AI systems are safer to use when safeguards are matched to their intended purpose, users, and foreseeable misuse—and applied throughout the system’s lifecycle. Risk assessment, suitable technical controls, meaningful human oversight, pre-release testing, and post-release monitoring work together; no single measure guarantees safety.
How can AI systems be made safer?
Start by identifying what the system is meant to do, who may be affected, and what could go wrong in normal use or foreseeable misuse. Then choose controls proportionate to those risks, test whether the controls work, and revisit them as the system or its operating context changes.
A safeguard is useful only if it addresses a plausible failure mode and someone can verify and maintain it. For example, a system that gives people consequential recommendations may need stronger checks on accuracy, bias, explanation, and human intervention than a tool used to brainstorm low-stakes ideas.
| Lifecycle stage | What to do | Useful evidence |
|---|---|---|
| Define the use | Specify the intended task, users, operating conditions, affected people, and foreseeable misuse. | A documented use description, boundaries, and risk assessment. |
| Design and develop | Build proportionate controls into the system and its operating process; address risks that can be reduced through design before relying on later intervention. | Design records, data checks, security measures, and documented mitigation decisions. |
| Test before release | Evaluate the system and its safeguards against the intended use, relevant failure cases, and defined acceptance criteria. | Test results against stated metrics and thresholds, plus records of unresolved risks. |
| Deploy and oversee | Give deployers relevant information and assign qualified people who can recognize problems and act within their authority. | Operating instructions, training, escalation routes, and intervention procedures. |
| Monitor and respond | Watch for changes in performance, harmful outcomes, security issues, and incidents; investigate and update controls when needed. | Monitoring records, incident reports, corrective actions, and reassessment decisions. |
This is a continuous cycle, not a one-time sign-off. A change in users, data, model behavior, or deployment conditions can alter the risks and make an earlier assessment inadequate.
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What safeguards should AI systems have?
Controls should be selected for the system’s actual risks. These safeguard areas address different failure modes and reinforce one another; none, by itself, establishes that a system is fair or safe.
- Data governance and quality: Check whether data is relevant and appropriate for the intended use, and examine its statistical properties and possible gaps. Pay attention to groups that could be affected by biased or unrepresentative data.
- Accuracy and robustness: Test performance in expected conditions and relevant edge cases. A system that performs well on average can still fail for particular inputs, users, or settings.
- Bias and impact checks: Assess how outputs and errors may affect different groups, including minors or other vulnerable people where relevant. Use findings to select targeted mitigations rather than treating a single fairness check as a guarantee.
- Cybersecurity: Protect the system and relevant infrastructure against threats that could compromise its operation or outputs. Security controls should fit the system’s deployment and exposure.
- Transparency and records: Provide deployers with information they need to use the system appropriately, and maintain documentation and records that support oversight, investigation, and accountability.
- Monitoring and incident response: Define what signals will be monitored, who reviews them, how incidents are escalated, and what corrective actions are available.
The EU AI Act includes data governance, technical documentation and record-keeping, transparency and information for deployers, human oversight, robustness, accuracy, and cybersecurity among requirements for covered high-risk systems. These are mutually reinforcing requirements, not a claim that any one system quality eliminates risk (Regulation (EU) 2024/1689, including its recitals and requirements).
How do you manage risks from generative AI?
Generative AI calls for the same lifecycle discipline, with attention to risks that arise from or are amplified by generation and the system’s particular deployment. NIST’s Generative AI Profile says it “defines risks that are novel to or exacerbated by the use of GAI.” The profile proposes actions aligned with NIST’s AI Risk Management Framework (AI RMF), rather than treating generative AI as a separate guarantee or certification.
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For a generative AI use case, make the intended role and limits clear, test outputs against relevant misuse and failure scenarios, and decide how users can report harmful or unreliable results. Where a human is expected to review outputs, specify what they must check and what they can do when an output is unsuitable. Monitor outcomes after deployment and revise the controls when evidence shows the original assumptions no longer hold.
NIST organizes its AI RMF around four functions:
- Govern: Establish accountability, policies, and responsibilities for managing AI risks.
- Map: Understand the system’s intended context, affected people, and potential impacts.
- Measure: Assess risks and system behavior using appropriate methods and evidence.
- Manage: Select, implement, and monitor mitigations in response to the assessment.
NIST describes the AI RMF as voluntary guidance intended to help incorporate trustworthiness into AI design, development, use, and evaluation. AI RMF 1.0 was released on January 26, 2023; NIST’s framework overview says it is being revised. NIST published the cross-sectoral Generative AI Profile, AI 600-1, on July 26, 2024. Neither the framework nor the profile is a certification or proof that a particular system is safe (NIST, AI Risk Management Framework overview; NIST, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile).
When do EU AI Act safeguards apply?
The EU AI Act does not impose the same duties on every AI system or use. Its requirements depend on defined categories and the actors involved. In particular, Article 9 establishes a documented, iterative risk-management system for high-risk AI systems; it is not a universal checklist legally imposed on all AI.
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For high-risk systems in scope, Article 9 calls for identifying and analyzing known and reasonably foreseeable risks to health, safety, or fundamental rights under intended use; estimating risks under intended use and reasonably foreseeable misuse; considering post-market monitoring information; and applying measures targeted to the identified risks. Risks should be eliminated or reduced as far as technically feasible through design and development, with mitigation and control measures used for risks that cannot be eliminated. The article also calls for considering potential adverse impacts on minors and, as appropriate, other vulnerable groups.
Testing for covered high-risk systems should take place as appropriate throughout development and, in any event, before the system is placed on the market or put into service. Testing is to use predefined metrics and thresholds appropriate to the system’s purpose. The Act also links safeguards to relevant information and, where appropriate, training for deployers (Regulation (EU) 2024/1689, Article 9).
A separate category has additional duties: general-purpose AI models with systemic risk. Under Article 55, those duties include model evaluation using protocols and tools reflecting the state of the art, documented adversarial testing, systemic-risk assessment and mitigation, serious-incident reporting, and adequate cybersecurity for the model and its physical infrastructure. These provisions apply to that defined model category, not to every generative AI system (Regulation (EU) 2024/1689, Article 55).
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Whether a particular system or actor falls within a legal category depends on the Act’s definitions and applicable circumstances. NIST guidance can inform risk-management practice, but it is voluntary and is not a substitute for determining legal obligations in the relevant jurisdiction.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What makes human oversight meaningful?
Human oversight is effective only when it is operational. Assign people who have the competence, training, information, and authority to recognize when the system is not working as intended and to intervene. A nominal reviewer who lacks time, context, or power to act is not a reliable safeguard.
Depending on the context, oversight may require operational constraints that the AI system itself cannot override. The process should help assigned people decide if, when, and how to intervene, including stopping the system when appropriate. People responsible for oversight also need a clear route to report concerns and obtain support (Regulation (EU) 2024/1689, recitals on human oversight).
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How can an organization tell whether its safeguards are working?
Before deployment, define what acceptable performance means for the intended use and how important risks will be tested. Use scenarios that reflect likely operating conditions and reasonably foreseeable misuse, and document the metrics, thresholds, findings, and remaining uncertainties. For systems covered by the EU Act’s high-risk requirements, Article 9 specifically calls for testing against predefined metrics and thresholds appropriate to the purpose.
After release, review performance and incidents against those expectations. A safeguard that worked during pre-release testing may not be sufficient if users, inputs, operating conditions, or system behavior change. Monitoring should connect observed problems to an owner with authority to investigate, revise controls, limit use, or stop the system where needed.
NIST’s voluntary AI RMF offers one way to structure this work across governance, context mapping, measurement, and mitigation. It can help organize decisions, but following a framework is not itself evidence that every relevant risk has been controlled.
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