AI’s expansion is creating a security requirement as well as a security tool: organizations must protect the models, applications, data, identities and workflows they are adopting, while also defending against attackers using AI. That makes cybersecurity a consequential, but less obvious, beneficiary of AI investment. It does not prove that every security company will gain or that cybersecurity is objectively the “most” overlooked beneficiary.
Why AI adoption creates cybersecurity demand
AI changes both sides of the security equation. Organizations can use AI and automation to assist detection, investigation and response, but deploying AI also creates systems and data flows that need protection. NIST says broad AI adoption can affect cybersecurity and privacy risks, and that cybersecurity considerations can in turn shape AI adoption. Its program focuses on tools and practices for managing those risks, using AI responsibly for cyber defense, and adapting defenses to AI-enabled offensive techniques. NIST’s Cybersecurity, Privacy, and AI program describes this two-way relationship.
The result is a second-order demand: as companies roll out AI, they need controls for how it is accessed and used, how its applications are secured, and how deployments are governed. At the same time, security teams may seek AI-enabled tools to handle cyber work more effectively. Neither need makes a particular product, provider or investment a guaranteed winner.
What risks need to be secured?
AI systems can face familiar risks, such as unauthorized access or exposure of sensitive data, as well as attacks that target model behavior or the information a model reveals. NIST’s adversarial machine-learning taxonomy discusses risk classes including evasion, model extraction, membership inference and attacks on availability. NIST also notes that existing frameworks and guidance do not comprehensively cover the complex attack surface and security abuses associated with AI systems. These are categories organizations may need to assess—not a claim that every AI deployment is exposed to every attack. NIST AI 100-2 E2025 sets out the taxonomy and terminology.
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Access and oversight are practical concerns. In IBM’s 2025 study, 13% of organizations in the researched population reported breaches of AI models or applications. Of those reporting such breaches, 97% said they lacked AI access controls; IBM also reported that 60% of AI-related security incidents led to compromised data and 31% to operational disruption. These are IBM-reported findings from its study, which IBM described as its first on AI security, governance and access controls; the group reporting breaches was a small representation of the researched population. IBM’s report release provides its scope and findings.
Attackers’ use of AI adds pressure, too. Microsoft’s 2026 Digital Defense Report says AI is compressing attack timelines, lowering the cost of sophisticated capabilities, and enabling greater speed, scale and autonomy. Microsoft also reported detecting at least 46 million business-contact impersonation attacks over the preceding 12 months. That figure is Microsoft threat telemetry for its stated observation period, not a census of all such attacks worldwide. Microsoft’s 2026 report provides its account of these trends.
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How large could the market opportunity be?
Gartner’s forecasts point to growth, but distinguish between a market specifically for securing AI and a broader category it calls AI cybersecurity spending. The estimates use different market labels and should not be added together or treated as interchangeable.
| Gartner forecast | 2026 estimate | 2027 estimate | What it covers |
|---|---|---|---|
| Market for securing AI | $2.835 billion | $4.783 billion | Includes AI application security, AI usage control, AI governance platforms, AI gateways and other securing-AI spending. Gartner forecast a 68.7% increase from 2026 to 2027. |
| Worldwide AI cybersecurity spending | $51.347 billion | $85.997 billion | A broader Gartner category, separate from the market for securing AI. |
Both rows are Gartner forecast values, not audited realized revenue. The estimates come from separate releases: Gartner’s August 2026 securing-AI forecast and its September 2026 AI cybersecurity spending forecast. Gartner’s September release also forecast total worldwide AI spending at $2.7 trillion in 2026; that figure is broader still and is not a measure of cybersecurity spending alone.
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What kinds of security solutions may be needed?
Gartner identifies four categories within its securing-AI market. They address different problems, so organizations should match a solution to the risk and systems they need to protect rather than treat the labels as interchangeable.
- AI application security: addresses security risks in AI applications. Gartner forecast this segment at $851 million in 2027.
- AI usage control: helps organizations control how AI tools and services are used. Gartner forecast $749 million in 2027 and 73% growth from 2026 to 2027.
- AI governance platforms: support oversight and governance of AI deployments.
- AI gateways: provide controls around AI access and interactions; Gartner forecast this segment to grow 70.9% from 2026 to 2027.
The segment values and growth rates are Gartner estimates, not confirmed sales. Gartner’s August release says organizations need to combine existing security systems with specialized AI security tools; specialized products do not replace foundational controls or a complete security program.
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Can AI improve cybersecurity operations?
AI and automation may help defenders work through security operations, but outcomes depend on implementation and the surrounding security program. In IBM’s 2025 report, organizations extensively using AI and automation in security operations were associated with an average $1.9 million lower breach cost and an 80-day shorter breach lifecycle. This is an association reported by IBM, not evidence that AI alone caused the differences. NIST likewise identifies responsible use of AI for cyber defense as an area for standards, tools and practices—not an automatic performance gain.
How should organizations evaluate the opportunity?
For a buyer, the useful question is not whether a product is “AI security,” but which risk it addresses and whether it fits the organization’s controls and operations. Compare options against:
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- Visibility and enforcement: Which assets and data can the product see, and can it apply appropriate identity and access controls?
- Integration: How does it work with existing identity, endpoint, cloud, data and security-operations systems?
- Evidence in context: What evidence supports detection quality, response time, false-positive rates, resilience and operational cost in the buyer’s own environment?
- Operational burden: What monitoring, human review, data handling and supply-chain exposure will deployment require?
For anyone considering the theme as an investment idea, the distinction is just as important: forecasts of market growth describe expected category spending, not the revenue, profitability or share-price performance of any individual company. The evidence establishes a growing security need and forecast demand, not which vendors will capture it.
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