The complete AI security checklist: from discovery to remediation

AI agents, MCP servers, and shadow AI are creating risk faster than traditional tools can track. This three-phase checklist gives security teams a practical, sequential framework to secure their AI environment, from initial asset discovery through continuous governance and compliance.

What you'll learn:

• How to inventory every AI model, agent, and MCP server across your environment, including shadow AI deployed without security review
• Why tracing sensitive data flows from source code to inference is the critical first step most teams skip
• How to detect compound risks that only emerge when data sensitivity, identity permissions, and AI agent behavior are correlated together
• A phased approach to move from discovery (phase 1) to contextual risk understanding (phase 2) to operational remediation and live compliance (phase 3)

This checklist is a companion to our AI Security Whitepaper. Use it as a practical action plan to close AI security gaps across code, cloud, and AI systems.

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