Lilly Tech Systems
Designing AI Security Architecture
Build production-grade security for AI systems — from defending against prompt injection and data exfiltration to securing models against adversarial attacks and achieving SOC2/HIPAA compliance. Learn the security patterns that security engineers and ML engineers use to protect AI systems handling real user data.
Your Learning Path
Follow these lessons in order to build a complete AI security architecture from scratch, or jump to any topic you need right now.
1. AI Security Threat Landscape
OWASP Top 10 for LLMs, AI-specific attack vectors, threat modeling for AI systems, security architecture layers, and real-world breach examples that changed the industry.
2. Prompt Injection Prevention
Direct vs indirect injection, detection techniques using classifiers, canary tokens, and input sanitization. Defense-in-depth architecture, output validation, and a production injection detection pipeline.
3. Data Privacy Architecture
PII detection and redaction, differential privacy for ML, data anonymization pipelines, GDPR/CCPA compliance for AI, data retention policies, and a production PII filtering pipeline.
4. Access Control for AI Systems
API key management, OAuth for AI APIs, RBAC for model access, per-user rate limiting, usage auditing, multi-tenant isolation, and a production authentication middleware.
5. Model Security
Model theft prevention, adversarial attack defense, model watermarking, secure model serving, supply chain security with model provenance, and model signing verification.
6. Compliance & Governance
SOC2 for AI, HIPAA for healthcare AI, EU AI Act requirements, audit logging design, model cards and documentation, and risk assessment frameworks for production AI.
7. Best Practices & Checklist
Complete AI security checklist, penetration testing for AI systems, incident response for AI breaches, and a comprehensive FAQ for security engineers protecting AI infrastructure.
What You'll Learn
By the end of this course, you will be able to:
Threat Model AI Systems
Identify and prioritize AI-specific threats including prompt injection, data poisoning, model theft, and adversarial attacks using the OWASP LLM Top 10 framework.
Build Security Pipelines
Implement production injection detection, PII redaction, auth middleware, and audit logging in Python that you can deploy into your AI systems this week.
Protect Models & Data
Defend against adversarial attacks, prevent model extraction, implement model watermarking, and build data privacy pipelines that satisfy GDPR and CCPA requirements.
Achieve Compliance
Design audit logging, model documentation, and governance frameworks that satisfy SOC2, HIPAA, and EU AI Act requirements for production AI systems.