AI Governance
AI governance is the set of policies, processes, and organizational structures that define how AI systems are developed, deployed, monitored, and retired within an enterprise. It covers accountability, risk management, compliance, ethics, and operational standards.
How It Works
Building an AI system is a technical problem. Running it responsibly in an enterprise is a governance problem. AI governance answers questions like: Who is responsible when the AI makes a wrong decision? How do we ensure the system treats all users fairly? What happens when regulations change?
A governance framework typically covers several areas. Data governance defines what data can be used for training and inference, who has access, and how privacy is maintained. Model governance tracks which models are deployed, their performance, and their known limitations. Operational governance sets standards for monitoring, incident response, and human oversight.
Regulatory pressure is making AI governance non-optional. The EU AI Act classifies AI systems by risk level and imposes requirements on high-risk applications. Similar regulations are emerging in other regions. Enterprises that deploy AI without governance structures will face compliance gaps.
In practice, AI governance often starts with an AI use case review process. Before a team can deploy an AI system, it goes through a review that evaluates risk, data sensitivity, fairness implications, and required safeguards. This does not need to be slow or bureaucratic. The best governance processes are lightweight for low-risk applications and thorough for high-risk ones.
Good governance also means ongoing monitoring. Models can degrade over time as the data they encounter changes. A governance framework includes regular performance reviews and criteria for when a model needs to be retrained or replaced.
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