GenAI Security Engineering
Engineer defenses against prompt injection, jailbreaks, and data exfiltration. Implement PII leakage detection, content safety, and compliance.
Verifiable skill graph
10 skill groups · each becomes a signed node on your graph.
Verifiable skill graph
10 skill groups · each becomes a signed node on your graph.
Every lab you pass signs a W3C Verifiable Credential on your public skill graph. Completing the labs in each group below mints one node on that graph — the badge you walk away with is a cryptographic record of what you can ship, not a completion certificate.
Share the URL on your résumé or with a hiring manager. They click; they see the discipline, the labs you passed, and the verification signature. No honor system, no broker.
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.
What you'll ship in production
Core responsibilities this discipline prepares you for.
What you'll ship in production
Core responsibilities this discipline prepares you for.
- 1
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
- 2
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
- 3
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
- 4
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
- 5
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
- 6
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
- 7
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
- 8
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
Curriculum
8 courses · each builds on previous goals
Curriculum
8 courses · each builds on previous goals
13 goals unlocked for preview — click to read. Locked goals need a subscription.