Five Pillars. Zero Blind Spots.
Every AI approval your security team makes can look reasonable in isolation, and still add up to enterprise-wide exposure. This checklist breaks AI risk management into five operating pillars, from shadow AI discovery to continuous monitoring, plus four accountability requirements every program needs. Use it to see where your coverage already holds and where the gaps sit.
What's inside
Visibility Into Every AI Tool and Data Flow
Maintain a continuous inventory of sanctioned and unsanctioned AI tools, including embedded copilots and agents employees adopt on their own.
Track usage intensity across tools, not just adoption, and flag fast-growing endpoint agents that carry an outsized share of data exposure.
Trace where sensitive data originated, how it moved across systems and prompts, and where it shaped an AI-generated output.
Risk-Based Policy and Point-of-Use Enforcement
Replace static allow-or-block rules with policies calibrated to data sensitivity, tool type, user role, and decision impact.
Enforce safeguards at the moment of AI interaction, not after sensitive data has already left the organization.
Apply controls consistently across endpoints, browsers, and cloud applications so coverage doesn't stop at the browser.
Continuous Monitoring and Clear Accountability
Monitor AI systems on an ongoing basis and investigate anomalies as data, prompts, and adversarial pressure change.
Feed monitoring results back into policy and governance updates instead of treating AI security as a one-time launch task.
Assign risk ownership, define escalation paths, and bring in reviewers independent from the system's developers and operators.