AI governance
- Definition
- The organizational frameworks, policies, and processes that govern how AI systems are developed, deployed, monitored, and retired within an enterprise. AI governance covers model risk management, bias auditing, access controls, and regulatory compliance.
- Why it matters
- Without governance, AI deployments create shadow risk that compounds over time. Models drift, training data goes stale, access controls get bypassed, and nobody knows which version of which model is making which decisions. The EU AI Act, NIST AI RMF, and emerging state-level legislation are turning governance from a nice-to-have into a legal requirement. Companies that build governance infrastructure early will move faster when regulations tighten, while those that treat it as an afterthought will face costly retrofits, fines, and reputational damage. The Chief AI Officer role exists because someone has to own this.
- In practice
- JPMorgan Chase built a centralized AI governance board that reviews every model before production deployment, tracking over 300 AI use cases. The EU AI Act, which took effect in phases starting August 2024, requires risk classification, conformity assessments, and incident reporting for high-risk AI systems. Microsoft published a Responsible AI Standard with six principles and mandatory impact assessments. Smaller companies are adopting tools like Credo AI and Holistic AI to automate governance workflows, cutting compliance documentation time from weeks to days.
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Related terms
Responsible AI
A framework for developing and deploying AI systems that are ethical, transparent, and accountable. Responsible AI practices are becoming table stakes for enterprise procurement and regulatory compliance.
AI safety
The interdisciplinary field focused on ensuring AI systems behave as intended and do not cause unintended harm. Encompasses alignment research, red teaming, content filtering, and policy advocacy.
Red teaming
The practice of systematically probing an AI system to find vulnerabilities, biases, and failure modes before deployment. Red teaming is now standard practice at major AI labs and increasingly required by regulation.
Responsible scaling policy
A governance framework that ties the deployment of increasingly capable AI models to demonstrated safety evaluations, creating commitments about what safety conditions must be met before a model can be released or scaled.
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