Enterprise AI Security Checklist
Enterprise AI deployments fail security review for predictable, preventable reasons. This checklist covers every security, governance, and compliance consideration we encounter in enterprise AI deployments - from data perimeter design to prompt injection testing, access control architecture to audit trail requirements. It's the document we review before every production launch.
What's inside
Section 1: Data perimeter and access control (12 items) - Where data goes, who can access it, how it's controlled
Section 2: LLM and model risk (8 items) - Prompt injection, jailbreak resistance, output filtering
Section 3: Audit and logging requirements (10 items) - What to log, how to store it, retention requirements
Section 4: Third-party provider security (7 items) - Evaluating OpenAI, Azure OpenAI, Anthropic, and others
Section 5: Compliance mapping (9 items) - GDPR, HIPAA, SOC2, ISO 27001 considerations for AI systems
Section 6: Incident response (5 items) - AI-specific incident procedures and escalation paths
What you'll get
Complete 51-item security checklist covering all six domains above
Data flow diagram template for documenting where data moves in an AI system
Prompt injection test suite: 20 test cases for evaluating LLM application robustness
Audit log schema template: what fields to log for regulatory compliance
Third-party AI provider security comparison: OpenAI, Azure OpenAI, Anthropic, Google on key enterprise criteria
GDPR and HIPAA quick-reference for AI-specific considerations
Who this is for
CISOs and security teams evaluating enterprise AI deployments
Compliance teams assessing AI system regulatory risk
Engineering teams preparing for security review of production AI systems
Procurement teams evaluating AI vendor security posture
Free to read
Enterprise AI Security Checklist
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