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Start With AI Ownership
Every enterprise AI program needs named owners before it needs more tools. Assign an executive sponsor, a day-to-day platform owner, security owner, legal or compliance reviewer, finance owner, and department owners for each major business unit. The goal is to avoid a common failure mode: IT buys an AI tool, security worries about data leakage, finance sees unexpected cost growth, and business teams keep adopting tools without a shared operating model.
Build an AI Inventory
Create a current inventory of approved and unapproved AI usage. Include ChatGPT, Claude, Gemini, Microsoft Copilot, Google Gemini for Workspace, browser extensions, meeting bots, AI writing tools, AI coding tools, model APIs, internal agents, and tools connected through OAuth. For each item, record owner, users, data touched, model provider, retention terms, authentication method, and whether usage is logged. You cannot govern tools and agents you cannot see.
Classify AI Use Cases by Risk
Separate low-risk drafting and summarization from higher-risk workflows involving customer data, employee data, financial decisions, legal review, clinical information, production code, security actions, or external communications. Use a simple tiering model: allowed, allowed with controls, requires review, and prohibited. This makes policy understandable for non-technical teams and helps avoid one-size-fits-all governance that blocks harmless work while missing high-risk usage.
Define Data Rules
Set plain rules for what data may be used with which AI tools. Public information and non-sensitive internal content may be allowed in more places. Customer PII, PHI, employee records, financial information, secrets, legal matter information, and unreleased strategy should require approved environments, masking, or blocking. Connect these rules to sensitive data protection so the policy is enforced in the moment, not only written in a document.
Control Access and Model Choice
Not every team needs the same models or the same privileges. Use role-based access to decide who can use expensive frontier models, who can create workflows, who can connect data sources, who can deploy agents, who can approve exceptions, and who can view audit logs. Model access should reflect task risk, cost, and department needs rather than defaulting every employee to every available model.
Log the Evidence You Will Need Later
Audit logs should capture user identity, model, tool, timestamp, prompt category, data protection events, policy decisions, cost, and administrative changes. For higher-risk workflows, logs may also need prompt and response records with appropriate privacy controls. The practical question is simple: if legal, security, finance, or an auditor asks what happened, can you reconstruct the decision without guessing?
Set Budgets Before Usage Scales
AI cost governance works best before spend becomes political. Set budgets by department, workspace, model tier, or project. Alert managers before limits are reached, review high-cost workflows, and route routine tasks to cheaper models when quality does not suffer. Department budgets make AI spending visible to the teams creating the demand, which is the first step toward sustainable adoption.