What is AI Governance?
Governance is more than Security. It includes Policy, Ownership, Risk, Access, Evaluation, Monitoring, Audit and Human Oversight.
Its purpose is to make the use of models, data and Agents understandable, controllable and accountable across the lifecycle.
Who owns AI?
Business Owner, Technical Owner, Security, Data Owner, Compliance and Operator can be defined in an enterprise architecture. These are architectural examples, not a single mandatory standard.
Ownership should clarify who decides about purpose, data, access, quality and retirement.
Inventory: what does the organization have?
An organization needs an up-to-date inventory of capabilities, connections and owners.
- Models in use
- Active Agents
- Connected data and Knowledge Sources
- Available Tools
- System owners
Access Governance
Define who can build an Agent, add a Tool, select a model or connect a Knowledge Source. Access should be role-based, recorded and reviewable.
Human Approval and operation levels
Operations can range from Read and Suggest to Draft, Execute and Sensitive Execute. Sensitive actions such as payments, deletion or official messages should require Policy-based approval.
Evaluation before and after release
Evaluation continues after release using real scenarios to review behavior, errors and policy impact.
- Quality
- Safety
- Hallucination
- Access control
- Tool behavior
Logging and Audit
Record user, agent, tool, action, resource and result as needed for review. Sensitive data should not enter Logs without a clear reason.
The AI lifecycle
A lifecycle such as Design → Test → Approve → Deploy → Monitor → Review → Retire keeps systems under management after launch.
Governance for Agents
Agents make Governance more important because they act, not only answer. Tool permissions, action limits, approval, logging and stop conditions belong in Agent design.
AI Governance at Aivan
In an enterprise platform, Governance should be part of architecture: roles and permissions, tool controls, operation records, evaluation and human approval for sensitive actions.
Frequently asked questions
What is AI Governance?
A framework of rules, roles and controls for managing models, data, Agents and AI risk.
How is it different from AI security?
Security is one part of Governance; Governance also covers ownership, policy, evaluation, oversight and accountability.
Is Governance only for large organizations?
No. Any organization using AI with real data or workflows needs a level of rules and ownership.
How do Agents change Governance?
Agents can act, so Tool permissions, approvals, action limits and records become more important.
Which operations need human approval?
Sensitive, irreversible, financial or legal-impact actions should depend on policy-based approval or multi-step control.
Sources and further reading
- NISTArtificial Intelligence Risk Management Framework (AI RMF 1.0)26 January 2023 · Risk Management Framework
- NISTArtificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile26 July 2024 · NIST AI 600-1