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GenAI Security Engineering

L5-L6 · 8 courses · 114 chapters

Engineer defenses against prompt injection, jailbreaks, and data exfiltration. Implement PII leakage detection, content safety, and compliance.

What you'll learn

Core responsibilities this discipline prepares you for.

Conduct adversarial red-team testing

of LLM systems

Automate red-teaming with Garak for prompt injection, jailbreak, and data extraction probes
Run multi-turn adversarial campaigns with Meta GOAT and structured vulnerability reporting
Execute campaigns against realistic GenAI systems, discover attack vectors, and produce actionable reports

Implement defense-in-depth guardrails

— input validation, output filtering, content safety

Layer NeMo Guardrails, Llama Guard 4, Prompt Guard 2, and Model Armor into a unified defense stack
Configure multi-layer input validation, output filtering, and content classification policies
Measure the safety-vs-helpfulness tradeoff across different defense layer configurations

Threat-model GenAI agent systems

— analyze attack surfaces across tools, memory, and inter-agent communication

Analyze MCP security boundaries, memory manipulation vectors, and inter-agent trust relationships
Map tool access control surfaces and agent communication channel vulnerabilities
Threat-model a complete multi-agent system, identify attack vectors, and design targeted mitigations

Build PII protection

— detect, classify, and redact sensitive data in LLM pipelines

Integrate Presidio for multi-language PII detection with custom entity recognizers
Implement masking vs. pseudonymization redaction strategies with compliance validation
Configure PII protection for a RAG pipeline and verify zero sensitive data leakage in outputs

Design compliance programs

aligned with OWASP LLM Top 10, MITRE ATLAS, EU AI Act

Map OWASP LLM Top 10 mitigations to specific technical controls and implementation patterns
Implement MITRE ATLAS threat taxonomy and NIST AI RMF compliance frameworks
Create compliance mappings for GenAI systems and design repeatable audit procedures

Build security monitoring

for GenAI systems

Build security-specific monitoring dashboards with anomalous prompt pattern detection
Detect data exfiltration attempts, unusual token patterns, and adversarial input signatures
Monitor a production-like GenAI system and detect simulated attacks in real time

Implement incident response

for GenAI security events

Build GenAI-specific incident response playbooks with severity classification and containment procedures
Design forensic analysis workflows for LLM interactions and post-incident reporting
Simulate security incidents and practice the full end-to-end response lifecycle

Secure GenAI supply chain

— model provenance, dependency scanning, container security

Verify model integrity with provenance checks and scan dependencies for known vulnerabilities
Design secure CI/CD pipelines with container image scanning and signing for GenAI deployments
Audit a complete GenAI application supply chain and implement security controls at each stage

Your learning path

8 courses · sequenced for compounding · 114 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate12 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Advanced16 Ch

Step 5

GenAI Agent Engineering

Build production-grade agents with hosted LLMs — planning, tools, memory, evaluation, and orchestration patterns.

Advanced14 Ch

Step 6

GenAI Evaluation, Safety & Governance

Evaluate, red-team, and govern GenAI systems — offline evals, online metrics, safety guardrails, compliance.

Advanced10 Ch

Step 7

GenAI Operations

Run GenAI in production — monitoring, dunning, incident response, cost control, and the on-call runbook.

Advanced12 Ch

Capstone

AI Security Engineering

Harden GenAI applications against prompt injection, data exfiltration, and the full OWASP LLM threat model.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

Guardrails AI

Validate + filter every model output

NeMo Guardrails

Enforce conversation-layer safety

Llama Guard

Open-source content moderation

Presidio

PII detection + redaction in pipelines

OWASP ZAP

Fuzz your LLM app endpoints

K8s NetworkPolicy

Isolate agents from the internet in labs

OpenAI API

Baseline model for attack-simulation labs

Anthropic API

Claude for adversarial reasoning labs

Langfuse

Audit trail for every policy violation

Argilla

Label adversarial examples in labs

Rebuff

Detect prompt injection in input filters

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