Enterprise AI Security Architecture: Complete Guide

Q: What are the biggest AI security threats?

Top threats include prompt injection (direct and indirect), data poisoning, model extraction, PII leakage through outputs, jailbreaking, and supply chain attacks through compromised model weights or training data.

Q: How do you prevent prompt injection attacks?

Layer multiple defenses: input sanitization, instruction hierarchy (system > user prompts), output filtering, canary tokens, rate limiting, and response validation. No single defense is sufficient — defense in depth is essential.

Q: What is an AI threat model?

An AI threat model maps attack surfaces specific to AI systems: training data integrity, model endpoints, prompt interfaces, tool-use capabilities, and output channels. Standard STRIDE/DREAD frameworks are extended with AI-specific threats.

Q: Should you encrypt AI model weights?

Yes, for proprietary models. Encrypt weights at rest and in transit, use secure enclaves for inference on sensitive data, implement access controls on model artifacts, and audit model access logs regularly.

Q: What compliance frameworks apply to AI?

Key frameworks include EU AI Act (risk-based), NIST AI RMF, ISO 42001 (AI Management System), SOC 2 + AI controls, HIPAA (healthcare AI), and sector-specific regulations. The EU AI Act is the most comprehensive to date.

Satyam Kumar

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Enterprise AI Security Architecture (Beyond Basics)

March 2, 202627 min read

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Satyam

AI & Cloud Architect. Helping teams build systems that scale to millions.

Enterprise AI Security Architecture (Beyond Basics)

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