A practical approach to inventorying AI, classifying risk, assigning accountability and establishing controls without slowing useful adoption.
AI governance works best when it is treated as an operating system for responsible adoption rather than a document-production exercise. The goal is to know where AI is used, what data it touches, who is accountable and which controls apply.
Record sanctioned and discovered AI tools, models, agents, owners, business purpose, data categories, integrations and user populations. Without inventory, governance remains theoretical.
Use a simple risk model that considers business impact, data sensitivity, autonomy, external exposure, regulatory relevance and potential harm. Higher-risk use cases should receive stronger review and monitoring.
Assign business ownership, technical ownership, security/privacy review and approval authority. Agentic AI also needs explicit boundaries for actions, credentials and human oversight.
Typical controls cover acceptable use, identity, data handling, prompt and model security, third-party assessment, logging, human-in-the-loop requirements, incident handling and evidence retention.
Track new tools, policy exceptions, incidents, control failures and material changes to models or integrations. Governance should evolve with adoption.
Talk with EAKA IT about a focused assessment or implementation roadmap.