. ZeroEyes ($107M, Series B)  Firearm Detection AI. Raised $53M in 2025. Eleven rounds in, their AI models detect firearms on video feeds in seconds—cutting active shooter response time dramatically.

10. Upscale AI ($100M, Seed)  AI Networking Infrastructure. Raised a $100M Seed round in 2025. Building AI-native networking with hardware-accelerated encryption, aimed at high-performance compute environments.

The Bottom Line

Series B dominance (70%) shows that capital is flowing into platforms with market traction, not speculative bets. Forty-six rounds across these ten companies demonstrate durability and enterprise validation. The signal to security leaders is becoming clear based on the escalating nature of weaponized AI attacks: manual security processes are now liabilities. Defending at human speed against AI-enabled attackers is untenable. Investors understand this. $1.41B in recent capital confirms it.

← Older Entries