Approved-Use Baseline

What an approved-use baseline establishes

An approved-use baseline turns policy into a working reference point. It defines the AI tools, identities, data types, and actions that are acceptable, so analysts can quickly separate sanctioned activity from activity that needs review.

The value of the baseline is not only approval, but specificity. If it is too broad, it will miss risky behaviour; if it is too narrow, it creates noise by flagging routine work that should be allowed.

How the baseline supports investigation decisions

A useful baseline connects policy to observed behaviour. Endpoint telemetry, application logs, and account context can be compared against the approved set to decide whether an action is expected, ambiguous, or clearly outside policy.

That comparison is especially important when AI tools operate through shared endpoints or common browser sessions, because the same device may be used for both authorised work and unauthorised experimentation. The baseline gives responders a consistent way to decide when to escalate and when to leave activity in normal operations.

For broader security operations, the baseline also helps reduce judgment drift. Analysts do not have to infer approval from memory or informal norms; they can check a documented standard and apply it consistently across teams and time.

What belongs in the policy set

A strong approved-use baseline usually names the tool or service, the approved accounts, the data classes that may be processed, and the actions that are allowed. It may also distinguish between read-only use, administrative use, and any permitted automation or integration.

That level of detail matters because “approved” is often conditional. A tool may be sanctioned for low-risk content but prohibited for regulated data, or allowed for analysis but not for autonomous execution. The more precisely those conditions are written, the easier it is to detect misuse and explain enforcement decisions.

The baseline should also be versioned and owned. When tools change, new accounts are added, or data handling rules shift, the policy reference has to change with them or it stops being reliable evidence of sanctioned use.

Why approved use still needs monitoring

Approval does not eliminate risk. A sanctioned tool can still be misused, over-permissioned, or repurposed beyond the original intent, so the baseline must be paired with monitoring that shows whether real usage still matches the policy.

That includes looking for unexpected data movement, unusual account sharing, or activity that starts within an approved tool but crosses into unapproved destinations or workflows. The baseline is the yardstick, not the control by itself.

When organisations treat approval as a one-time label instead of an active governance boundary, they create blind spots. The result is often delayed detection, weak containment decisions, and poor evidence when a review or incident investigation begins.

Risk and Threat Considerations

Approved-use baselines can be abused when organisations assume that “allowed” means “safe.” A sanctioned AI tool, account, or workflow can still become a pathway for data exposure, policy bypass, or stealthy misuse if the baseline is outdated or too permissive.

Failure mechanism: The control fails when policy and reality drift apart, for example when new tools, accounts, datasets, or action types are adopted without being added to the baseline, or when a sanctioned tool is used with broader privileges than intended.

Impact: Misuse can blend into normal activity, making it harder to detect exfiltration, unauthorised experimentation, or dangerous automation. That can prolong exposure and weaken incident response because analysts lose a trusted reference for containment decisions.

Practitioner Guidance

Why practitioners should care: Treat the approved-use baseline as an operational control, not just a policy artifact. It should be specific enough that analysts can apply it during review without guessing what “approved” was supposed to mean.

Common misunderstanding: Many teams overfocus on the tool itself and under-specify the allowed accounts, data classes, and actions. In practice, those details are what make the baseline useful for triage and enforcement.

Practitioner takeaway: Keep the baseline tightly tied to observable behaviour so it can support both day-to-day monitoring and post-incident review.

Related resources from NHI Mgmt Group

• What is the difference between shadow AI and approved AI use?
• How should organisations audit AI use that happens outside approved tools?
• What signals show that an AI system is drifting outside its approved use case?
• Why do AI governance programmes need ongoing evaluation when models are already approved for use?