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Protecting an AI/ML system means securing more than its model. Teams need to account for data, training and testing processes, deployment services, and supporting infrastructure, then assess how attackers could affect confidentiality, integrity, or availability at each stage. NIST’s voluntary AI Risk Management Framework (AI RMF) and its adversarial machine learning (AML) taxonomy offer useful ways to organize that work—not a universal checklist or a guarantee of security.
What AI/ML security needs to cover
AI systems retain familiar cybersecurity risks: information can be exposed, data or behavior can be altered, and services can be disrupted. Those risks may involve the model, but they can also arise in the surrounding software, hardware, infrastructure, access controls, training data, or output data. Security planning should therefore cover the whole system boundary rather than treating the model as the only asset.
AI systems can also create attack paths that need specific analysis. NIST notes that existing cybersecurity frameworks and guidance do not comprehensively address some machine-learning attacks, including evasion, model extraction, membership inference, and availability attacks. Established cybersecurity practices remain important, but they do not replace AI-specific threat analysis.
NIST describes security and resilience as characteristics of trustworthy AI. In its AI Research – Security and Resilience overview, NIST states that the trustworthiness of AI technologies depends in part on how secure they are.
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Map threats to the AI/ML lifecycle
An attack’s relevance depends on the system, its use, and the attacker’s opportunity. NIST’s AI 100-2 E2025 taxonomy organizes adversarial ML by attack type, lifecycle stage, attacker goal, and attacker capabilities and knowledge. It covers predictive AI (PredAI) and generative AI (GenAI), multiple learning methods and data modalities, and discusses mitigations along with their limitations.
| Lifecycle area | Threats to assess | Questions for the team |
|---|---|---|
| Data and training | Training-data manipulation or poisoning may compromise model behavior or integrity. | Who can supply, change, approve, or access data and training processes? What would a change do to model behavior or downstream decisions? |
| Inputs and inference | Evasion attacks use inputs intended to make a model behave incorrectly or lose performance. GenAI applications also need to assess misuse and risks arising from interactions with the application. | Which inputs can users or connected systems provide? What incorrect behavior would matter, and what safeguards or review are appropriate in this use context? |
| Model and information exposure | Privacy attacks may seek information about people represented in training data. Other attacks or interactions may seek to extract information about the model or proprietary information it can access. | What sensitive information is in the system’s data or within its reach? Who can access model behavior, outputs, or connected information? |
| Deployment and operations | Conventional weaknesses in software, hardware, infrastructure, access controls, or service availability can affect AI systems alongside AI-specific threats. | Which services and infrastructure are required to operate the system? How could unauthorized access, compromise, or disruption affect the model and connected systems? |
| Evaluation and change | Risk can shift as a system, its use, or its lifecycle context changes. Security and resilience need to be evaluated and documented in context. | What changed since the last assessment, and should the threat analysis, evaluation, or mitigation plan be revisited? |
These are categories to investigate, not a claim that every attack applies to every model. For example, the relevant attacker capabilities and likely impact depend on how the system is trained, exposed, connected, and used.
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Use a risk-based assessment rather than a fixed control list
NIST’s AI RMF is voluntary guidance for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. Its companion Core frames risk management as continuous across AI system lifecycle dimensions and calls for security and resilience to be evaluated and documented. The framework supports contextual risk management; it is not a certification or a universal control prescription.
- Set the system boundary. Identify the data, model, training and testing processes, deployment services, infrastructure, users, and connected systems that could affect or be affected by the AI system.
- Describe plausible attacker goals and access. Consider which lifecycle stages are exposed, what an attacker might seek to change, learn, or disrupt, and what capabilities or knowledge would be plausible in this deployment.
- Trace consequences. For each credible threat, consider effects on confidentiality, integrity, and availability—not only for the model, but also for data, service operation, decisions, and connected systems.
- Select context-appropriate mitigations. Match mitigations to the threats and system boundaries they are meant to address. Record the assumptions they depend on and the risks they leave unresolved.
- Evaluate and document security and resilience. Assess the system in its intended context, preserve findings and relevant assumptions, and use the results to inform risk decisions.
- Revisit the assessment when the system changes. Changes to data, models, processes, deployment, use, or lifecycle context can change exposure; repeat relevant risk work rather than treating the initial assessment as permanent.
Account for mitigation limits
A mitigation is not a universal fix simply because it addresses a named attack class. NIST’s AML taxonomy discusses limitations of existing techniques, so teams should ask what a proposed measure assumes about the attacker, the model, the data, and the operating environment. They should also consider which attack goals or lifecycle stages it does not address.
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Documenting those limits makes residual risk visible to system owners and decision-makers. It also helps teams avoid mistaking a successful check against one threat for evidence that the entire AI system is secure.
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The AI RMF is voluntary guidance. Its value is in providing a lifecycle-oriented structure for identifying and managing trustworthiness risks, including security and resilience—not in certifying a system or establishing that every organization has met a legal or regulatory duty. Organizations should identify any separate requirements that apply to their system and jurisdiction rather than treating voluntary framework use as a substitute for them.
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NIST published AI 100-2 E2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, on March 24, 2025. NIST says it plans annual maintenance of the report, so teams applying its taxonomy should check the current NIST report and any revisions.
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