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AI is changing how some malicious activity can be prepared or carried out, but current evidence does not show that AI has replaced conventional cyberattacks or established a new global set of security rules. It is better understood as both an aid attackers may use and an added attack surface organizations must secure. For defenders, that means addressing AI-specific risks while continuing to protect against phishing, ransomware, exploited vulnerabilities, and supply-chain weaknesses.
What “AI cyberattack” can mean
The phrase covers two different problems. In one, an attacker uses AI to assist a malicious operation—for example, to help prepare deceptive messages or support activity across stages of an intrusion. In the other, an attacker targets an AI system itself, using weaknesses in its data, model, inputs, or deployment. The distinction matters: controls for stopping a fraudulent email are not the same as controls for protecting a machine-learning system from manipulated inputs.
AI used to assist an attack
AI can help malicious groups facilitate or enhance activity. That does not mean every attack is automated, that an AI system acts independently from start to finish, or that AI is necessary for familiar tactics such as phishing and credential theft. NIST’s initial preliminary draft on cybersecurity risks involving AI describes possible use across attack stages, including vulnerability discovery, accelerating attack paths, and assisting with data exfiltration or tampering. Because it is a preliminary draft, it should be read as an emerging risk analysis, not a finalized standard or proof that every capability is routinely used in real incidents. NIST IR 8596, initial preliminary draft (December 2025).
AI systems as targets
AI systems integrated into business environments can extend an organization’s attack surface. NIST’s adversarial machine-learning taxonomy describes attack categories including evasion, poisoning, privacy attacks, and misuse; the relevant categories differ between predictive and generative AI systems. A system may therefore need protection not only for its infrastructure and accounts, but also for the data and model-related processes on which its outputs depend. NIST AI 100-2 E2025.
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Are AI cyberattacks actually increasing?
There is evidence that malicious groups are increasingly using AI to facilitate or enhance activities, and that organizations’ adoption of AI creates additional exposure. But the available figures do not establish what percentage of cyberattacks are caused by AI. Nor should broad cyber-incident statistics be presented as AI-attack rates.
ENISA’s Threat Landscape 2026 analyzes incidents and events observed in the EU from 1 January through 31 December 2025. It describes a wider threat environment in which ransomware remained the most short-term impactful incident type, alongside phishing and social engineering, vulnerability exploitation, supply-chain exposure, and ideology-driven distributed denial-of-service (DDoS) activity. The report’s figures are about EU incidents and targeting—not a global count of AI-caused attacks. ENISA Threat Landscape 2026, published 22 September 2026.
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- 73% of targeted organizations were essential or important entities under NIS2.
- 32% of reported targeting was directed at public administration.
- More than 48,000 new CVE identifiers were published in 2025, a 22% increase over the prior year.
- 82% of recorded public-administration events were ideology-driven DDoS.
Each figure above reflects ENISA’s EU 2025 threat-landscape scope; none measures the share of attacks caused by AI. The figures point to pressure on organizations and dependencies, not to AI as the sole or dominant cause. ENISA Executive Director Juhan Lepassaar said the report highlights how threats become more interconnected and how groups can spread impact across digital services and infrastructure.
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What is changing—and what is not
The practical change is that security teams must consider how AI affects both attacker capability and their own systems. AI may make some activity faster or easier to scale, and it can create new failure modes when integrated into business processes. That changes risk assessment and operational practice.
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It does not, on the evidence cited here, amount to a new worldwide legal rule. “Security rules” might mean day-to-day controls, voluntary frameworks, technical standards, or binding law; these are not interchangeable. The NIST materials discussed below provide voluntary guidance or, in one case, an early draft or concept note. They should not be described as binding regulations or finalized requirements.
How organizations can respond
Defenses should address common intrusion routes as well as AI-specific exposure. The measures below are risk-reduction practices, not guarantees that an organization will prevent every incident. NIST’s December 2025 preliminary draft specifically discusses training, email security, authentication, and integrated defenses; ENISA’s threat landscape underscores the continuing relevance of vulnerabilities and dependencies.
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Maintain systems and dependencies
- Keep operating systems, applications, and exposed services maintained, and prioritize remediation of vulnerabilities according to exposure and organizational risk.
- Track important third-party and software dependencies. Review what services and data could be affected if a supplier or component is compromised.
- Include AI services and integrations in asset inventories and security reviews rather than treating them as outside the organization’s technology environment.
Make impersonation harder to exploit
- Strengthen email security and authentication, especially for accounts that can approve payments, reset credentials, or access sensitive systems.
- Train personnel to verify unusual payment, credential, or data requests through a trusted channel instead of relying on the apparent sender or the urgency of a message.
- Use clear escalation and reporting paths so staff can flag suspicious messages or requests quickly.
Govern AI systems through deployment and use
- Identify where AI is used, what information it can access, and which people or systems can act on its outputs.
- Assess risks relevant to the system type and use case, including manipulated inputs, unsafe data handling, privacy exposure, or inappropriate use.
- Define who is responsible for reviewing system changes, monitoring operation, and responding when an AI-enabled process behaves unexpectedly.
Which NIST guidance applies?
The NIST documents have different purposes and maturity levels. The table distinguishes the final adversarial-ML taxonomy from voluntary risk-management guidance and from material that remains preliminary or conceptual.
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|---|---|---|---|
| NIST AI 100-2 E2025 | Adversarial machine-learning attacks on AI systems; relevant to AI developers and deployers. | Final report, published March 2025; voluntary guidance. | Taxonomy and terminology for attack types, including evasion, poisoning, privacy, and misuse attacks. |
| NIST AI Risk Management Framework | Organization-wide AI risk management, rather than only adversarial attacks. | Voluntary framework; NIST says AI RMF 1.0 is being revised. | A risk-management approach for addressing AI risks across development and use. |
| NIST IR 8596 | Cybersecurity risks and defense considerations involving AI; useful to security practitioners. | Initial preliminary draft, December 2025. | Discusses possible AI use across attack stages and defense concepts; it is not a finalized standard. |
| Critical-infrastructure AI profile concept note | AI risk management for critical-infrastructure contexts. | Concept note released 7 April 2026. | Signals profile development work; it is not a finalized requirement. |
These resources can inform security practice, but their status and scope differ. Organizations should not mistake a taxonomy for a complete security program, or a voluntary framework, preliminary draft, or concept note for binding law.
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