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AI can help find software vulnerabilities and may make cyberattacks faster or easier to scale, but available evidence does not show that an AI system has independently carried out an end-to-end attack on critical infrastructure. Nor does it prove that nobody is prepared. The evidence does show significant gaps in how U.S. agencies assessed AI-related risks, alongside practical security measures operators can strengthen now.
What can AI do in a cyberattack?
AI is a dual-use technology in cybersecurity. Tools that help defenders discover software flaws can also help malicious actors identify and exploit attack paths. The International AI Safety Report 2026 describes this offense-defense tension: vulnerability-finding capabilities may support security work while also enabling offensive activity.
That is a reason for concern about the speed, scale, and accessibility of cyber operations—not proof that an AI can independently compromise a power grid, water system, or other connected service. It is important to distinguish AI assistance from increasingly autonomous task execution, and both from an AI independently completing an entire attack chain.
| Evidence level | What it means | What the cited evidence establishes |
|---|---|---|
| AI-assisted activity | A person directs an AI to support a task, such as analyzing information or helping identify a vulnerability. | The International AI Safety Report 2026 describes the dual-use potential of vulnerability-finding capabilities. |
| Supervised agentic activity | An AI system performs bounded tasks under human oversight and controls. | Joint government guidance addresses how to constrain and supervise agentic systems; this is guidance for risk management, not evidence of an infrastructure attack. |
| Autonomous end-to-end attack | An AI independently carries out the full sequence needed to compromise and affect an operational service. | The cited GAO, International AI Safety Report, and UK AI Security Institute materials do not establish a verified real-world incident of this kind against critical infrastructure. |
The UK AI Security Institute says it has conducted frontier-system evaluations since November 2023 in domains relevant to national security and public safety. Those evaluations can inform understanding of capability trends, but the available material does not demonstrate an independently completed attack on critical infrastructure.
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What do we know about infrastructure preparedness?
The strongest concrete finding in the cited U.S. evidence is not that all operators are unprepared. It is that federal assessments had substantial gaps. In 2025, the U.S. Government Accountability Office (GAO) reviewed 17 assessments by nine sector risk management agencies, covering 16 critical-infrastructure sectors and one subsector.
- All 17 assessments identified AI use cases.
- Sixteen of the 17 only partially identified potential AI-related risks.
- In 16 of the 17, the likelihood of those risks occurring was not identified.
- None fully evaluated risk by considering both likelihood and impact.
GAO examined whether assessments documented their methodology; identified AI uses, threats, and vulnerabilities; addressed likelihood and impact; evaluated risk levels; identified mitigations; and mapped mitigations to risks. A risk inventory that omits likelihood or does not connect controls to identified risks can leave decision-makers without a clear basis for prioritizing work.
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GAO also recognized the potential upside: AI may improve essential services and cybersecurity. It cautioned that adoption may increase vulnerability in systems supporting essential functions, including water supply, electricity generation, and food production. GAO recommended that the Department of Homeland Security update its risk-assessment guidance and template. The report page listed March 31, 2027, as DHS’s estimated completion date for that recommendation, with status information current through July 2026.
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Does the evidence show that “nobody’s ready”?
No. A 2025 World Economic Forum survey, as reported in the International AI Safety Report 2026, asked about confidence that respondents’ countries were prepared for major cyber incidents targeting critical infrastructure. It included 409 respondents across 57 countries. That is a measure of perceptions among survey respondents—not an objective audit of infrastructure security, and not evidence that every country or operator is unprepared.
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Preparedness varies by organization, sector, system, and threat. The GAO findings expose shortcomings in federal agencies’ AI-risk assessments; they do not measure every operator’s controls or recovery capability. The survey captures confidence, not tested defensive performance. Those findings justify scrutiny and improvement, but neither supports the absolute claim that nobody is ready.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should infrastructure operators do now?
Current government guidance emphasizes established security practices and controlled AI adoption. The Australian Cyber Security Centre (ACSC) advises organizations to verify that core controls work in practice, rather than treating written policies as proof of protection.
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Verify foundational controls
- Patch systems: Apply security updates promptly, with operational testing and change controls appropriate to the system.
- Segment networks: Separate networks and restrict pathways between business IT, operational technology, and other sensitive environments to limit the effect of a compromise.
- Use least privilege: Give users, services, and tools only the access needed for their tasks; avoid broad or unrestricted permissions.
- Secure configurations: Establish and check secure configuration baselines instead of relying on default settings.
- Analyze logs: Centralize security logs and review them promptly so suspicious activity can be detected and investigated.
The ACSC says automated triage and response can be appropriate when actions are pre-approved, narrowly scoped, and reversible. That boundary matters in environments where a mistaken automated action could disrupt a service or affect physical operations.
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Joint guidance from the Australian Signals Directorate’s Australian Cyber Security Centre, CISA, NSA, the Canadian Centre for Cyber Security, New Zealand’s NCSC, and the UK NCSC recommends a security-first approach across design, deployment, and operation. Its advice is to begin with low-risk, well-defined tasks; maintain strong oversight, continuous monitoring, and human control; and conduct ongoing risk assessment and threat modelling.
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- Do not give an AI agent broad or unrestricted access, particularly to sensitive data or critical systems.
- Define permitted tasks, access boundaries, and escalation points before deployment.
- Keep a human responsible for consequential decisions and actions.
- Monitor the system continuously and reassess its risks as its use or environment changes.
For AI-assisted security work specifically, the ACSC recommends secure, controlled, human-supervised use to help analyze logs from internet-facing services and perform penetration tests or vulnerability assessments of those services. This is different from granting an AI autonomous control over infrastructure.
Assess risk as a complete chain
A useful assessment should connect plausible threats and vulnerabilities to both potential impact and likelihood, then map mitigations to the risks they address. Operators should also account for the boundary between information technology and operational technology: exposure of an office network is not automatically the same as compromise of a physical process, but connections between environments can create paths that need to be understood and controlled.
NIST’s AI Risk Management Framework is voluntary and intended to support risk management across AI design, development, use, and evaluation. NIST says AI RMF 1.0 is being revised; on April 7, 2026, it released a concept note for a profile focused on trustworthy AI in critical infrastructure. That concept note is not a finalized mandatory standard.
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