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