AI agents with governance
AI that can act must be controlled, logged, and reviewable
An AI agent is different from a chatbot: it can take actions through tools (tickets, systems, scripts, cloud services). That power must be bounded or it becomes risk. We deploy agents with least privilege, human approval gates where needed, and end-to-end logging for accountability.
Common agent use-cases
- Support triage: categorize issues, collect required details, draft resolution steps.
- Documentation: convert runbooks, SOPs, and change notes into searchable knowledge.
- Compliance support: map evidence to controls and build audit-ready reporting.
- Security operations: summarize alerts, enrich context, propose response steps for review.
Agents should reduce workload—without expanding blast radius.
Non-negotiable controls
- Least privilege: agents only get the tools and access required for the specific task.
- Human review gates: high-impact actions require approval (account changes, deletes, deployments).
- Logging: prompts, outputs, tool calls, and artifacts captured for auditing and troubleshooting.
- Data boundaries: explicit rules around PHI/sensitive data, plus secure channels when needed.
- Rollback: if an agent can change systems, rollback must exist and be tested.
Implementation framework
A safe rollout is structured and measurable.
1) Scope
Define what the agent can do, where it can operate, and what “success” looks like.
2) Controls
Permissions, review gates, logging, retention, and incident procedures.
3) Prove it
Test against failure modes: bad inputs, prompt injection attempts, permission errors, data leakage risks.
If someone is selling you agents without governance, they are selling you risk.