This course prepares practitioners to secure, govern, and economically operate production LLM systems. Students assess real-world AI applications against the OWASP LLM Top 10, deploy layered guardrail and content-safety frameworks, and implement privacy, provenance, and supply-chain controls across the model lifecycle. The course closes with AI governance frameworks (NIST AI RMF, ISO/IEC 42001, EU AI Act) and GPU inference FinOps, culminating in a capstone that ties security, governance, and cost optimization together into a single deployable system.
54 contact hours (3-unit equivalent), delivered over a 16-week term, approximately 3.4 hours/week. This is a non-credit course on the CDCP Certificate of Completion pathway.
AIINFRA 301 (LLM applications, RAG, and deployment; AIINFRA 301 itself requires AIINFRA 300).
By the end of this course, students will be able to:
| Week | Topic |
|---|---|
| 01 | AI Security Landscape and the LLM Threat Model |
| 02 | OWASP LLM Top 10 (2025) Deep Dive |
| 03 | Direct and Indirect Prompt Injection Attacks |
| 04 | Prompt Injection Defenses and Input Validation |
| 05 | Jailbreaks, Red-Teaming, and Adversarial Testing |
| 06 | Guardrails Frameworks: NeMo Guardrails and LLM Guard |
| 07 | Content Safety and Moderation with Llama Guard 4 and Rebuff |
| 08 | PII Protection with Presidio and Output Validation (Midterm) |
| 09 | Data Privacy, Secrets Management, and Access Control |
| 10 | Model Provenance and AI Supply-Chain Security |
| 11 | AI Governance Frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act |
| 12 | Responsible AI, Bias, Fairness, and Model Cards |
| 13 | GPU FinOps Foundations: Cost per 1,000 Requests and Right-Sizing |
| 14 | Fractional GPUs, MIG, Quantization, and KV-Cache Savings |
| 15 | Autoscaling Economics, Spot Instances, and Build-vs-Buy |
| 16 | Capstone Project & Course Review (Capstone) |
| Component | Weight |
|---|---|
| Labs | 40% |
| Discussions | 10% |
| Weekly Quizzes | 15% |
| Midterm | 15% |
| Final Capstone | 20% |
Credit/No-Credit, 70% to pass.
Academic integrity: Students are expected to submit their own work. Collaboration on concepts is encouraged, but labs, quizzes, the midterm, and the capstone must reflect individual understanding and effort. Submitting others' work as your own, or fabricating security assessments or governance documentation, constitutes a violation of the LACCD academic integrity policy and may result in a failing grade. Late work: Assignments are due as posted in Canvas. Late submissions are accepted up to 7 days after the due date with instructor notification; work submitted after that window may not be accepted absent documented extenuating circumstances. Contact your instructor as early as possible if you anticipate a delay. Responsible use of AI: This course is about securing and governing AI systems, so responsible, transparent use of AI tools in coursework is expected and modeled. Students may use AI assistants (including LLMs) to support learning, drafting, and debugging, but must disclose significant AI assistance on graded work and remain accountable for verifying accuracy, security, and originality of anything submitted. Accessibility: This course is committed to full inclusion of students with disabilities. Students needing accommodations should contact the campus Disabled Students Programs and Services (DSPS) office as early in the term as possible so that reasonable accommodations can be arranged. All course materials are designed to meet applicable accessibility standards; please notify the instructor of any barriers encountered.
All tools are free or free-tier.
Notion ID: 392c08fd-0278-8101-868f-d32dd45a7ca6 Notion URL: https://app.notion.com/p/392c08fd02788101868fd32dd45a7ca6