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Traceability and Auditability

Ensuring full traceability of all AI interactions is critical for clinical accountability and regulatory compliance.

🔎 What Is Traceability ?​

Traceability means logging and storing detailed records of:

  • API requests and responses
  • Model versions used
  • User actions and permissions
  • Data processing timestamps

🗃️ Logging and Storage​

  • Logs are securely stored and encrypted.
  • Retention periods comply with legal and institutional policies.
  • Access is restricted to authorized personnel.

🧾 Audit Trails​

  • Enables retrospective analysis of clinical decisions aided by AI.
  • Facilitates investigations in case of adverse events.
  • Supports compliance audits by regulatory bodies.

🔐 Security Measures​

  • Role-based access controls (RBAC) protect sensitive logs.
  • Continuous monitoring detects unauthorized access attempts.

📈 Benefits​

  • Enhances trust in AI-assisted diagnostics.
  • Protects institutions from legal risks.
  • Improves AI system accountability and quality.
tip

Integrate traceability early in your AI deployment to avoid costly compliance gaps.

Last updated on by Berangere Gallais