AI-agent security covers the full process: accessible data, technical identity, authorised actions, approvals, logs and incident handling.
Apply least privilege and govern sources
- Grant only the permissions required for the task.
- Keep secrets server-side.
- Record source, date and document version.
- Require human approval for sensitive actions.
Measure quality over time
Monitor quality, errors, rejected approvals, costs, processing time and adoption to improve the agent while retaining control.
Move from an idea to an operational scope
Document the current process, monthly volume, users, systems, available data, common errors and expected outcome before selecting a model. This baseline makes value measurable and exposes missing data or ownership.
Set autonomy according to risk
Start with read-only access or a draft submitted for human approval. Allow execution only for clearly authorised and reversible actions. Sensitive, uncertain or exceptional cases should be escalated with their context and sources.
Test and measure in production
- Quality on a representative test set.
- Processing time and manual rework.
- Tool errors and human escalation rate.
- Actual adoption by the intended users.
- Cost per completed and accepted operation.
France Num recommends a progressive approach based on concrete business needs, clear objectives and employee involvement. Official guidance: https://www.francenum.gouv.fr/guides-et-conseils/intelligence-artificielle/comprendre-et-adopter-lia/comment-deployer-lia