What is AI governance?

AI governance is presented as an enforceable operational system rather than a set of values alone, combining policies, an inventory of approved models and agents, least-privilege access controls, and comprehensive audit records. It applies responsible-AI principles such as accountability, transparency, privacy, security, fairness, and human oversight through controls including attribution, logging, data loss prevention, model evaluation, approval workflows, and scoped credentials. The need has increased as AI agents can access sensitive information and take actions in business systems, creating privacy, security, financial, and regulatory risks. The proposed ownership model assigns policy-setting to a cross-functional group involving IT, security, legal, AI leaders, and business owners, while administrators enforce rules through identity-provider groups, inventories, and monitoring. Organizations are advised to begin by routing AI traffic through an approved gateway, restricting access according to roles, and sending detailed activity logs to a SIEM, then add agent ownership, DLP, spending limits, and revocable keys. The text also positions Barndoor as a platform that provides these governance capabilities for models, agents, MCP servers, data access, auditing, and spending management.

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