AI System Access Controls
Added May 2026
Apply authentication, authorization, and role-based access controls to AI systems, their APIs (software connections), and the sensitive data they process.
Objective
Ensure that only authorized users and systems can interact with AI capabilities, and that access is scoped to what each role requires.
Maturity Levels
Initial
AI systems are accessible to all employees with no role-based restrictions.
Developing
Basic authentication exists but authorization is broad rather than role-specific and not regularly reviewed.
Defined
Role-based access controls are defined, documented, and enforced for all AI systems and APIs.
Managed
Access is reviewed quarterly; provisioning and de-provisioning are tied to HR processes.
Optimizing
Access is dynamically adjusted based on context and risk level; unusual access patterns trigger automated alerts.
Evidence Requirements
What an auditor or assessor would expect to see for this control.
- —Role-based access control (RBAC) matrix documenting roles, access levels, and authentication methods for all AI systems and APIs
- —Access provisioning records confirming formal approval was obtained before access was granted
- —Quarterly access review records showing active accounts were verified and unused access was revoked
- —De-provisioning confirmation records for departing employees, completed within the defined timeframe
- —Access logs showing system usage by identity for spotting unusual activity (anomaly detection) and misuse investigation
Implementation Notes
Key steps
- Treat AI system APIs as sensitive infrastructure: manage API keys (secret access codes), replace them on a schedule, and audit their use, as for financial or HR systems.
- Implement separate access tiers for running the model, training data, output logs, and admin functions.
- Ensure removing AI system access (de-provisioning) is part of your offboarding process: departing employees who still hold AI API keys are a frequent oversight.
- Log all access with sufficient context to detect misuse: user identity, timestamp, inputs provided, and outputs returned.
Example Implementation
Enterprise SaaS company with three AI-powered product features and an internal analytics assistant
AI System Access Control Matrix
| System | User Role | Access Level | Auth Method | Review Cadence |
|---|---|---|---|---|
| Customer Summarizer | Account Manager | Invoke (own accounts only) | SSO + RBAC | Quarterly |
| Customer Summarizer | Admin | Invoke (all accounts) + view logs | SSO + RBAC | Quarterly |
| Fraud Detection API | Risk Analyst | Query (read results) | API key per user | Quarterly |
| Fraud Detection API | Engineer | Invoke + configure | API key + MFA | Quarterly |
| Internal Analytics Assistant | All employees | Invoke (own data only) | SSO | Annual |
| Model training pipelines | ML Engineer | Read/write | Short-lived token via CI/CD | Per deployment |
De-provisioning: AI system access is included in offboarding checklist; access revoked within 24 hours of departure
Unused access: Accounts with no AI system activity in 90 days flagged for review and de-provisioning