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Advanced AI risks are best reduced through safeguards across the system’s lifecycle: understand the use case, test likely failure modes, layer technical and human controls, monitor real-world effects, and restrict or stop use when remaining risks are unacceptable. No single safeguard makes a system risk-free.
Start with the use case, not a generic safety checklist
A capability’s consequences depend on who uses it, for what purpose, with what data, and under what oversight. The first step is to map the AI system and the setting in which it will operate. NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for managing AI risks generally; the International AI Safety Report 2026 focuses on general-purpose AI, so its findings should not be treated as covering every kind of advanced AI.
Define what the system is—and where it will be used
- Inventory the system and its components, including third-party models, data, and software.
- Document the intended purpose, users, deployment environment, human oversight, and known limitations.
- Identify affected people and groups, potential benefits, and possible harms, including downstream effects.
- Use that context to make an initial decision about whether development or deployment should proceed.
NIST’s AI RMF 1.0, released January 26, 2023, organizes risk work around context, measurement, and management. NIST released a Generative AI Profile on July 26, 2024, and its current overview says the framework is being revised. The AI RMF is voluntary guidance—not a safety certification or, by itself, proof of regulatory compliance.
Evaluate capabilities and behavior before and after release
Testing should be tied to the harms identified for the particular use case. A benchmark score can describe performance on a test; it does not establish that behavior will be safe in a real deployment. NIST recommends documented evaluations before deployment and regular evaluation during operation.
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Use different kinds of evaluation for different questions
- Model testing: examines capabilities and behavior under defined test conditions.
- Red-teaming: probes for weaknesses, adversarial behavior, and misuse pathways.
- Field testing: evaluates the system in conditions closer to actual use.
The U.S. AI Safety Institute’s ARIA program describes these as distinct evaluation levels. They can expose different issues; ARIA’s evaluation design is not a universal certification that a system or safeguard is safe. Where feasible, include reviewers who were not the system’s front-line developers, and involve relevant domain experts and affected communities. Record uncertainty and limitations alongside results.
Layer safeguards around the model and its use
Controls should match the system’s threat model and be evaluated together. A useful defense-in-depth design can combine measures during development with checks and limits during deployment. The International AI Safety Report 2026 describes this layered approach but does not claim that multiple layers eliminate risk.
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| Safeguard layer | Examples | What to assess |
|---|---|---|
| Development | Data curation and safety training | Whether tests cover the harms and capabilities relevant to the intended use |
| Access and inputs | User access controls and input screening | Whether the intended users and requests can be distinguished from misuse attempts |
| Outputs and actions | Output screening, constrained or sandboxed actions, and human oversight | Whether risky outputs or actions can be detected, reviewed, or stopped in context |
| Operations | Content monitoring and mechanisms to log, flag, filter, or stop activity | Whether the organization can identify emerging problems and intervene in time |
Safeguards can be bypassed. The 2026 report describes cases in which harmful outputs can still be elicited by rephrasing requests, breaking tasks into smaller steps, or modifying models. It also warns that current evaluations may not reliably predict behavior in real-world settings. These limits make it important to test the combined system, not only the model in isolation.
Monitor deployments and prepare to respond
Pre-release evaluation cannot cover every operating condition. Plan to detect unexpected behavior and impacts after launch, and make monitoring part of ongoing risk management rather than a one-time gate.
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Set up reporting, review, and recovery
- Collect operational evidence relevant to the identified risks.
- Give users and affected people ways to report problems and, where appropriate, appeal outcomes.
- Define how incidents will be investigated and communicated to affected parties.
- Assign named roles and authority to restrict access, roll back changes, supersede, disengage, or deactivate the system.
- Rehearse recovery and change-management procedures.
NIST’s AI RMF Core includes post-deployment monitoring, user input, appeal and override, incident response, recovery, and decommissioning. Those responsibilities need to be practical: a response plan is of limited use if no one can make the system change or stop it.
Choose a release model that fits the risk
How a model is released affects how much control its developer can retain. A controlled service can preserve more ability to monitor use, limit access, and intervene than a release that lets people download model weights. The International AI Safety Report 2026 notes that open-weight models can be modified or operated outside the original developer’s monitoring, that safeguards can be removed through modification, and that released weights are difficult to recall.
Release decisions are therefore part of risk management, not just packaging. Consider whether the expected use requires broad access, what monitoring or intervention remains possible after release, and how incidents will be reported when a system operates beyond the developer’s environment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set a threshold for limiting or stopping use
Risk management does not mean proceeding regardless of the findings. NIST’s AI RMF 1.0 says that when an AI system presents unacceptable negative risk—for example, imminent significant impacts, severe harms already occurring, or catastrophic risks—development and deployment should cease safely until risks can be sufficiently managed.
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This is a context-based decision, not a universal numeric threshold. Organizations should document what risks remain, who is affected, what controls have been tried, and who has authority to decide whether use can continue. If risk cannot be brought within tolerance, the available choices include restricting access, delaying deployment, or stopping it safely.
Prepare for harm that safeguards fail to prevent
Prevention and mitigation cannot eliminate every failure. The International AI Safety Report 2026 also emphasizes resilience: organizations and public institutions may need the capacity to detect and respond to AI-enabled deception or other emerging threats, including when safeguards fail. Resilience complements controls on AI systems; it is not a substitute for reducing the likelihood or severity of harm.
How to judge whether a safeguard is credible
When comparing safeguards, ask whether they address the relevant risk, work at the necessary point in the lifecycle, and have been evaluated under conditions resembling intended use. Also consider whether independent review exists, how easily the control can be bypassed or removed, and whether it affects usefulness, latency, cost, privacy, or the ability to appeal and override decisions. Finally, check whether the organization can detect an incident and recover, restrict access, or shut down safely.
The International AI Safety Report 2026 says 12 companies published or updated Frontier AI Safety Frameworks in 2025. That count describes the number of frameworks, not their quality, implementation, or effectiveness. A policy document or framework is not evidence on its own that the safeguards work in deployment.
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