AI Governance
AI governance is the set of policies, processes, roles, and controls an organization uses to make sure its AI systems are developed and used responsibly, legally, safely, and in line with its goals.
AI governance answers practical questions. Which AI tools are approved? Who is accountable for each system? What data can AI use? How are risks assessed before and after launch? Public frameworks such as the NIST AI Risk Management Framework and the ISO/IEC 42001 standard for AI management systems give organizations a common structure for these decisions.
Governance is sometimes seen as a barrier that slows AI down. Done well, it has the opposite effect, because clear rules give teams confidence about what they can do. A common approach is to match the level of control to the level of risk, with light review for low-risk uses and closer oversight for systems that make consequential decisions or act on their own, such as AI agents. Governance also needs to keep pace with change, since new tools, agents, and regulations appear often.
Key parts
Policies. Rules for acceptable use, data handling, and approved tools.
Inventory. A current list of the AI systems and agents in use and who owns them.
Risk assessment. Reviewing each use for potential harm, bias, and legal exposure.
Oversight. Defined roles, approvals, and human review for higher-risk uses.
Monitoring. Ongoing checks on performance, incidents, and compliance.
