GenAI Security Engineering
Engineer defenses against prompt injection, jailbreaks, and data exfiltration. Implement PII leakage detection, content safety, and compliance.
What a GenAI security engineer does · GenAI Security Engineering
Assume an attacker is already typing. Prompt injection arrives through a retrieved document, a jailbreak routes around the system prompt, a tool call exfiltrates data the model was never meant to see, and a poisoned dependency shows up in the model supply chain. GenAI security engineering is the defense-in-depth answer: adversarial red-team testing, layered guardrails with Guardrails AI, NeMo Guardrails and Llama Guard, PII protection with Presidio, threat models for agent systems, Kubernetes network policies around tool sandboxes, and security monitoring in Langfuse that turns an attack into an alert. Compliance programs and incident response close the loop. Where safety and evaluation measures whether the model behaves, you make sure nobody can force it not to.
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8 courses, each building on the last. The bar shows each course's share of the curriculum. 45 lessons are free to read.
Python Essentials for Agent Builders 8.4% of the curriculum
In scope for GenAI Security Engineering: chapters 1–13 — 62 of this path’s 734 lessons. 1 free GenAI Security Engineering lesson
LLM Foundations for Agent Builders 8.2% of the curriculum
In scope for GenAI Security Engineering: chapters 1–6, 14–15, 17–20 — 60 of this path’s 734 lessons. 1 free GenAI Security Engineering lesson
Kubernetes Essentials for GenAI Engineers 8.2% of the curriculum
In scope for GenAI Security Engineering: chapters 1–4, 7–12 — 60 of this path’s 734 lessons. 1 free GenAI Security Engineering lesson
Web APIs & Services for GenAI Engineers 8.2% of the curriculum
In scope for GenAI Security Engineering: chapters 1–10 — 60 of this path’s 734 lessons. 1 free GenAI Security Engineering lesson
GenAI Agent Engineering 30.1% of the curriculum
In scope for GenAI Security Engineering: chapters 11–47 — 221 of this path’s 734 lessons.
GenAI Evaluation, Safety & Governance 15.5% of the curriculum
In scope for GenAI Security Engineering: chapters 1, 4, 7–23 — 114 of this path’s 734 lessons.
GenAI Operations 4.9% of the curriculum
In scope for GenAI Security Engineering: chapters 45–49, 62 — 36 of this path’s 734 lessons.
AI Security Engineering 16.5% of the curriculum
- Prompt Injection DefenseBuild prompt injection classifier using LLM-as-judge via LiteLLMImplement input sanitization pipeline with NeMo GuardrailsDetect indirect injection in RAG-retrieved documentsBuild defense-in-depth with layered guard chainDeploy injection defense as FastAPI sidecar on GKEMonitor injection attempts with Prometheus and Grafana
- Jailbreak Prevention
- Output Sanitization EngineeringDeploy output sanitizer as response middleware on GKE
- Content Safety PipelinesDeploy multi-stage safety pipeline on GKE
- Multimodal Injection DefenseDeploy multimodal defense on GKE with resource limits
- RAG Data Poisoning DefenseDeploy canary documents for tampering detectionDeploy RAG defense system on GKE with pgvector
- AI Supply Chain SecurityEnforce supply chain policies in GKE with Binary Authorization
- PII Leakage EngineeringBuild bidirectional PII redaction pipelineIntegrate PII defense with LiteLLM gatewayDeploy PII defense pipeline on GKE
- Embedding & Vector Store SecurityImplement row-level access control for vector storesImplement embedding integrity and provenance trackingEncrypt embeddings at rest and in transitDeploy secure vector store on GKE with network isolationMonitor vector store access patterns and anomalies
- Agentic AI SecurityEnforce least-privilege for agent tool accessDetect and contain rogue agent behaviorDeploy agent security monitor on GKETest agent security with adversarial scenarios
- MCP Protocol SecurityDetect tool poisoning in MCP tool descriptionsImplement MCP server authentication and authorizationSecure agent-to-agent communication channelsDeploy secure MCP infrastructure on GKEAudit MCP security with automated testing
- GKE Security for AI WorkloadsConfigure GKE network policies for AI service isolationImplement GKE Workload Identity for secure service authMonitor GKE security posture continuously
- API Security for LLM EndpointsImplement OAuth2/OIDC authentication for LLM APIsDeploy LLM API gateway on GKE with LiteLLM
- Secrets & Key Management for AIDeploy secrets infrastructure on GKE with Workload Identity
- Threat Modeling for AI Systems
- Automated AI Red TeamingTest agent security with DeepTeam vulnerability scans
- Security Monitoring for AIMonitor agent behavior for security anomaliesDeploy security monitoring stack on GKE
- AI Incident ResponseDeploy incident response automation on GKE
- AI Security Compliance EngineeringDeploy compliance monitoring on GKE
- Security Engineering CapstoneDeploy security infrastructure on GKE
Core responsibilities this discipline prepares you for. Open one to see how you build it.
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
Each group becomes a signed node on your public skill graph — a recruiter-auditable record of the labs you actually passed. Share the URL; they see the discipline, the labs, and the signature.
Run the offense program: AI threat modeling (STRIDE-for-LLMs, attack trees), OWASP-LLM-Top-10 / MITRE ATLAS fluency, red-team automation, and adversarial test campaigns — the discipline that frames every other defense.
Attack and defend the prompt surface: direct and indirect/cross-domain injection (via retrieved or tool content), jailbreak/refusal-bypass, red-teaming content filters, and output-encoding to neutralize downstream injection.
Stop data getting out under attack: exfiltration via prompt/output/tool side-channels, system-prompt leakage, and PII-leak detection — the adversarial channels, not corpus or in-feature redaction.
Defend the model itself as an asset: membership-inference, training-data extraction, and model-stealing attacks — and the defenses against them.
Catch the attack and respond to the breach: adversarial anomaly/abuse detection, attack forensics and containment, and post-breach response — not operational on-call or outage IR.
Attack the agent: confused-deputy and tool-poisoning, excessive-agency exploitation, malicious-MCP-server attacks, privilege escalation through tool chains, and sandbox-escape testing.
Poison and defend the retrieval layer: corpus/embedding poisoning, malicious-document injection, retrieval manipulation, and detection vs prevention — attacking the index, not building it.
Secure the AI supply chain and abuse surface: model provenance/signing, malicious-model-file scanning, AI dependency/SBOM, and abuse/token-flooding DoS — not generic endpoint or org-secrets hardening.
Baseline provider access in security tooling: LLM/embedding SDK calls, auth, and retries.
Production Python for security tooling: async, typing, parsing, and error handling.