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The strongest safeguards combine clear limits on what an AI system may identify or do, testing that reflects real operating conditions, independent checks before consequential action, and human oversight with the authority to reject, reverse, or stop the system. The level of control should rise with the potential harm and the difficulty of undoing an error. The details differ substantially between biometric identification, military targeting, and software agents that act on websites; no single safeguard or law applies to all three.
First, define what “the wrong target” means
Target selection is not one technical task. A system might match a face to a person, flag an object for a military decision, or choose a person, account, file, or webpage as the object of a software action. The failure can occur when the system identifies the wrong entity, when it selects the right entity for an unauthorized purpose, or when a correct identification leads to an inappropriate action.
These distinctions matter because the controls must fit the system’s action path. A recommendation that a trained person can inspect before acting creates a different risk from an automated identification that directly triggers an engagement. In military contexts, the Stockholm International Peace Research Institute’s June 2025 comparison describes autonomous weapon systems as having a direct path from target identification to engagement; an AI decision-support tool can also contribute to harm if a human acts on a false identification.
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For a software agent, the target may be an account, purchase, message, or setting rather than a person or physical object. A system can be misdirected by instructions embedded in third-party web content, even when those instructions are not authorized by the user. OpenAI’s Operator system card describes prompt injection from websites as a risk. Accordingly, “target safety” includes controlling both what the system recognizes and what it is allowed to do after recognition.
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Set the system’s role and boundaries before use
Write down the system’s intended purpose, permitted targets, permitted data, and operating context. Also specify what it must not identify or do. A system evaluated for one population, environment, or task should not silently be treated as suitable for another. Reassess it before changing its purpose or applying it in a materially different context.
Make the limits visible to the people who configure, operate, review, and rely on the system. Define who is accountable at each stage, from setting the purpose and approving deployment to reviewing outputs and responding to failures. If responsibility passes between people or teams, make that handoff explicit. The US Intelligence Community’s Artificial Intelligence Ethics Framework, version 1.0 from June 2020, calls for asking questions such as which performance metrics suit the AI and how results were independently verified.
Before adopting AI, consider whether it is necessary for the task and whether a less risky method could meet the need. This is not a claim that AI is always avoidable; it is a practical check against granting a system authority simply because it can produce a prediction or recommendation.
Test the errors that matter in the intended context
Overall accuracy alone can hide the errors most likely to cause harm. A false positive selects or identifies something that should not have been selected; a false negative fails to identify something that should have been. Which error matters more depends on the use. A false positive could trigger an unwarranted investigation or action, while a false negative could prevent a legitimate identification or response. Evaluations should measure both where relevant and explain the consequences of each.
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Test the system with data and conditions that reflect its intended use, including relevant populations, operating environments, and foreseeable misuse. Record the limitations of the data and the conditions under which the evaluation was performed. Assess for bias and examine whether performance changes across populations or conditions that matter to the decision. If the environment or data changes, a result from an earlier evaluation may no longer support the same level of confidence.
Set explicit performance thresholds and operational constraints appropriate to the risk. There is no universal accuracy threshold established by the cited frameworks for every target-selection system. A threshold that might be acceptable for a reversible, low-impact suggestion cannot simply be carried over to a decision that could cause serious or irreversible harm. Document why the selected measures and thresholds are suitable for the use, rather than presenting a single score as proof that the system is safe.
Require independent verification when a mistake would matter
For consequential decisions, add a check that is meaningfully separate from the AI output before action. The reviewer should have enough relevant context to assess the identification, rather than being asked merely to confirm the model’s answer. Depending on the application, this may involve checking other available evidence, reviewing the circumstances, or obtaining a second qualified assessment. The source material supports independent verification as a safeguard, but does not prescribe one universal verification method for every domain.
There is a specific legal rule worth distinguishing from general best practice. Article 14 of the EU AI Act includes a two-person verification requirement for certain decisions involving high-risk AI systems used for remote biometric identification, subject to exceptions under applicable law. That rule is not a general requirement for every AI identification, military targeting system, or software agent. Its application depends on the defined system category, circumstances, and applicable law.
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The European Commission AI Act Service Desk page describes its displayed text as following the consolidated version as of 27 July 2026 and cautions that its summaries are not legally binding. Organizations making legal or deployment decisions should consult the applicable legal text and jurisdiction, rather than treating this summary as legal advice.
Make human oversight real, not ceremonial
A human checkpoint reduces risk only if the person can understand enough about the output to judge it and can intervene in time. Showing a reviewer a confidence score or asking them to click “approve” does not, by itself, create meaningful oversight. Reviewers need relevant context, information about limitations, and training to recognize anomalies and automation bias—the tendency to give a system’s recommendation more weight than the evidence warrants.
Most importantly, the reviewer must have genuine authority and a practical opportunity to act. Article 14 of the EU AI Act says that oversight for high-risk systems is intended to enable a person to decide not to use the system or to disregard, override, or reverse its output. It also addresses intervention or interruption through a stop button or similar procedure that brings the system to a safe state. For a control to work in practice, the person must know how to use it, have the authority to do so, and be able to act before the consequential step occurs.
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Protect software agents from misleading inputs
Agents that browse or interact with websites face a distinct risk: external content can contain instructions that try to redirect the agent. Treat webpages and other third-party material as potentially misleading input, not as an authority to change the user’s task or permissions. Limit the actions an agent can take, monitor its interaction where appropriate, and require confirmation before selected high-impact steps.
OpenAI’s Operator system card describes confirmation and human oversight for certain consequential actions as mitigations. These are vendor-described controls, not independently verified evidence that the mitigations eliminate the risk or guarantee safe outcomes. A confirmation prompt is useful only if it clearly describes the action and its consequences and is shown before the agent commits to it.
Choose controls by action path, harm, and reversibility
When comparing system designs or deciding what safeguards to require, assess the full path from input to action rather than comparing model names or accuracy figures in isolation.
| Decision factor | Questions to answer | Why it matters |
|---|---|---|
| Action path | Does an identification directly trigger an action, or does it inform a human decision? | A direct path leaves less opportunity to catch an error before action. Decision support still carries risk if a person relies on an incorrect output. |
| Potential harm and reversibility | How serious would a wrong action be, and can it be undone? | Higher-impact or harder-to-reverse actions call for stronger confirmation and intervention controls. |
| Human control | Can an authorized person understand, reject, reverse, or safely stop the system in time? | Nominal approval is not enough if the reviewer lacks context, time, authority, or a functioning stop mechanism. |
| Verification and records | Is a consequential identification checked independently, and are purpose, limits, versions, tests, and decisions recorded? | Records support accountability and help teams investigate whether a system remains suitable. |
| Operating context | Do evaluations and reviews cover the relevant data, environment, affected populations, and law? | Results from a different context may not establish performance for the actual use. |
Monitor the system throughout its use
Approval at deployment is not a permanent guarantee. Track performance and look for changes in the data, environment, affected populations, and patterns of use that could undermine prior evaluation. Set a documented review interval appropriate to the system and its risks, and define who investigates concerns and who can restrict or stop use if requirements are no longer met.
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Maintain records of the intended purpose, system and model versions, relevant data and test information, known limitations, output verification, and decisions made using the system. These records should be useful to the people responsible for oversight and incident response, not merely stored without an owner. When a system is updated or used in a new context, determine whether its evaluation, constraints, and oversight remain appropriate before relying on it there.
ISO/IEC DIS 42105 is draft guidance on human oversight, not a final standard. Its stated expectation is that monitoring and control capability should be “sufficient, but not necessarily perfect.” That is a reminder that oversight is a practical, risk-based control—not a promise that every error can be prevented. The relevant question is whether the people and procedures around the system can detect and address failures in time for the use at hand.
What a responsible safeguard plan should specify
A usable plan turns broad principles into decisions that operators and accountable managers can apply. At minimum, it should identify:
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- The system’s authorized purpose, target, data, and operating context, plus uses and actions that are prohibited.
- The relevant false-positive and false-negative risks, evaluation conditions, limits, and justified performance thresholds.
- Which consequential outputs require independent verification and who is qualified to perform it.
- What information reviewers receive, how they are trained, and how they can reject, override, reverse, or stop an output before action.
- For agents using external content, which actions are restricted and which require a clear human confirmation.
- Who owns monitoring, incident investigation, periodic review, and the decision to limit or stop deployment.
The plan should be specific to the actual system and jurisdiction. The EU AI Act’s high-risk oversight requirements, SIPRI’s analysis of military targeting, the US Intelligence Community framework, UK defence policy, and a vendor system card address different settings; their controls should not be merged into a claim that one rule governs all AI systems.
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