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AI safety focuses on preventing harm caused by an AI system’s behavior; AI security focuses on 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 an AI system can produce harmful results even when no one has attacked it.
What is the difference between AI safety and AI security?
A useful shorthand is to ask whether the concern is a harmful outcome or a compromise of the system. Safety examines what the AI might do and the consequences in its intended setting. Security examines who or what could access, alter, expose, or disrupt the system and its information. This is a practical distinction, not a complete formal taxonomy.
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| Question | AI safety | AI security |
|---|---|---|
| What is being prevented? | Harm to people, property, or the environment caused by system behavior. | Unauthorized access, manipulation, disclosure, or disruption. |
| What might cause the risk? | Design limits, errors, unexpected conditions, unsuitable deployment, or misuse. | Attackers, compromised components, weak access controls, or vulnerable software and data pipelines. |
| What should teams examine? | Use context, severity of possible harm, reliability, robustness, fail-safe behavior, monitoring, and human intervention. | Confidentiality, integrity, availability, attack pathways, access controls, model and data protection, and incident response. |
| What evidence can help? | Testing under relevant conditions, monitoring, documented residual risk, and response plans. | Security assessments, adversarial testing, protective controls, and evidence of recovery capability. |
NIST treats “safe” and “secure and resilient” as separate characteristics of trustworthy AI, alongside qualities such as validity and reliability, accountability and transparency, explainability, privacy enhancement, and fairness. Its AI Risk Management Framework (AI RMF) places these characteristics within one broader risk-management approach; no single characteristic by itself establishes trustworthiness. NIST’s overview of AI risks and trustworthiness describes that framework.
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In the NIST AI RMF, safety means keeping AI systems, under defined conditions, from endangering human life, health, property, or the environment. It applies to practical operational risks—not only to concerns about advanced AI’s long-term or existential effects. The relevant question is whether the system is appropriate for its use and can operate without unacceptable harm in that context.
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Safety work can include lifecycle decisions, testing and simulation in the intended domain, monitoring during use, and plans to modify or shut down a system when it deviates from expected function. Teams should prioritize risks according to context and severity rather than assuming a control that works in one application is sufficient in another. NIST’s AI RMF section on safety sets out this approach.
What does AI security cover?
AI security concerns protecting the system and its data, including the confidentiality, integrity, and availability of those assets. In practical terms, teams consider whether unauthorized parties could access information, alter models or inputs, or interfere with a service. Many risks come from familiar software and deployment weaknesses, but AI also creates or changes attack pathways.
- Data poisoning: an attacker manipulates data used in training or operation, potentially changing system behavior.
- Adversarial examples: inputs are crafted to cause a model to produce an unintended or incorrect result.
- Exfiltration: an attacker attempts to extract a model, training data, or intellectual property through system endpoints.
These examples are not an exhaustive list. NIST’s AI RMF discussion of secure and resilient systems and its AI security and resilience research page describe AI-related threats as part of a wider security picture.
Where do AI safety and security overlap?
The same event can be both a security incident and a safety hazard. For example, poisoned data is a security compromise; if it leads an AI system to make dangerous decisions, the resulting risk also belongs in a safety assessment. Conversely, a model may produce a harmful output because of a limitation or unexpected condition without any attacker or security breach.
That distinction matters for response. Security teams may need to contain access, investigate the compromise, and restore affected systems or data. Safety teams may need to assess the potential harm, restrict the system’s operation, monitor its behavior, or bring in human intervention. An incident can require both kinds of response, so the assessments should connect rather than run as isolated checklists.
How should an organization assess both risks?
- Define the use and conditions. Describe what the system is for, where and how it will be used, and the conditions under which it is expected to operate.
- Assess potential harms. Consider who or what could be harmed, how severe the consequences could be, and what system limits or unexpected conditions could contribute.
- Map security threats. Identify what needs protection, who might seek access or disruption, and where data, model, software, or deployment weaknesses could be exploited.
- Choose connected controls. Use relevant testing, monitoring, access protection, response plans, and means for people to intervene. A control aimed at one risk may also reduce another, but that should be assessed rather than assumed.
- Evaluate and document. Record test results, monitoring plans, response responsibilities, and residual risks. Revisit them as the system, its use, or its operating conditions change.
NIST’s AI RMF is voluntary guidance for managing AI risks across design, development, use, and evaluation. In its measurement guidance, NIST states: “Safety metrics reflect system reliability and robustness, real-time monitoring, and response times for AI system failures.” The framework’s treatment of measurement and evaluation is in AI RMF 1.0, Measure 2.6 and 2.7.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How current is the NIST AI Risk Management Framework?
NIST released AI RMF 1.0 on January 26, 2023. Its framework page says that version is being revised and notes an April 7, 2026 concept note for a Trustworthy AI in Critical Infrastructure profile. These are program-status details, not a change to the basic distinction between safety and security; consult NIST’s AI Risk Management Framework page for current framework information.
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