AIINFRA 302: AI Security, Guardrails & Governance — Outcomes & Rubrics

Course Learning Outcomes

By the end of this course, students will be able to:

1. CLO1 — Assess LLM applications against the OWASP LLM Top 10 (2025) and construct layered defenses against direct and indirect prompt injection, jailbreaks, and data leakage.
2. CLO2 — Deploy and configure production guardrail frameworks (NeMo Guardrails, Llama Guard 4, LLM Guard, Rebuff, and Presidio) to enforce input/output validation and PII redaction.
3. CLO3 — Implement AI security controls for data privacy, secrets and access management, model provenance, and supply-chain integrity across the model lifecycle.
4. CLO4 — Evaluate AI systems against the NIST AI RMF, ISO/IEC 42001, and EU AI Act, producing model cards, risk documentation, and bias and fairness assessments.
5. CLO5 — Calculate and optimize GPU inference economics using cost per 1,000 requests, spot and fractional GPUs, quantization, KV-cache tuning, and autoscaling to make build-vs-buy decisions.

Together, CLO1–CLO5 map to the certificate's program-level outcomes by building the applied AI infrastructure, security, and governance skills that California employers expect of AI infrastructure and architecture practitioners.

Rubrics

1. Lab / Hands-on Assignment Rubric