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How does AI affect cybersecurity?
AI affects cybersecurity in two connected ways. Defenders can apply AI to threat detection, prevention, and vulnerability-related work. At the same time, attackers can target AI systems or misuse generative AI. The technology is neither an automatic security upgrade nor a replacement for established security practices; its value depends on the system, the task, and the controls around it.
CISA’s 2023–2024 roadmap describes the agency’s use of AI tools in threat detection, prevention, and vulnerability-related work. These are areas of application, not evidence of a quantified improvement in detection or response. Organizations should evaluate AI against their own needs and operating conditions rather than assume it will outperform existing methods.
The security boundary is also wider than the model itself. An AI-enabled service may depend on training or input data, externally hosted models, software packages, interfaces, deployment infrastructure, and the people or processes that act on its output. Each part can affect the reliability and security of the whole system.
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What are the security risks of AI?
NIST’s final 2025 edition of Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (AI 100-2 E2025) distinguishes several attack classes. Its taxonomy covers evasion, poisoning, and privacy attacks for predictive AI, and those classes plus misuse for generative AI. The terms describe different failure paths, so assessing one does not establish that a system is protected against the others.
| Attack class | What it means in practice | Where NIST includes it |
|---|---|---|
| Evasion | An attacker crafts or alters inputs to make a model produce an incorrect or otherwise desired output at use time. | Predictive AI and generative AI |
| Poisoning | An attacker manipulates data or another part of the learning process so the model’s behavior is compromised. | Predictive AI and generative AI |
| Privacy attacks | An attacker seeks to learn sensitive information about data used by, or represented in, a model. | Predictive AI and generative AI |
| Misuse | Generative AI is used to support harmful activity, rather than only being attacked to change the model’s own behavior. | Generative AI |
These categories are a vocabulary for threat analysis, not a checklist that guarantees complete coverage. NIST discusses multiple learning approaches, data modalities, possible mitigations, and the limitations of existing techniques. Its guidance is voluntary technical guidance, not a certification or assurance that a system is secure.
Supply-chain and infrastructure exposure
Risk can enter through the components and services an AI system depends on. ENISA’s 2025 threat landscape, version 1.2, reports targeting of the AI supply chain, including poisoned hosted machine-learning models and malicious packages, and describes vulnerabilities in AI-related infrastructure. These examples show why defenders should assess model and package provenance, trusted sources, deployment security, and the dependencies behind a service. They are threat examples, not prevalence estimates.
Why mitigations cannot eliminate the risk
In a January 4, 2024 news item, NIST said: “Adversaries can deliberately confuse or even ‘poison’ artificial intelligence (AI) systems to make them malfunction — and there’s no foolproof defense that their developers can employ.” That caution matters in planning: safeguards can reduce exposure and improve detection or recovery, but no single control should be treated as a guarantee against adversarial attacks.
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How can organizations secure AI systems?
Use a lifecycle-based assessment rather than treating model security as a standalone task. NIST’s taxonomy helps teams name threats and consider mitigations; the practical work is to connect those threats to the system’s purpose, components, and operating context.
- Define the system and its stakes. Record what the AI system does, who uses it, which decisions or services depend on its output, and what harm could follow from incorrect, manipulated, or unavailable results.
- Map the attack surface. Include training and input data, model behavior, interfaces, connected tools, software and hosted-model dependencies, deployment infrastructure, and the human workflows that consume outputs.
- Match safeguards to attack classes. Consider how the system could be evaded, poisoned, exposed to privacy attacks, or misused. Choose controls that address the relevant paths, rather than assuming a general AI safeguard covers all of them.
- Protect provenance and deployment. Track where models, data, and packages come from; use trusted sources; manage access to components and interfaces; and secure the infrastructure on which the system runs.
- Test and monitor in context. Assess behavior using realistic inputs and workflows, watch for unexpected changes, and revisit controls as the model, dependencies, users, or threat conditions change.
- Prepare for incidents. Define how teams will investigate suspicious inputs or outputs, contain affected components, restore trusted versions, and communicate with the people responsible for dependent services.
The right implementation varies with the system and its consequences. For example, a model whose output informs a high-impact decision may need a human review or a safe fallback when confidence or system integrity is in doubt. A public-facing AI interface may call for careful control of access and connected tools. These are design choices to evaluate against specific threats, not universal guarantees.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can AI help defend against cyberattacks?
AI can support defensive work such as identifying patterns in security data, helping prioritize detection, and assisting vulnerability-related analysis. CISA’s roadmap documents agency use in these areas, but does not establish a general performance gain that every organization can expect. Teams should define the task, measure whether the system helps in their environment, and retain accountable human decision-making where the consequences warrant it.
AI-assisted detection should fit into a broader response process: analysts need enough context to assess an alert, a way to validate findings, and a defined path to contain or investigate a real incident. A model output is an input to that process, not proof that an attack occurred or that a system is safe.
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Why do technical controls and collaboration both matter?
Technical risk management and operational coordination address different problems. NIST provides terminology and mitigation guidance for understanding attacks against AI systems. CISA’s Joint Cyber Defense Collaborative (JCDC) AI Cybersecurity Collaboration Playbook and fact sheet, announced January 14, 2025, address operational collaboration among government, industry, and international partners.
For organizations, collaboration can help coordinate planning, defense, and response information across relevant partners. The announcement does not mean participation is universal or that reporting is mandatory. Each organization still needs its own incident roles, escalation routes, and decisions about what information it can share.
