Start with use-case mapping
Every AI initiative does not carry the same level of risk. Customer-facing copilots, internal knowledge assistants, and model-enabled workflow automation all have different abuse paths and control needs.
Map where prompts originate, what data can be retrieved, what tools models can call, and what outputs can trigger external action.
Focus on runtime failure points
Prompt injection, sensitive-data exposure, and unsafe tool execution are common points of failure. Addressing those areas first usually delivers stronger risk reduction than writing broad policy without technical enforcement.
Make governance operational
AI governance works when ownership is clear, logging is available, and reviews happen on a cadence tied to product change. Security should be part of the release model, not a once-a-year checkpoint.