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Dec 30

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AI Security market 2025 funding data, top startups, and the ServiceNow factor

ServiceNow dropped $11.6 billion on security acquisitions in 2025 alone. Armis for $7.75 billion. Moveworks for $2.85 billion. Veza for roughly $1 billion. In 2025, just one company, ServiceNow, spent more on acquiring security startups than 175 startups raised in two years. Meanwhile, the entire AI security startup ecosystem raised $8.5 billion across 175 companies over 24 months. That single data point should reshape how security leaders think about vendor consolidation and how AI builders think about their exit paths.

I analyzed Crunchbase data covering every AI security startup that raised Series A, B, or C funding between January 2024 and December 2025. The patterns are striking.

The acceleration is real

Q1 2024: $274 million across 8 deals. Q4 2025: $2.17 billion across 28 deals. That’s 8x growth in quarterly funding over two years.

The full-year numbers tell the story more clearly. 2024 saw $2.16 billion in total funding. 2025 hit $6.34 billion, nearly tripling. Average deal sizes jumped from $34 million to $54 million. This isn’t a gentle upward trend. The market is restructuring in real time.

Where the money flows

Network and Zero Trust infrastructure captured $1.9 billion across 44 companies. Tailscale‘s $161 million Series C reflects what enterprises already know. VPN architectures are dying. Identity-based access is replacing them.

Threat Detection and SOC automation drew $1.2 billion across 28 companies. 7AI‘s $130 million Series A stands out as one of the largest A funding rounds in this category. The bet: AI agents can handle the full security operations lifecycle at a scale human analysts cannot match.

Identity and Access Management pulled $990 million. But here’s what matters: that money went to just 6 companies. Saviynt‘s $700 million Series B dominates the category. When one company captures 71% of a category’s funding at Series B, investors see platform consolidation ahead. ServiceNow’s Veza acquisition, three weeks later, validated that thesis.

Insights into deal sizes

Median tells a different story from average deal sizes. Series A median: $20 million. Series A average: $28 million. The gap widens at later stages. Series C median: $85 million. Series C average: $119 million.

Translation: mega-deals skew the data significantly. Eighteen companies raised $100 million or more. Those 18 deals represent 10% of companies but 40% of total funding. For every Saviynt raising $700 million, dozens of startups are raising $15-25 million Series A rounds.

The AI/LLM security gap

Only 13 companies focus specifically on securing AI systems, LLMs, and agentic applications. Total funding: $414 million. That’s less than 5% of the $8.5 billion total. For context: ServiceNow paid more for Veza alone than the entire AI/LLM security category raised in two years.

The players building in this space:

• Noma Security ($100M, Series B). Unified AI and agent security platform.

• Credo AI ($21M, Series B). AI governance and compliance automation.

• Lakera ($20M, Series A). Real-time GenAI security against LLM vulnerabilities.

• Prompt Security ($18M, Series A). Enterprise generative AI adoption platform.

• GetReal Security ($17.5M, Series A). Deepfake and AI-generated impersonation defense.

• Jericho Security ($15M, Series A). Training against generative AI-powered attacks.

Enterprises are deploying AI systems at unprecedented rates. Shadow AI breaches cost $4.63 million per incident. That’s $670,000 more than standard breaches, according to IBM’s 2025 Cost of a Data Breach Report. Model Context Protocol vulnerabilities. Prompt injection attacks. Data exfiltration through AI assistants. The attack surface expands while protection lags.

Either these 13 companies scale rapidly, established players acquire their way into the space, or CISOs face a protection gap without commercial solutions.

How spending breaks out geographically

The U.S. captured $6.1 billion across 119 companies. That’s 71% of total funding. Israel remains the second hub: 15 companies, $738 million. Germany, the UK, and Canada trail with single-digit percentages.

Within the U.S., California dominates: $2.7 billion across 62 companies. That’s more than all non-U.S. markets combined ($2.4 billion). Texas ($865M), New York ($667M), and Colorado ($295M) round out the top states.

The concentration creates vendor risk. Regulatory fragmentation between the U.S. and EU markets. Geopolitical tensions affecting Israeli companies. Single-region dependency in security infrastructure. These are fundamental considerations for enterprise security architects.

ServiceNow’s acquisitions signal large-scale consolidation

ServiceNow’s 2025 acquisition spree warrants its own analysis. Armis brings cyber-physical security and OT/IoT visibility. Moveworks adds agentic AI capabilities. Veza delivers identity security for the AI era. The company calls it an “AI control tower.” A unified security stack that sees, decides, and acts across the entire technology footprint.

The driver: ServiceNow’s Security and Risk business crossed $1 billion in annual contract value in Q3 2025. They expect Armis alone to triple their market opportunity. When a platform vendor invests $11.6 billion in its own security workflows, point solutions become acquisition targets or competitors.

What this means for 2026

For security leaders: Map your vendor portfolio against both funding momentum and M&A activity. Startups with strong backing will survive consolidation. Others won’t. Audit your AI deployment pipeline against available protections. The gap between AI adoption and AI security is widening. Accelerate zero-trust adoption while solutions mature.

For AI builders: Security isn’t a feature to add later. The $414 million flowing into AI/LLM security represents smart money recognizing that unprotected AI systems are enterprise liabilities. Build with guardrails or build vulnerabilities.

Analysis based on Crunchbase data covering 175 AI security startups that raised Series A, B, or C funding between January 2024 and December 2025. ServiceNow acquisition data from the company’s press releases dated December 2025.

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.