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AI and machine learning can help with cybersecurity, but their presence is not evidence that a system is safer. Whether they help depends on the security task, the data and deployment, and the controls around the technology. At the same time, AI systems and their supply chains create risks of their own, while attackers can use AI tools to support their operations. A sound assessment asks both whether AI improves a defined defensive process and how the AI-enabled system could be attacked.
How is AI used in cybersecurity?
AI and machine learning (ML) can be applied to defensive work, but the useful question is not whether a product has an AI feature. It is whether that feature improves a specific security outcome compared with the process or tool already in place. For example, an organisation might assess a feature intended to help sort alerts or review suspicious activity. It should define what improvement would count, what baseline it is measured against, and what evidence supports the claim.
NIST says AI technologies have the potential to transform cybersecurity: they may give defenders new tools, while also enhancing the capabilities of people seeking to target organisations and individuals. That is a statement about potential, not proof that a particular product reduces risk. NIST characterizes AI security and resilience as an active research area, with challenges and possible solutions changing rapidly.
What a useful claim needs to show
- A defined task: Name the security problem and the outcome the feature is meant to improve.
- A meaningful baseline: Compare with the existing workflow or tool, not an undefined “manual” alternative.
- Relevant evidence: Look for the test conditions, data, time period, and results, and whether they can be independently checked.
- Operational fit: Establish how people review outputs, handle errors, monitor performance, and respond when the feature is unavailable or wrong.
A feature’s existence, a demonstration, or a broad vendor claim does not establish effectiveness in an organisation’s own environment. Conversely, limits in general guidance do not prove that every deployed AI security feature is ineffective. The evidence has to be judged against the particular use case.
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What are the risks of AI in cybersecurity?
There are two related but distinct questions: how defenders might use AI, and how to secure the AI systems that are part of a product or workflow. The second question matters whenever models, data, connected services, or automated decisions become part of the security environment.
NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, a voluntary guidance report dated March 24, 2025 (with a corrected PDF noted April 1, 2025), provides a shared vocabulary for attacks and mitigations across machine-learning methods and lifecycle stages. It includes evasion, poisoning, privacy attacks, and misuse. The taxonomy helps teams ask what an attacker might try; it is not a certification or evidence that a particular product is secure.
Attack classes to include in a threat model
- Evasion: Attempts to make a model produce an incorrect result when it processes an input.
- Poisoning: Attempts to influence a model through compromised or manipulated training or other learning data.
- Privacy attacks: Attempts to infer information about data or people associated with a model.
- Misuse: Use of AI capabilities in ways that enable harmful or unauthorized activity.
These labels describe broad classes, not a prediction that every model is vulnerable to every attack. The relevant exposure depends on the system’s design, data, lifecycle, and attacker capabilities. NIST also notes that some mitigation techniques have limitations; a listed control should not be treated as a guarantee.
Think beyond the model
An AI feature sits inside a larger system. Assessment should cover its inputs and outputs, the services it depends on, how it is updated, and the people and processes that act on its results. NIST notes that existing frameworks and guidance do not yet comprehensively address several ML attack classes—including evasion, model extraction, membership inference, and availability attacks—or the complex attack surface of AI systems. This is a reason to examine the surrounding system as well as the model, not a claim that all such systems are compromised.
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Are attackers using AI, or attacking AI systems?
Both are concerns, but public reporting has more often described misuse of AI tools than direct compromise of AI systems. ENISA’s Threat Landscape 2025 (October 2025) reports that threat actors used commercial and diverted or jailbroken large language models (LLMs) to augment operations, including social engineering and development of malicious tools. ENISA also reports AI supply-chain targeting, including poisoned hosted ML models and malicious packages. ENISA says publicly available evidence suggested that misuse of AI tools was more frequent than direct attempts to compromise AI systems; that distinction reflects the evidence it reported, not a guarantee about every threat actor or organisation.
For defenders, these are different problems. Misuse of an AI tool by an attacker can support existing malicious activity. An attack on an AI system or its supply chain targets the technology, its inputs, or dependencies. A risk review should consider both rather than treating “AI threats” as one category.
What do the ENISA generative-AI figures say?
ENISA’s Threat Landscape 2024 (September 2024) reported the following organisational figures. They are dated findings from that report, not universal rates or measurements of all organisations in 2026.
| Reported organisational measure | ENISA figure | Source and date |
|---|---|---|
| Organisations employing generative-AI usage policies | 21% | ENISA, Threat Landscape 2024, September 2024 |
| Organisations mitigating generative-AI cybersecurity risks | 38% | ENISA, Threat Landscape 2024, September 2024 |
| Organisations mitigating generative-AI compliance risks | 28% | ENISA, Threat Landscape 2024, September 2024 |
The measures describe different practices, so the percentages should not be collapsed into a single measure of AI security readiness. They do show that, in the organisations covered by ENISA’s 2024 reporting, policy use and risk mitigation were not universal. They should not be presented as current adoption rates or applied to an organisation without checking its own controls and circumstances.
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How should an organisation evaluate an AI cybersecurity claim?
Use a task-specific review that follows the system from its intended job through its data, threat model, safeguards, and day-to-day operation. The questions below synthesize NIST’s attack taxonomy and lifecycle framing with its discussion of AI security and resilience; they are an evaluation approach, not a quoted NIST checklist.
- Define the outcome and baseline. State the concrete security task, the result the AI feature is expected to improve, and the existing process or tool against which it will be compared.
- Inspect the evidence. Ask what data and conditions were used, over what period, what results were measured, and whether those results can be independently checked. A result without its test context may not transfer to your environment.
- Map data and lifecycle exposure. Identify how training or fine-tuning data, retrieval sources, inference inputs, model updates, and supply-chain components are protected. Include the people and services that can change or provide them.
- Set the threat model. Identify attacker goals and capabilities, then decide which relevant classes—such as evasion, poisoning, privacy attacks, or misuse—are in scope. Record what remains out of scope rather than assuming a general security claim covers it.
- Check mitigations and residual risk. For each identified threat, document the control, what it is intended to reduce, and what failure modes remain. Account for NIST’s warning that mitigation techniques can have limits.
- Plan human oversight and recovery. Specify who reviews outputs, how errors are escalated, what monitoring can reveal a change in performance or abuse, and what fallback or incident-response process applies if the feature fails or is unavailable.
- Review the wider attack surface. Include connected applications, access controls, deployment and update paths, and dependencies—not just the model in isolation.
This process makes an AI-enabled control comparable with alternatives on evidence and risk, rather than on the label “AI.” It also makes uncertainty visible: teams can distinguish tested protections from assumptions and identify where additional controls or oversight are needed.
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