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AI is changing cybersecurity in two connected ways: it can help defenders analyze threats, and it can help attackers scale or adapt their work. At the same time, AI systems themselves need protection. A useful 2026 security strategy therefore has two jobs: secure the models, data, software, and infrastructure behind AI, and adapt cyber defenses to threats that may use AI.
How is AI changing cybersecurity?
“AI cybersecurity” describes both the use of AI in cybersecurity and the protection of AI systems. These are related, but not interchangeable. A security team might use AI to support defensive work while also protecting the AI model, its data, and the systems connected to it.
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NIST describes AI as a potential way to augment defensive capabilities, while warning that organizations must adapt defenses to AI-enabled attacks and protect AI systems and their components. That is a dual-use picture—not evidence that AI will automatically make security better or that it has made every attack more dangerous.
AI used by defenders and attackers
Defenders may use AI to support analysis and other security tasks. Attackers may use AI as an accelerator for offensive activity. The existence of these possibilities does not, by itself, establish how often AI is used in real attacks or how much damage it causes.
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Attacks on AI and attacks enabled by AI
An attack on an AI system targets its data, model, operation, or privacy. An attack enabled or accelerated by AI uses AI as part of an offensive capability against a system or organization. An AI application can face both kinds of risk, alongside familiar cybersecurity threats.
What risks should organizations account for?
AI does not replace the basic security goals of confidentiality, integrity, and availability. Organizations still need to protect information from unauthorized access, prevent unauthorized changes, and keep systems and services available. Machine-learning systems add risks tied to how data and models are trained, queried, and used.
Adversarial machine learning
NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (AI 100-2 E2025) organizes adversarial machine-learning risks by attack methods, lifecycle stages, and attacker goals, capabilities, and knowledge. Its topics include:
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- Data poisoning: compromising training data so a model learns or behaves in an attacker-influenced way.
- Evasion: manipulating an input so a deployed model makes an incorrect or undesirable decision.
- Privacy breaches: attempts to infer sensitive information about data used to train or operate a model. Membership inference is one example: an attacker tries to determine whether particular data was included in a model’s training set.
- Model extraction: attempts to reproduce or infer a model by querying it and studying its outputs.
- Availability attacks: actions that disrupt or impair access to an AI system or service.
These categories describe types of risk, not proof that a particular system has been attacked. NIST’s broader AI security and resilience material also identifies model extraction, membership inference, and availability as areas not comprehensively covered by existing frameworks.
Generative AI and agents
Prompt injection is one possible concern, not a complete map of AI risk. A broader review should consider the confidentiality, integrity, and availability of the system; how the AI is developed and deployed; what data and tools it can access; and how its autonomy and connections to other systems affect risk.
How do you secure AI agents?
AI agents can take actions using connected tools or systems, so their permissions and oversight matter as much as the model’s behavior. Joint guidance announced by CISA and international partners on May 1, 2026, identifies risks including privilege escalation, emergent behavior, and accountability gaps. It recommends managing agentic AI through existing risk-management approaches and applying controls throughout deployment and operation.
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- Limit autonomy and access. Give agents only the permissions and data they need; avoid broad or unrestricted access, especially to sensitive information and critical systems.
- Apply identity controls. Make agent identities and permissions manageable, and use strong identity management for agents and the systems they interact with.
- Keep meaningful oversight. Define where human review or approval is needed, and establish who is accountable for actions taken by an agent.
- Threat-model the full workflow. Consider the agent, its tools, its data, its connections, and the consequences of its actions—not only the model in isolation.
- Monitor and reassess. Continuously monitor activity and conduct regular security assessments as systems, permissions, and uses change.
- Use layered defenses. Do not rely on a single safeguard to prevent misuse or contain an error.
These measures are practical controls, not a guarantee that an agent will behave predictably. The guidance specifically calls attention to emergent behavior and the need to manage autonomy and access.
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NIST AI Risk Management Framework
NIST’s AI RMF 1.0 is a voluntary framework for incorporating trustworthiness into AI design, development, use, and evaluation. Released on January 26, 2023, it is not a universal legal requirement. NIST says the framework is being revised. Its Generative AI Profile, NIST AI 600-1, was released on July 26, 2024. NIST published a concept note for a critical-infrastructure profile on April 7, 2026.
The framework can help an organization structure risk management across an AI system’s lifecycle. It does not replace security engineering, establish that a system is safe, or by itself satisfy every legal or sector-specific obligation.
Secure software development for AI models
NIST SP 800-218A, published in July 2024, augments the Secure Software Development Framework (SSDF) 1.1 with AI-specific practices, tasks, recommendations, and considerations for model development. It is intended for AI model producers, producers of systems that use models, and acquirers. NIST says it should be used with SP 800-218. Its focus is secure development across the software development lifecycle, complementing broader risk-management guidance.
Shared terminology for machine-learning attacks
NIST AI 100-2 E2025 is a final report dated March 24, 2025. Its taxonomy offers shared terms for adversarial machine-learning methods, attack lifecycle stages, and mitigation approaches. NIST’s publication page notes that an error on page x was identified and that potential updates are listed; consult the report’s errata before relying on material affected by that notice.
What is established—and what is not—about AI-enabled attacks?
The official guidance described here identifies risks, attack categories, and defensive practices. It does not establish a named, attributable statistic for the prevalence, financial cost, or measured impact of AI-enabled cyberattacks. That means a percentage or forecast should not be presented as settled evidence on the strength of these sources alone.
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Likewise, a risk framework or taxonomy is not an incident dataset: it helps organizations reason about threats but does not prove that a threat is common. To assess a specific organization’s exposure, pair lifecycle risk management and secure development with threat modeling, access controls, monitoring, and regular security assessments.
Do these recommendations create legal obligations?
The NIST AI RMF is voluntary, and the guidance summarized here should not be mistaken for a universal legal mandate. The sources discussed do not determine the laws or sector-specific compliance duties that apply in every country or industry. Organizations should check the requirements that govern their jurisdiction and activities separately.
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