Mar 28
Palo Alto Networks spent $29 billion acquiring three companies. ServiceNow spent $11.6 billion on three more. Alphabet paid $32 billion for Wiz. The startups building agentic AI defenses raised $3.6 billion. Total MCP security funding for 17,000+ deployed servers: $40 million. Then RSAC 2026 happened.
Mar 24
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.
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.
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.
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.
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.
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.
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.
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.
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
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:
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
Agentic AI spending grows 141% in 2026 to $201.9 billion. By 2027, it will overtake chatbot and assistant spending for the first time. Then chatbot spending starts declining. I’ve tracked Gartner’s AI forecasts through multiple iterations. This crossover changes where security risk concentrates for every security professional reading this.
The crossover is in the segment-level data tables of Gartner’s Forecast: AI Spending, Worldwide, 2024–2029, 4Q25. The headline number is well known: $2.53 trillion in 2026, $4.7 trillion by 2029 at 33% CAGR. The segment breakdowns are not. Eight markets. Nineteen sub-segments. The sub-segment data tells a different story than the top line.
This is Gartner’s first dedicated AI spending forecast, and I’ve been waiting for it. Gartner states that comparisons to previous AI estimates are not meaningful because the scope widened, adding AI cybersecurity, agentic AI as a separate segment from chatbots, AI data technology, and expanded infrastructure coverage. Gartner writes, “This is the first iteration of the forecast on AI spending that Gartner has published. Gartner has significantly expanded and modified its AI forecast coverage. Spending comparisons to previous iterations are therefore not meaningful as the scope has widened. This includes both coverage of new markets and broadened definitions of the types of AI spending that are reflected in some market segments.”
Forrester’s Predictions 2026: Cybersecurity and Risk arrives at the same warning from a different angle: an agentic AI deployment will cause a publicly disclosed breach in 2026, leading to employee dismissals. Two firms. Same conclusion. The spending data explains why.
CAPTION: Total worldwide AI spending, 2024–2029. $1.14T to $4.71T. 33% CAGR. Growth decelerates from 54% (2025) to 16% (2029) as the base expands. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025).
AI infrastructure dominates at $1.37 trillion, 54% of the total. AI software follows at $452.5 billion, growing 60% year-over-year. AI services add $588.6 billion. AI cybersecurity and AI data are the outliers: growing at 74% and 155% CAGR, respectively, rates that dwarf everything else in the forecast.
Source: Gartner Forecast: AI Spending, Worldwide, 2024–2029, 4Q25 (December 19, 2025). All figures in U.S. dollars. CAGR = 2024–2029. Gartner press release: https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026
AI-optimized servers alone account for $421.6 billion in 2026, growing to $699.7 billion by 2029. AI processing semiconductors add $289.4 billion. AI-optimized IaaS hits $38.3 billion at 71% CAGR, the fastest-growing infrastructure sub-segment. AI network fabric, a new category in this forecast, reaches $28.7 billion.
Infrastructure’s share drops from 54% to 48% by 2029 as software and services scale faster. The capital-intensive build-out phase is not over.
Gartner now splits AI software into chatbots/assistants and agentic AI. The spending lines cross in 2027.
CAPTION: Agentic AI spending overtakes chatbot/assistant spending by 2027. Chatbots peak at $264.7B then decline. Agentic AI grows at 119% CAGR to $752.7B by 2029. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). AI Software segment, Table 1-2.
Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). CAGR = 2024–2029.
Chatbots talk to people. Agents act on behalf of people. They access databases, execute transactions, chain multi-step workflows without human approval at each step. The attack surface has moved well beyond conversation windows. Agents are autonomous decision engines with production access.
Gartner’s Top Trends in Cybersecurity for 2026 lists agentic AI oversight as the number-one trend. Forrester’s Predictions 2026: Cybersecurity and Risk goes further: an agentic AI deployment will cause a public breach this year, and employees will lose their jobs for it. Forrester senior analyst Paddy Harrington calls it a “cascade of failures,” not a single point of error. Two analyst firms. Different methodologies. Same conclusion. Security strategies built for chatbot-era risk have a shelf life measured in quarters, not years.
Gartner created a dedicated AI cybersecurity market for the first time in this forecast. It nearly doubles in 2026. But the category name hides a structural split that matters more than the growth rate.
Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). CAGR = 2024–2029.
Two sub-segments. Two very different problems.
AI-amplified security ($48.5 billion, 94.5% of the market) is what most enterprises mean when they say “AI cybersecurity.” This is AI working for your security team. Machine learning models that analyze network traffic patterns and flag anomalies faster than a human analyst can. Natural language processing that reads threat intelligence feeds and correlates indicators of compromise across millions of data points in seconds. Automated triage systems that prioritize which of the 11,000 daily alerts actually need a human response. AI-powered endpoint detection that identifies malware variants that signature-based tools miss. Behavioral analytics that learn what normal looks like for each user and flag deviations. Security orchestration platforms that automate incident response playbooks, reducing mean time to containment from hours to minutes.
This is the category where enterprises are spending aggressively. And for good reason. The math on analyst workloads demands it. Security operations centers are drowning in alerts, facing a persistent talent shortage, and defending attack surfaces that expand every quarter. AI-amplified tools address all three.
Securing AI ($2.8 billion, 5.5% of the market) is the other problem. AI-amplified security puts AI to work defending the enterprise. Securing AI reverses the relationship entirely — defending the AI itself. Protecting the models, the training data, the inference pipelines, the agent workflows, and the decision outputs that enterprises are deploying at $2.53 trillion in 2026. Prompt injection defenses. Model access controls. Training data poisoning detection. Output validation. Agent permission boundaries. Audit trails for autonomous decisions.
The distinction matters because they protect different things. AI-amplified security protects your enterprise using AI. Securing AI protects the AI itself. One is a tool. The other is the thing that needs protecting. Enterprises are investing 17 times more in the tool than in protecting the thing the tool runs on.
Gartner names the mechanism driving AI software growth: vendor push. Software providers are integrating GenAI and agentic AI into existing product lines. AI software grows from $143 billion in 2024 to $981 billion by 2029 at 47% CAGR.
For CISOs, vendor push changes the equation. AI capabilities are being added to tools already in production. Often without explicit procurement decisions. The AI features embedded in your existing ERP, CRM, and developer platforms may already exceed what your security team has inventoried. Shadow AI is vendors activating AI inside products you already own.
AI data technology: $134 million in 2024. $3.1 billion in 2026. $14.6 billion by 2029. The 155% CAGR is the highest in the forecast. The 277% year-over-year growth in 2026 is the steepest single-year jump of any segment.
Synthetic data generation is the standout sub-segment, going from $41 million to $6.8 billion by 2029. Gartner is direct: enterprises need AI-ready data with proper labeling, quality checks, and compliance. For organizations running AI projects on ungoverned data, the readiness gap compounds every quarter.
CAPTION: AI spending markets ranked by five-year CAGR. AI Data (155%) and AI Cybersecurity (74%) lead. AI Infrastructure is the largest by absolute dollars. Source: Gartner Forecast: AI Spending, 4Q25 (December 2025).
Indirect AI services, where AI is a supporting component in a larger project, grow from $78.4 billion in 2024 to $255.9 billion in 2026 at 50% CAGR. Direct AI services hit $332.8 billion. By 2028, indirect overtakes direct.
Indirect AI means capabilities embedded in consulting and implementation projects that procurement does not classify as AI. If you cannot see it in your AI inventory, you cannot govern it.
AI-optimized servers alone hit $421.6 billion in 2026, just below the entire AI software market at $452.5 billion. By 2029, servers reach $699.7 billion. Cloud providers are building capacity for AI workloads that have not materialized at scale. The infrastructure is ahead of the applications.
Gartner’s DSML segment includes a dedicated agent builder platforms sub-segment at $5.0 billion in 2026, reaching $13.7 billion by 2029. AI observability and governance adds $1.3 billion, growing to $4.0 billion. The xOps sub-segment (MLOps, DataOps, ModelOps) is the largest at $15.0 billion.
Together, these form the tooling layer for building, monitoring, and governing agents in production. The enterprise agentic stack is materializing in the spending data. Most organizations have not formalized it in their architecture.
If you take one thing from this forecast into a budget meeting, take this table. I built it from the raw spreadsheet data. Six years of AI deployment spending next to AI security spending. The bottom row is the one that gets the questions.
Source: Gartner Forecast: AI Spending, 4Q25 (December 2025). All percentages derived from Gartner’s published data tables (Tables 1-1 and 1-2).
The ratio improves over time. Securing AI goes from 0.07% in 2024 to 0.25% by 2029. But watch the absolute numbers. In 2029, enterprises will spend $4.71 trillion deploying AI and $11.6 billion securing it. The percentage gets better. The dollar gap gets wider. Every year, the market grows its way into a larger exposure.
Three things worth tracking from the segment data:
The agentic crossover. Agentic AI overtakes chatbot spending in 2027. The enterprise risk profile shifts from conversational data leakage to autonomous decision-making at scale. CISOs who build agentic governance frameworks in 2026 position themselves before the inflection. The spending curve says the window is narrowing.
The securing-AI gap. $2.8 billion to protect AI systems in a year when $2.53 trillion deploys them. Enterprises are enthusiastic about using AI for defense. The investment in defending AI itself has not caught up.
Data readiness is the bottleneck. The 277% growth in AI data spending confirms that AI without governed data delivers diminished returns. Data classification investments directly enable or constrain AI ROI.
If your security budget is growing at 12% and AI deployment inside your enterprise is growing at 44%, the gap compounds every quarter. You cannot close it by holding steady. The organizations getting this right treat AI security as a proportion of AI deployment, not a fixed line item.
—
Sources
Gartner, Forecast: AI Spending, Worldwide, 2024–2029, 4Q25, December 19, 2025, ID G00843179.
Gartner press release (January 15, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026
Gartner, Top Trends in Cybersecurity for 2026 (February 5, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-02-05-gartner-identifies-the-top-cybersecurity-trends-for-2026
Gartner, IT Spending Forecast 1Q26 (February 3, 2026): https://www.gartner.com/en/newsroom/press-releases/2026-02-03-gartner-forecasts-worldwide-it-spending-to-grow-10-point-8-percent-in-2026-totaling-6-point-15-trillion-dollars
Forrester, Predictions 2026: Cybersecurity and Risk (October 2025): https://www.forrester.com/blogs/predictions-2026-cybersecurity-and-risk/
All dollar figures in U.S. dollars. Growth rates and CAGR derived from Gartner’s published data tables (Tables 1-1 and 1-2).
Feb 10
Over 57% of employees are using personal GenAI accounts for work. A third of them admit to uploading sensitive data into tools their security teams haven’t approved. Meanwhile, agentic AI is proliferating through no-code platforms and vibe coding, creating attack surfaces most CISOs can’t see, let alone govern. And quantum computing? No longer a 10-year planning horizon. It’s a 2030 action deadline.
Gartner’s Top Trends in Cybersecurity for 2026 report, released February 5, 2026, identifies six forces reshaping how CISOs must operate. These cut across governance, AI adoption, identity, workforce, and cryptographic strategy simultaneously. None of them is incremental.
The trends report lands alongside Gartner’s updated Forecast: Information Security, Worldwide, 2023–2029, 4Q25 (G00843183, December 18, 2025) and the Forecast Analysis: Information Security, Worldwide, 2026 (G00838442, February 5, 2026), which together project global information security spending reaching $244.2 billion in 2026, up 13.3% in current U.S. dollars. I’ve tracked this forecast through multiple quarterly updates. The trajectory keeps steepening. The six trends below explain where that money is going and why.
“Cybersecurity leaders are navigating uncharted territory this year as these forces converge, testing the limits of their teams in an environment defined by constant change,” said Alex Michaels, Director Analyst at Gartner. “This demands new approaches to cyber risk management, resilience, and resource allocation.”
Before getting into the six trends, context matters. Gartner’s 4Q25 forecast shows the three major security segments all growing at double-digit constant currency rates in 2026:
Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183). Constant currency rates.
Cloud security remains the fastest-growing subsegment at 28.8% growth in 2026. Nothing else comes close. The combined cloud security market (cloud security posture management, cloud access security brokers, and cloud workload protection platforms) is projected to reach $32.4 billion by 2029, with a 25% CAGR in constant currency. I’ve been watching this subsegment accelerate for three quarters straight. CSPM alone is growing at a 31.30% CAGR.
Cloud security spending reaches $32.4 billion by 2029. CSPM leads at 31.30% CAGR. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)
This is the trend that touches everything else on this list. Employees and developers are deploying AI agents through no-code/low-code platforms and “vibe coding” at a pace that outstrips security governance. Unmanaged AI agent proliferation. Unsecured code. Compliance violations that most security teams don’t even have visibility into yet. That’s the picture Gartner is painting.
Gartner’s recommendation is blunt: cybersecurity leaders must identify both sanctioned and unsanctioned AI agents operating within their environments, enforce access controls and data guardrails, and develop incident response playbooks specific to agent-driven threats.
“While AI agents and automation tools are becoming increasingly accessible and practical for organizations to adopt, strategic cybersecurity planning for these technologies is essential,” said Michaels. Cybersecurity leaders must work cross-functionally to manage agentic AI adoption, identifying sanctioned and unsanctioned AI agents, enforcing data access controls, and developing incident response playbooks.
The spending data backs this up. Gartner’s 4Q25 forecast projects the AI-amplified security market reaching $160 billion by 2029, up from $49 billion in 2025. Gartner is clear that this isn’t additive spending. It represents the portion of existing security products that now embed AI capabilities. But the expectation tells the story: over 75% of enterprises will use AI-amplified cybersecurity products by 2028, up from less than 25% in 2025. Vendors that don’t embed AI will lose shelf space. (For more on AI security platforms, see Gartner’s Top Strategic Technology Trends for 2026, which predicts that over 50% of enterprises will use AI security platforms to protect their AI investments by 2028.)
Regulators are getting personal. Boards and executives now face direct liability for compliance failures. Not just organizational fines, but individual accountability. The penalties for inaction have moved from theoretical to career-ending. Across multiple jurisdictions simultaneously.
Gartner advises cybersecurity leaders to formalize collaboration across legal, business, and procurement teams to establish clear accountability for cyber risk. Align control frameworks to recognized standards. Address data sovereignty concerns before they become enforcement actions. The organizations doing this well are treating regulatory preparedness as a core security function, not an annual compliance checkbox.
This is where the spending data gets interesting. Gartner’s forecast shows security consulting services growing from $24.2 billion (2024) to $36.6 billion (2029), adding $12.4 billion in five years. Security professional services follow a similar trajectory: $27.3 billion to $40.8 billion, adding $13.5 billion. Organizations are buying outside expertise because they can’t build regulatory competence fast enough in-house. I’ve been covering these numbers for three quarters, and the services growth is the part of the forecast that keeps surprising me.
Infrastructure protection adds $26.4 billion between 2024 and 2029, the largest absolute growth of any subsegment. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)
Gartner predicts advances in quantum computing will render the asymmetric cryptography that organizations rely on unsafe by 2030. Four years. That’s the window to adopt post-quantum cryptography alternatives before “harvest now, decrypt later” attacks start cashing in on data that adversaries are collecting today.
Organizations need to identify their cryptographic deployments, assess data sensitivity and lifespan, and prioritize cryptographic agility. That last phrase keeps coming up in my conversations with CISOs. The ability to swap encryption methods without re-architecting entire systems. Swapping an algorithm is one thing. Doing it across a production environment without downtime is an entirely different problem.
“Post-quantum cryptography is reshaping cybersecurity strategies by prompting organizations to identify, manage, and replace traditional encryption methods, while prioritizing cryptographic agility,” said Michaels. “By investing in these capabilities and prioritizing migration now, assets will be secured when quantum threats become a reality.“
The encryption market in Gartner’s 4Q25 forecast grows from $1.04 billion in 2023 to $2.04 billion by 2029 at an 11.95% CAGR. A 2.0x increase. For what has historically been one of the slower-growing security subsegments, that’s a significant acceleration. Quantum urgency is changing the math.
AI agents are breaking traditional IAM models. Plain and simple. Identity registration and governance, credential automation, and policy-driven authorization weren’t designed for autonomous machine actors that can initiate actions, access data, and interact with systems without human intervention. The scale problem compounds fast: when every employee can deploy dozens of AI agents, the identity surface area explodes.
Gartner recommends a targeted, risk-based approach. Invest where gaps and risks are greatest. Leverage automation where possible. The practical starting point is understanding which AI agents carry the most privilege and the least oversight. Those are your highest-risk identities right now, and most organizations haven’t inventoried them.
The identity market is already significant. Gartner’s 4Q25 forecast shows identity access management growing from $18.7 billion (2024) to $29.0 billion (2029), adding $10.3 billion in five years. That’s before the full scale of agentic AI identity requirements hits the market. IAM vendors that solve machine-actor identity at scale will capture a disproportionate share of that $10.3 billion growth.
AI-enabled security operations centers are enhancing alert triage and investigation workflows. The technology works. But deploying AI into a SOC doesn’t automatically reduce headcount needs. It changes the skill mix. Analysts who excelled at manual triage need different capabilities to oversee AI-driven workflows. Organizations are discovering this the hard way. That’s an organizational transformation challenge, and throwing more technology at it doesn’t help.
“To realize the full potential of AI in security operations, cybersecurity leaders must prioritize people as much as technology,” said Michaels. “Strengthening workforce capabilities, implementing human-in-the-loop frameworks into AI-supported processes and aligning adoption with clear strategic objectives will be critical to maintaining resilience as SOCs evolve.”
The talent dimension makes this harder than it already sounds. ISC2’s 2024 Cybersecurity Workforce Study, published in October 2024, documented a global workforce gap of 4.8 million professionals, a 19% year-over-year increase. The active workforce flatlined at 5.5 million (up just 0.1%). The numbers are brutal: 25% of organizations reported cybersecurity layoffs in 2024. 37% faced budget cuts. 90% report skills shortages. 58% believe the shortage puts their organization at significant risk. On the spending side, managed security services are growing at 11.1% in 2026, the fastest rate in the services segment. Organizations can’t hire fast enough, so they’re buying managed SOC capacity instead.
Existing security awareness programs are failing. Full stop. A Gartner survey of 175 employees conducted between May and November 2025 found that 57% use personal GenAI accounts for work purposes, while 33% admit to uploading sensitive information to tools their organizations haven’t sanctioned. Those numbers should alarm every CISO reading this. A third of your workforce is actively feeding proprietary data into tools you can’t audit.
Gartner recommends shifting from general awareness training to adaptive behavioral programs that include AI-specific tasks. Generic compliance videos won’t cut it here. The organizations getting this right are making approved GenAI tools easy to access and unsanctioned tools hard to justify. Trying to ban GenAI outright just drives usage underground and costs you talent.
Strengthening governance, embedding secure practices, and establishing clear policies for authorized GenAI use will reduce exposure to privacy breaches and intellectual property loss. The governance gap on GenAI usage is, in my view, the most underestimated risk on this entire list. Every other trend has a spending line item attached to it. This one requires behavioral change, which is harder to buy.
Gartner’s year-by-year spending trajectory shows the acceleration curve these six trends are riding:
Source: Gartner Forecast: Information Security, Worldwide, 2023–2029, 4Q25 Update (G00843183, December 18, 2025). Current U.S. dollars.
CSPM and CASB lead all security categories with 31% and 26% CAGR through 2029. Source: Gartner 4Q25 Forecast. (Please click on the image to expand for easier reading)
Three of the six trends (agentic AI oversight, IAM for machine actors, and GenAI awareness) are fundamentally about the same problem: autonomous AI systems operating inside enterprise environments without adequate governance. The other three (regulatory volatility, post-quantum readiness, and AI-driven SOCs) are the structural forces those governance failures will collide with. That convergence is the signal about where 2026 budgets need to go.
The organizations that will navigate this environment successfully are doing three things simultaneously:
Mapping their AI agent footprint now. If you don’t know how many AI agents are operating across your environment, sanctioned and unsanctioned, you can’t govern what you can’t see. Gartner’s 75% AI-amplified product adoption projection by 2028 means this window for establishing control is narrow.
Building cryptographic agility into their architecture. The 2030 quantum deadline means migration planning starts in 2026, not 2028. The encryption market’s 2.0x growth reflects early movers. Late movers face rip-and-replace costs that compound every quarter they wait.
Investing in people alongside AI tooling. AI-enabled SOCs work when human operators have the skills to oversee them. The ISC2 data is unambiguous: a 4.8 million professional gap growing at 19% year-over-year. Managed security services growth at 11.1% tells you where CISOs are finding capacity.
Gartner’s numbers aren’t projections anymore. They’re procurement trends already hitting finance systems. The $244.2 billion flowing into information security this year will fund agentic AI governance, quantum migration, and SOC transformation, whether your organization participates or not.
Bottom line: CISOs planning for 2027 are watching their competitors buy the tools they’ll be scrambling for in 18 months. The data says move now.
Jan 22
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.
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.
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.
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.
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.
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.
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.”
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.
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.
Dec 22
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:
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 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.
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.
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.
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 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.
Oct 29
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.
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.
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.
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”.
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:
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
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.
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.
Sixty percent of startups integrate AI into their core technology. Yet contrary to hype, that hasn’t bought them higher valuations.
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.
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.
