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How AI changes the cybersecurity equation
AI is not simply another tool for security teams. Its adoption changes both what defenders must protect and how they can defend it. NIST says widespread AI use can affect cybersecurity and privacy risks, while cybersecurity requirements can also shape how AI is adopted. It identifies a need for practices that manage those risks, support responsible AI use in cyber defense, and adapt defenses to AI-enabled offensive techniques. NIST’s Cybersecurity, Privacy, and AI program describes this two-way relationship.
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The resulting opportunity is easy to miss when AI is discussed mainly as a productivity tool or a new software market. Every new model, AI-enabled application, and automated workflow can add sensitive data flows, access paths, dependencies, or failure modes that a security program must understand. At the same time, defenders may apply AI and automation to detection, investigation, and response. These are related but distinct sources of demand.
Why AI systems need their own security attention
Traditional security controls remain essential, but AI systems also introduce risks that may not be fully covered by existing guidance. NIST’s adversarial machine-learning taxonomy discusses attack classes such as evasion, model extraction, and membership inference, as well as availability risks and the broader attack surface of AI systems. These are categories of risk—not a claim that every AI deployment is exposed to every attack. NIST’s AI 100-2 E2025 publication provides the taxonomy.
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In practice, organizations need to know which people and systems can access models, what data an AI application can retrieve or disclose, how third-party components affect exposure, and whether use is governed consistently. The risk is not limited to a model being attacked directly: weak oversight or access controls around an AI application can expose data or enable misuse.
IBM’s 2025 report offers one indicator of that concern. In IBM’s researched population, 13% of organizations reported breaches of AI models or applications; 97% of that group 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. IBM said the breached group was a small representation of the researched population and that this was its first study of AI security, governance, and access controls, so these figures should not be treated as a universal prevalence estimate. IBM’s July 2025 report details the findings.
AI also changes the threat environment
AI can help attackers work faster or scale techniques, as well as help defenders. 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 count reflects Microsoft’s threat telemetry and stated observation period, not a global census. Microsoft’s 2026 report provides its account of the threat trends.
The implication is not that AI makes every attack more dangerous or inevitable. Rather, organizations have to account for how automation can alter the speed and scale of both attacks and response—and ensure that their controls, monitoring, and escalation processes keep pace.
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Two different sources of cybersecurity demand
Securing AI systems and use
Organizations need controls that address AI application vulnerabilities, govern deployments, manage or restrict AI use, and secure the gateways through which AI services are accessed. They also need to account for data, identity, and supply-chain exposure. These needs can lead to specialized AI security products, but they also depend on foundational controls such as identity management, data protection, and cloud security.
Using AI and automation in security operations
Security teams may use AI-assisted tools to identify suspicious activity, investigate alerts, and support response. IBM reported that organizations extensively using AI and automation in security operations had, on average, $1.9 million lower breach costs and an 80-day shorter breach lifecycle than other organizations in its study. This is an association reported by IBM, not proof that AI alone caused the difference. NIST supports the broader potential of AI for cyber defense, while emphasizing responsible use and risk management.
Together, these drivers explain why cybersecurity can benefit from AI adoption even when a company is not buying a product marketed specifically as “AI security.” Some spending is aimed at protecting AI itself; some is aimed at applying AI to security work.
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What the spending forecasts say—and what they do not
Gartner’s 2026 forecasts point to substantial expected growth, but its two figures below describe different market categories. They are forecasts, not audited realized revenue, and should not be added together or treated as interchangeable.
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| Gartner forecast category | 2026 estimate | 2027 estimate | What it covers |
|---|---|---|---|
| Market for securing AI | $2.835 billion | $4.783 billion | A defined market that includes AI application security, AI usage control, AI governance platforms, AI gateways, and other securing-AI spending. Gartner forecasts a 68.7% increase from 2026 to 2027. |
| Worldwide AI cybersecurity spending | $51.347 billion | $85.997 billion | A broader Gartner category for AI cybersecurity spending, distinct from the market for securing AI. |
The securing-AI figures come from Gartner’s August 26, 2026 forecast. Gartner’s separate September 16, 2026 forecast gives the broader AI cybersecurity spending estimates and forecasts total worldwide AI spending at $2.7 trillion in 2026. The broader number is not a measure of the securing-AI market alone.
Gartner’s release also forecasts 2027 spending of $851 million for AI application security and $749 million for AI usage control. It estimates AI usage control will grow 73% from 2026 to 2027 and AI gateways 70.9% over the same period. These are category forecasts, not guarantees of growth for any particular vendor. Gartner Senior Principal Analyst Shailendra Upadhyay said the surge reflects the need to secure AI systems, address emerging vulnerabilities, and strengthen defenses; that is an analyst’s explanation in Gartner’s release, not independently verified causal analysis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare AI security solutions
Gartner names four categories within the market for securing AI: AI application security, AI usage control, AI governance platforms, and AI gateways. The categories address different needs, so a buyer should begin with the risk and assets in scope rather than with an “AI security” label.
| Category | Primary question to ask |
|---|---|
| AI application security | How does the product identify or reduce vulnerabilities in AI applications? |
| AI usage control | Can it identify and enforce policy around who uses AI services and how? |
| AI governance platforms | Does it help track deployments, policies, oversight, and governance obligations? |
| AI gateways | What controls, visibility, or policy enforcement does it provide as AI requests are routed? |
Before choosing a product, assess the following in the organization’s own environment:
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- Dual USB-A and USB-C Security Key – Features both USB-A and USB-C connectors for seamless compatibility across desktops, laptops, and tablets. Supports plug-and-stay use or keychain carry.
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- FIDO Certified for Strong Authentication – [CHECK COMPATIBILITY before purchase] Fully compliant with FIDO2 and FIDO U2F standards. Works with major platforms like Google, Microsoft, GitHub, and Dropbox.
- Passwordless Login with PinPlex – Supports secure passkey login via WebAuthn and CTAP2 with added protection from PinPlex, a complex PIN system that enhances physical security.
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- Coverage: Which specific risk is addressed, and which AI models, applications, users, and data sources are visible?
- Identity and access: Can the tool enforce appropriate permissions, or does it only report activity?
- Integration: How does it work with existing identity, endpoint, cloud, data-protection, and security-operations systems?
- Performance evidence: What evidence is available for detection quality, response time, false positives, resilience, and operational cost under relevant conditions?
- Operating burden: What monitoring, human review, data handling, governance work, and supply-chain exposure does deployment add?
Gartner says organizations should combine existing security systems with specialized AI security tools. A specialist product can address a gap, but it does not replace a complete security program or foundational safeguards.
Why the opportunity remains easy to overlook
Cybersecurity’s role is less visible than AI applications that directly create content or automate business tasks. Yet adoption can make security both a prerequisite for responsible deployment and a place where organizations apply AI to existing operational pressure. The forecasts suggest that buyers are expected to spend more in these areas, while the risk evidence shows why governance and access controls matter.
The key qualification is that market growth does not reveal which providers will win. An organization evaluating vendors still needs to verify coverage, fit, controls, and performance evidence for its use case. IBM’s reported operational association is encouraging, but it does not establish that AI alone produces better breach outcomes. The strongest case for cybersecurity as an AI beneficiary is therefore strategic: adoption creates new protection requirements and new defensive options at the same time.
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