AWS AI Security Best Practices Cheat Sheet

After reading this cheat sheet, you’ll be able to:

• Secure AI model development and deployment with AWS-native guardrails and monitoring.

• Build IAM policies and org-wide guardrails that prevent overprivileged access to AI services.

• Protect inference endpoints—both public and private—from model abuse, data leaks, and prompt injection.

• Vet and quarantine third-party models to reduce supply-chain risk.

• Embed responsible AI governance that aligns with AWS, NIST, and regulatory frameworks.

• Extend AWS-native monitoring with Wiz AI-SPM for unified visibility and attack-path analysis across clouds.

Is this cheat sheet for you?

This guide is for CISOs, cloud security leaders, and security professionals who are building and deploying AI applications using AWS managed-AI services. It is essential for those who recognize that default AWS security controls and general cloud security tools are often not enough to address the unique risks posed by AI development environments.

What’s inside?

• Secure Model Development (data, model, artifacts).

• Enforce Least-Privilege IAM using SCPs and granular permissions.

• Secure Inference Endpoints via network isolation (PrivateLink) and layered defenses (WAF/API Gateway).

• Protect Third-Party Models by scanning and quarantining external model files.

• Prioritize Responsible AI with governance, Model Cards, and Bedrock Guardrails.

• Implement Strong Monitoring using CloudTrail, Model Monitor, and GuardDuty across the AI lifecycle.