Focuses on AI security, guardrails, governance, and GPU FinOps, complementing rather than duplicating cybersecurity programs.
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// AIINFRA 302 · Semester 4
AI Security, Guardrails & Governance
Securing, Governing, and Cost-Optimizing Production LLM Systems
This hands-on course prepares learners to secure, govern, and financially optimize generative AI and LLM systems in production. Students master the OWASP LLM Top 10 (2025), build layered defenses against prompt injection and jailbreaks, and deploy modern guardrail frameworks such as NVIDIA NeMo Guardrails, Meta Llama Guard 4, and Microsoft Presidio for PII protection. The course also covers AI governance frameworks including the NIST AI RMF, ISO/IEC 42001, and the EU AI Act, along with responsible AI, bias, and fairness practices. Learners finish with GPU FinOps skills to measure and reduce inference cost per 1,000 requests using spot instances, fractional GPUs, quantization, and autoscaling economics.
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01 / outcomes
Outcomes
Course objectives
- Assess LLM applications against the OWASP LLM Top 10 (2025) and construct layered defenses against direct and indirect prompt injection, jailbreaks, and data leakage.
- Deploy and configure production guardrail frameworks (NeMo Guardrails, Llama Guard 4, LLM Guard, Rebuff, and Presidio) to enforce input/output validation and PII redaction.
- Implement AI security controls for data privacy, secrets and access management, model provenance, and supply-chain integrity across the model lifecycle.
- 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.
- 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.
Student learning outcomes
- Assess LLM applications against the OWASP LLM Top 10 and build layered defenses against prompt injection, jailbreaks, and data leakage.
- Deploy and configure production guardrail frameworks such as NeMo Guardrails, Llama Guard 4, LLM Guard, Rebuff, and Presidio for input/output validation and PII redaction.
- Implement AI security controls for data privacy, secrets and access management, model provenance, and supply-chain integrity.
- Evaluate AI systems against the NIST AI RMF, ISO/IEC 42001, and EU AI Act, producing model cards and risk and fairness documentation.
- Calculate and optimize GPU inference economics using cost per 1,000 requests, spot/fractional GPUs, quantization, and autoscaling.
02 / schedule
16-week schedule
Wk 01
AI Security Landscape and the LLM Threat Model
Introduces the AI security landscape and establishes a threat model for LLM applications.
Wk 02
OWASP LLM Top 10 (2025) Deep Dive
Provides a deep dive into the OWASP LLM Top 10 (2025) risk categories for LLM applications.
Wk 03
Direct and Indirect Prompt Injection Attacks
Covers direct and indirect prompt injection attack techniques against LLM applications.
Wk 04
Prompt Injection Defenses and Input Validation
Covers layered defenses and input validation strategies against prompt injection attacks.
Wk 05
Jailbreaks, Red-Teaming, and Adversarial Testing
Covers jailbreak techniques and red-teaming/adversarial testing methods for LLM systems.
Wk 06
Guardrails Frameworks: NeMo Guardrails and LLM Guard
Covers deploying and configuring the NeMo Guardrails and LLM Guard guardrail frameworks.
Wk 07
Content Safety and Moderation with Llama Guard 4 and Rebuff
Covers content safety and moderation using Llama Guard 4 and Rebuff.
Wk 08
PII Protection with Presidio and Output Validation
Midterm week: covers PII protection with Microsoft Presidio and output validation, alongside the course midterm exam covering weeks 1-7.
Midterm · covers Wks 1–7Wk 09
Data Privacy, Secrets Management, and Access Control
Covers data privacy practices, secrets management, and access control for AI systems.
Wk 10
Model Provenance and AI Supply-Chain Security
Covers model provenance tracking and AI supply-chain security controls.
Wk 11
AI Governance Frameworks: NIST AI RMF, ISO/IEC 42001, and the EU AI Act
Covers evaluating AI systems against the NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
Wk 12
Responsible AI, Bias, Fairness, and Model Cards
Covers responsible AI practices, bias and fairness assessment, and producing model cards.
Wk 13
GPU FinOps Foundations: Cost per 1,000 Requests and Right-Sizing
Introduces GPU FinOps foundations, including cost per 1,000 requests and right-sizing inference deployments.
Wk 14
Fractional GPUs, MIG, Quantization, and KV-Cache Savings
Covers cost savings from fractional GPUs, NVIDIA MIG, quantization, and KV-cache tuning.
Wk 15
Autoscaling Economics, Spot Instances, and Build-vs-Buy
Covers autoscaling economics, spot instance usage, and build-vs-buy decisions for inference.
Wk 16
Capstone Project & Course Review
Final capstone week: students deliver a project tying together security, governance, and cost optimization, and review the course.
Capstone03 / Senate-ready controls
Course evidence and boundaries
These controls make the curriculum reviewable without changing the 10-course sequence.
Threat model, guardrail test, governance mapping, cost/right-sizing memo, and remediation plan.
OWASP LLM Top 10 mapping, prompt-injection test, secrets/access review, model provenance, and incident response note.
Governance memo, security scan or red-team report, cost model, accessibility note, and executive-ready risk summary.
04 / foundry projects
Foundry-backed assignments
These assignments are written in platform-agnostic language. They assess infrastructure evidence, not familiarity with a single vendor console.
Flat-Fee vs Metered Compute Memo
Students compare shared annual capacity against metered GPU, token, and managed-service pricing for cohort workloads including inference, RAG, voice, and adapter training.
Evidence: Cohort assumptions, workload estimates, utilization target, break-even analysis, governance risks, and pilot-size recommendation.
05 / tools
Tools & frameworks
Security & Red-Teaming
Guardrails
Content Safety
PII & Privacy
Structured Output & Validation
Governance Frameworks
Documentation & Fairness
Supply-Chain & Provenance
Serving & GPU Optimization
FinOps & Observability