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Shared control area · counts toward 2 standards
AI governance: oversight & transparency
AI governance ensures that artificial intelligence systems are deployed with human accountability, data integrity, and operational clarity. Frameworks require this to mitigate systemic risks such as algorithmic bias, hallucinations, and the unauthorized use of sensitive data.
Implement it once
- Establish an AI Asset Registry that catalogs every model in use, its purpose, the data it consumes, and its risk level.
- Define a formal approval process where a designated oversight body or committee reviews and signs off on models before production deployment.
- Integrate "Human-in-the-Loop" (HITL) checkpoints into workflows to ensure critical AI outputs are verified by a qualified person.
- Implement data provenance controls to document the source, legality, and cleaning processes of datasets used for training or fine-tuning.
- Create clear transparency disclosures that notify end-users when they are interacting with an AI system or consuming AI-generated content.
Evidence it produces
- A comprehensive AI Inventory/Registry including versioning and ownership.
- Documented AI Risk Assessments and corresponding mitigation plans for each deployed model.
- Approval logs and meeting minutes from the governance committee showing oversight activity.
- Screenshots or policy documents detailing user-facing transparency notices.
- Data lineage maps or manifests for training datasets.
Where it counts
Establishing a centralized AI governance framework satisfies the accountability and risk management requirements found in most modern security, privacy, and emerging AI regulations. By documenting these processes once, an organization can provide the same evidence to different auditors regardless of which specific standard they are assessing.