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AI safety is about preventing harmful outcomes from an AI system’s behavior; AI security is about protecting the system and its data from unauthorized access, manipulation, disclosure, or disruption. They are distinct but connected: a security breach can make an AI system unsafe, while a system can produce harmful results without being attacked.
What do AI safety and AI security mean?
In NIST’s AI Risk Management Framework (AI RMF), safety means managing the risk that an AI system, under defined conditions, could endanger human life or health, damage property, or harm the environment. The question is what the system might do, how serious the consequences could be, and how people can detect and respond when it does not behave as intended. NIST’s explanation of AI safety treats safety as a lifecycle concern, not simply a final test.
AI security is about protecting the system and its data. NIST describes security in terms of confidentiality, integrity, and availability: keeping information from unauthorized disclosure, preventing unauthorized changes, and maintaining access to systems and data when needed. Its AI RMF discussion of security and resilience includes AI-specific threats as well as familiar software and deployment weaknesses.
These are useful working definitions, not an exhaustive formal taxonomy. “AI safety” can refer to different scopes in different settings; in standards-oriented risk management, it includes operational hazards and harms, not only research into long-term or existential risks.
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How do the two risk lenses differ?
| Question | Safety lens | Security lens |
|---|---|---|
| What are you trying to prevent? | Harm to people, property, or the environment caused by system behavior. | Unauthorized access, manipulation, disclosure, or disruption of the system or its data. |
| What might cause a problem? | Design limitations, errors, unexpected conditions, or use in an unsuitable setting. | Attackers, compromised components, weak access controls, or vulnerable software and data pipelines. |
| What should teams examine? | Use context, severity of possible harm, reliability, robustness, system limits, fail-safe behavior, monitoring, and human intervention. | Confidentiality, integrity, availability, threat paths, access controls, model and data protection, and incident response. |
| What evidence is useful? | Testing under relevant conditions, monitoring, and documented residual risk and response plans. | Security assessments, adversarial testing, protection measures, and evidence that recovery is possible. |
NIST lists safety and “secure and resilient” as separate characteristics of trustworthy AI, alongside properties such as validity and reliability, accountability and transparency, explainability, privacy, and fairness. Its framework emphasizes that these characteristics must be considered in context; no single one guarantees trustworthiness. NIST’s trustworthiness overview places both in the same risk-management picture without treating them as interchangeable.
Where do AI safety and security overlap?
The overlap is clearest when a security compromise changes how an AI system behaves or what information it uses. NIST identifies threats such as adversarial examples, data poisoning, and attempts to extract models, training data, or intellectual property through system endpoints. NIST’s AI security and resilience work discusses these AI-related concerns alongside risks shared with ordinary software and deployment security.
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- Data poisoning: An attacker manipulates data used to train or operate a model. That is a security concern because data integrity has been compromised; it can also become a safety concern if the resulting behavior causes harm.
- Adversarial examples: Carefully crafted inputs can cause a model to produce an incorrect or unexpected result. The security lens asks how an attacker can manipulate the system; the safety lens asks what harm could follow from the output.
- Model or data exfiltration: Attempts to obtain a model, training data, or intellectual property through an endpoint are security issues involving unauthorized disclosure. Any resulting safety implications depend on how the exposed material can be used.
By contrast, a model might make a consequential error because it is unreliable in a particular setting, with no attacker involved. That is a safety issue even if no security incident occurred. Connecting the two assessments helps teams avoid overlooking how a compromise could create a hazard—or assuming every safety failure is an attack.
How should a team decide which controls apply?
Start with the system’s intended use and the consequences of failure, then assess both the possibility of harmful behavior and the possibility of compromise. NIST’s AI RMF 1.0 is voluntary guidance for managing AI risk across design, development, use, and evaluation; its structure is designed to support risk decisions in context rather than a one-size-fits-all checklist. NIST’s AI Risk Management Framework page describes the framework and its status.
- Define the use and context. Record what the system is intended to do, where it will be used, who may be affected, and what conditions or limits matter.
- Assess safety consequences. Identify plausible harmful outcomes, their severity, system limits, and the conditions under which performance may fail. Use testing or simulation relevant to the actual operating context.
- Assess security threats. Examine who could access or manipulate the system, its software, inputs, data pipelines, model, or outputs. Consider confidentiality, integrity, and availability, including AI-specific threats such as poisoning and extraction.
- Choose connected safeguards. Safety measures can include monitoring, human intervention, and the ability to modify or shut down a system when it deviates from expected function. Security measures should protect access and data and include a response to incidents. A single safeguard may support both goals, but the risk it addresses should be explicit.
- Test, monitor, and document. Evaluate the system under relevant conditions, monitor it in operation, and record residual risks and response plans. NIST’s AI RMF Measure 2.6 notes: “Safety metrics reflect system reliability and robustness, real-time monitoring, and response times for AI system failures.” The AI RMF 1.0 text discusses measurement and evaluation.
What is the status of NIST’s AI RMF?
NIST released AI RMF 1.0 on January 26, 2023, as voluntary guidance. The framework page states that AI RMF 1.0 is being revised and notes an April 7, 2026 concept note for a Trustworthy AI in Critical Infrastructure profile. These are program updates, not a change to the basic distinction between safety and security; check NIST’s framework page for the latest status.
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