Governance that keeps pace with shipped AI
Policies age in weeks once agents touch tools and data. Governance needs owners, telemetry, and release discipline — not slide decks.
Inventory and classification
Know every production model and workflow automation: inputs, prompts, tools, downstream systems, and materiality. Classification drives testing depth, logging retention, and approval paths.
Lifecycle controls
Version models, prompts, retrieval corpora, and evaluations together. Tie releases to risk review when behavior changes, not only when infrastructure changes. Pair this with the knowledge-layer guidance in RAG readiness.
Frequently asked questions
What is the minimum logging we need for auditability?
At least: who invoked the system, which model and prompt template version, retrieved sources, tool calls, final output, and escalation path. Retention should match your regulatory and contractual obligations.
Who should own AI governance day to day?
Pair a business sponsor with a technical lead and a second-line risk or legal partner. Central Enablement can set standards; domains must own outcomes in production.
How often should models be re-evaluated?
Whenever inputs drift, tools change, regulation shifts, or incidents occur — plus a regular cadence proportional to materiality (monthly for high-risk systems, quarterly for lower tiers).
Next step: a fixed-fee diagnostic.
Three weeks. Board-ready brief. Ranked opportunities. No discovery theatre.
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