Security leaders should push for evidence requirements before AI systems move into production. At a minimum, organizations should know what logs are available, how long they are retained, who can access them and whether they are sufficient for investigation. For higher-risk use cases, teams may also need records of model version, prompt history, output history, user actions, data sources and downstream decisions.
This does not mean every AI interaction needs heavy surveillance. Monitoring should be proportional to risk, and organizations still need to respect privacy, legal and workforce considerations. The point is simpler: if the AI system matters enough to influence real work, it matters enough to leave an evidence trail when something goes wrong.
Ownership cannot be implied
AI ownership is often fragmented. A business unit may sponsor the use case, a data science team may configure the model, IT may manage the platform, security may assess risk, and a vendor may provide the underlying capability. Everyone is involved, but no one may be fully accountable after deployment.
