AI Coworker Security Checklist: 12 Questions Before You Connect Company Data
Treat an AI coworker like a new operator with tool access: scope it deliberately, inspect every action, and expand only after evidence.
TL;DR
- Map every connector's read and write scope before installation.
- Verify retention, model-training policy, approvals, logs, and incident response.
- Test failure and adversarial cases in a narrow pilot.
- Document token revocation, data deletion, and offboarding before launch.
The short answer
Before connecting an AI coworker, verify identity, least-privilege access, data handling, model retention, action approvals, audit logs, incident response, connector removal, and offboarding. Then test those controls with a narrow pilot instead of accepting a security badge as the whole answer.
Agentic systems add tool use and delegated action to ordinary SaaS risk. The NIST AI Risk Management Framework and OWASP's agentic application guidance both support a lifecycle view: map access, measure behavior, manage failures, and keep governance active after launch.
The 12-question checklist
| Area | Question | Acceptable evidence |
|---|---|---|
| Identity | Whose authority does each action use? | Named user or service identity |
| Access | Can each connector be least-privilege? | Scopes and permission map |
| Data | Where is content processed and retained? | Data flow and retention terms |
| Models | Is customer data used for training? | Contractual policy |
| Actions | Which writes require approval? | Action-level approval rules |
| Audit | Can admins reconstruct a run? | Timestamped tool and approval logs |
Map every read and write
List the objects each connector can read, create, update, send, or delete. Separate read access from write access. 'Connects to Slack' is not a permission description.
Test prompt and content boundaries
Use benign adversarial examples in a sandbox: instructions hidden in a document, conflicting messages, sensitive content, and a request outside scope. The coworker should ignore untrusted instructions, surface uncertainty, and stop before an unauthorized action.
Make approval specific
Approval should attach to the exact draft or action, not a blanket promise that a human is somewhere in the loop. Confirm who approves, what they see, what expires, and whether the final action is logged.
Plan removal before installation
Document how to revoke tokens, remove the Slack app, delete retained data, export logs, and transfer ownership when an employee leaves. Offboarding is part of security, not an afterthought.
How Mio fits this framework
With Mio, start from the narrowest useful sources and draft-only work in Slack. Confirm the security posture against Mio's current trust and security materials, and expand access only when the initial workflow is dependable.
Mio lives in Slack, uses the company sources a team connects, and turns recurring coordination into reviewable work. It is designed to surface and draft while people retain judgment over consequential actions. Try Mio in Slack.
FAQ
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Mio is the Slack-native AI coworker that already knows your company, connects to 3,000+ tools, and turns shared context into work. Just @mio, it's handled.
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