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Start with a lifecycle risk process
The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) organizes risk work into four functions: Govern, Map, Measure, and Manage. They apply across design, development, deployment, use, and testing and evaluation; they are not a one-time pre-launch checklist. NIST describes the framework as voluntary, cross-sector guidance, not a safety guarantee or a replacement for applicable law or sector-specific requirements. NIST AI RMF overview and the AI RMF Core provide the framework details.
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Govern: decide who is accountable and what risk is acceptable
Before choosing controls, establish who owns the system and who has authority to approve, pause, or roll it back. Record the intended use, prohibited uses, risk tolerance, escalation routes, and which roles oversee the AI and any human-AI workflow. NIST’s Core calls for policies that define and differentiate responsibilities in human-AI configurations and oversight. A human reviewer is useful only when the person has the information, time, authority, and ability to intervene that the risk requires.
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- Define intended users, permitted tasks, prohibited actions, and deployment approval authority.
- Set risk tolerances and specify who can stop the system, restrict access, or roll back a release.
- Document how incidents and uncertain or out-of-scope behavior are escalated.
Map: trace actions, dependencies, and possible harm
Describe the full operating context, not just the model. Identify who uses the system, who may be affected, the conditions in which it runs, the data and model dependencies, connected tools or actuators, and decisions that may follow its outputs. Then trace plausible paths from error or misuse to harm, including failures in integrations and downstream processes.
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Distinguish a system that generates advice from one that can act. If it can invoke tools, modify data, or control equipment, map each permission and the consequences of misuse or a mistaken action. Consider ordinary mistakes, foreseeable misuse, adversarial inputs, missing or stale information, and operation beyond the system’s knowledge or design limits.
Measure: test against hazards before and after release
Turn mapped hazards into testable criteria. Evaluate whether the system behaves acceptably in normal use, edge cases, abuse and adversarial attempts, and failure and recovery scenarios. Include reliability, robustness, security anomalies, and the consequences of incorrect outputs—not just whether answers appear plausible. Record known limitations, test evidence, and unresolved risks.
Rank #2
Testing must continue after deployment. NIST’s Generative AI Profile recommends regular safety evaluation, metrics that reflect reliability and robustness, real-time monitoring, and response times for failures; it also recommends regular checks for circumvention of safety measures. Reassess after material model or integration changes, incidents, and changes in users or operating conditions.
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Manage: limit actions and make failure containable
Controls should match the possible harm and the system’s autonomy. Restrict permissions to what is needed, limit action scope, and use approval gates for high-impact or irreversible actions where appropriate. Monitor outputs and performance for relevant warning signs. Define safe fallback or shutdown behavior, and ensure the architecture can handle, recover from, and repair errors when anomalies, threats, or impacts are detected. Rehearse incident response rather than assuming a written plan will work.
Rank #3
Review outputs that can affect downstream decisions, including generated code before it is used. For systems connected to equipment or safety-related functions, involve qualified engineers and consult applicable functional-safety standards; general AI risk guidance is not a substitute for that engineering work.
Choose controls by comparing the risk, not by copying a checklist
There is no single control set that fits every AI system. Use these questions to compare implementation options and decide where stronger safeguards are warranted:
Rank #4
- Severity and reversibility: How serious could harm be, and can an action be undone?
- Autonomy and permissions: Can the system only recommend, or can it act directly? What is the narrowest permission scope that supports its task?
- Detectability and response time: How quickly would a failure be noticed, and how quickly must someone respond?
- Evidence: How strong and independent are the tests for the hazards that matter?
- Human escalation and override: Can an informed person intervene in time, with clear authority?
- Containment and recovery: Can the system be isolated, safely stopped, and restored without compounding harm?
These are practical decision questions, not a NIST scoring system. The more severe or difficult-to-reverse the potential harm, the less a deployment should depend on a single safeguard such as a prompt, a warning, or nominal human review.
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NIST’s AI RMF is voluntary guidance. Its Generative AI Profile, NIST AI 600-1, was released on July 26, 2024. NIST’s framework overview says AI RMF 1.0 is being revised, so check the current official status when adopting it. The profile states that a deployed system should be demonstrated safe, that residual negative risk should not exceed organizational tolerance, and that the system should fail safely, particularly beyond its knowledge limits. Read NIST AI 600-1 and its publication details.
For AI used in or around safety-related equipment, ISO/IEC TR 5469:2024 addresses AI within safety-related functions, non-AI safety functions that ensure the safety of AI-controlled equipment, and AI used to design safety-related functions. It is a technical report with a specific functional-safety scope, not a universal AI checklist. Check the ISO catalogue entry, and determine which legal and sector rules apply in the system’s jurisdiction.
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