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Yes, AI is changing cybersecurity, but the best-supported picture is acceleration, not a wholesale reinvention. Attackers and defenders can use AI to do familiar work faster, while AI-enabled systems add new security risks. That is a meaningful shift, but it does not establish that cyberattacks are now routinely autonomous or that cybersecurity has become unrecognizable.
How is AI changing cybersecurity so far?
The most immediate change is that AI can help people perform parts of an existing attack chain more efficiently. The UK National Cyber Security Centre (NCSC) says threat actors already use AI to support reconnaissance, vulnerability research and exploit development, social engineering, basic malware generation, and analysis of stolen information. Its assessment expects AI to increase the frequency and impact of attacks mainly by improving existing techniques, rather than by making wholly new kinds of attack the norm. The NCSC’s assessment through 2027 is a forecast, not a claim that every group has these capabilities.
This makes the comparison with AI-assisted software development useful but limited. In both fields, AI can speed up work involving code and analysis. In cybersecurity, however, that speed has consequences on both sides: it can help someone find and exploit a weakness, and it can help a defender find or fix one. The analogy does not show that the two fields are changing at the same rate or to the same degree.
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What changes the balance between attackers and defenders?
Vulnerabilities leave defenders less time to respond
The NCSC identifies vulnerability research and exploit development as a particularly significant pressure point. It says the time between disclosure of a vulnerability and its exploitation has already narrowed to days, and assesses that AI is likely to shorten it further. Known vulnerabilities that remain unpatched are a major target, so the practical risk is not limited to an attacker inventing a new technique: familiar weaknesses may be found and exploited faster.
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The agency also warns that well-resourced, highly capable actors are better placed to take advantage of advanced AI capabilities. Other groups may instead use or adapt commercial and open-source models. The NCSC flags potential risks to critical national infrastructure and its supply chains, especially operational technology with lower security levels.
AI can help with defense, but it does not replace security work
Google Threat Intelligence Group (GTIG) reports using AI agents to identify software vulnerabilities and reasoning systems to help fix them. That is an example of a defensive use reported by the organization, not evidence that AI tools will reliably secure every company. GTIG’s May 12, 2026 report also describes adversary uses, including accelerated development of infrastructure and malware, and a zero-day exploit the group believes was developed with AI. These are GTIG’s observations; they do not establish how prevalent such activity is across all attackers.
The National Academies’ 2026 rapid expert consultation likewise describes generative and agentic AI as expanding capabilities for both attackers and defenders, rather than pointing to a one-sided outcome. Its consultation considers near-term risks as well as longer-term opportunities and policy options.
What new risks come from putting AI into systems?
AI is not only a tool an attacker might use; it can also become part of an organization’s own attack surface. When an AI system is connected to company data, operational technology, or other services, weaknesses in the model’s surrounding software, access controls, or dependencies may create routes into wider systems.
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The NCSC identifies direct and indirect prompt injection, software vulnerabilities, and supply-chain attacks as possible ways to exploit AI systems. It warns that hurried deployment can put release schedules ahead of security, and that weak encryption, poor identity management, or collecting more user data than necessary can compound risk. The relevant issue is not that every AI integration is unsafe, but that each connection and dependency needs to be treated as part of the system’s security design.
What does the evidence say about adoption and readiness?
SANS Institute’s July 2026 survey collected responses from 536 security practitioners and 57 senior leaders globally. Its figures describe what respondents reported, not independently measured global incidence or a universal picture of cybersecurity teams.
| SANS 2026 survey finding | What respondents reported |
|---|---|
| Organizational use | 78% of organizations actively use AI in cybersecurity; 27% of practitioners described deployments as mature production. |
| Reported exposure and perception | 78% of organizations reported confirmed or suspected AI-enabled attacks in the past year, and 95% of respondents believed threat actors were already using AI. |
| Detection and response | 63% of practitioners reported significant shortcomings in AI threat detection and response, compared with 45% in 2025. |
| Training and red-team work | 73% said AI had changed their team’s training requirements, compared with 51% in 2025; 61% said they use AI in red-team work, compared with 33% in 2025. |
| Formal AI risk programs | 50% of senior leaders said their organization had a formal AI risk program, compared with 36% of practitioners. |
SANS’s 2026 survey suggests a gap between adoption and confidence in operational maturity: use is widely reported, while many practitioners still see shortcomings in detection and response. The difference between leaders’ and practitioners’ answers about formal risk programs also points to uneven visibility inside organizations.
How should organizations respond?
The practical response is to strengthen the processes AI can put under pressure, while applying security controls to AI tools and systems themselves. These measures reduce exposure; no single one guarantees protection from AI-related threats.
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- Prioritize patching and vulnerability response. Track disclosed vulnerabilities, identify affected systems, and shorten the time from finding an exposure to applying and verifying a fix.
- Secure the software lifecycle, including AI components. NIST’s final SP 800-218A, published in July 2024, supplements its Secure Software Development Framework with practices for generative AI and dual-use foundation models. It is intended for model producers, producers of systems that use models, and acquirers, and should be used with SP 800-218.
- Review generated code rather than trusting it by default. eu-LISA’s July 9, 2026 report says generative AI tools may support productivity in software engineering, but calls for ongoing evaluation and monitoring of tools and enough resources to review AI-generated code. The report examines productivity, code quality, and security.
- Assess integrations and dependencies. Know what data an AI tool can access, which services it can call, how identities and permissions are managed, and which software or suppliers it depends on. Limit access to what is required and account for prompt-injection and supply-chain risks.
- Train teams for changed workflows. The SANS survey’s reported changes in training requirements make workforce readiness part of the security response, not an afterthought to tool adoption.
Will AI make cyberattacks autonomous?
Not according to the NCSC’s near-term assessment. It judges fully automated, end-to-end advanced cyberattacks unlikely by 2027. Skilled people are expected to remain involved, while automating selected tasks such as vulnerability identification and exploitation, or changing malware and infrastructure to evade detection. The distinction matters: automation of pieces of an operation can raise its speed and scale without removing human operators from the process.
The NCSC also warns that AI’s application to cyber operations is changing quickly and that technical surprises are likely. Its forecast should therefore be read as a time-bounded assessment, not a guarantee about what will happen after 2027. The evidence available now supports a clear conclusion: cybersecurity is changing, but the most defensible account is faster, more scalable versions of familiar work alongside new risks created by AI integration—not the disappearance of human expertise or the end of conventional security practice.
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