Begin with detection

The safest starting point is to let a system collect signals, group related events and flag anomalies without changing infrastructure. An AI system might connect an unusual login, bulk file reads and new network activity into one incident. An analyst checks the context and decides what to do. This mode is useful when data sources are known, model requests are logged and the team can reconstruct why an alert appeared.

Move next to recommendations

At the second level, AI proposes an action: revoke a key, isolate a device or require fresh authentication. A person approves execution. Before moving here, define the cost of a false positive, the permitted command set and the owner of the decision. AWS recommends classifying use cases by the data they touch, the decisions they influence and the consequences of error; the NIST AI RMF places risk management across system design, operation and evaluation.

Reserve autonomy for reversible actions

Independent execution fits actions that are tightly scoped, quickly reversible and unlikely to deny someone a critical service. Temporarily restricting a suspicious token while immediately notifying the on-call analyst is one example. Deleting data, locking an executive's account or stopping a production system needs separate approval. Before launch, set a technical ceiling on authority, log every command, define when control passes to a person, test rollback and name the employee accountable for the result.

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