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AI for security engineers
The course links risk frameworks to SOC and engineering practices. You will turn policies into guardrails, detection content, and SLOs; and build with the platforms your teams run daily and prove effectiveness with measurable improvements in MTTA, MTTR, accuracy, and cost.
Course Content
AI risk differs from traditional IT risk because model behavior is probabilistic, highly sensitive to inputs, and dependent on training data and prompts. This demands controls that address data provenance, grounding, content policy, and continuous validation, not only perimeter security.
You will use NIST AI RMF to structure governance across Map, Measure, Manage, and Govern functions. CISA Secure by Design guides how you set secure defaults, limit attack surface, and log meaningfully. MITRE ATLAS provides adversary behaviors for AI-enabled systems so you can threat model realistically. OWASP LLM Top 10 frames misuse and design weaknesses such as prompt injection, data exfiltration, and insecure function calling.
The course links risk frameworks to SOC and engineering practices. You will turn policies into guardrails, detection content, and SLOs. You will build with the platforms your teams run daily and prove effectiveness with measurable improvements in MTTA, MTTR, accuracy, and cost.
Course Objectives
By the end of this course, you will be able to:
- Map SOC use cases to deployable AI patterns. You will be able to identify safe, high-value use cases and choose patterns such as retrieval augmented generation, sidecar assistants, and automation gates. Why it matters: You prioritize projects with the highest risk reduction and fastest cycle time.
- Design secure AI workflows with guardrails and policy enforcement. You will be able to implement content filters, data controls, grounding, and policy checks with full logging. *Why it matters: *You reduce prompt injection, data leakage, and overreliance risk.
- Implement platform-specific integrations on Microsoft, Google, Splunk, and Elastic. You will be able to connect AI components to your existing telemetry, identity, and workflow tools. Why it matters: You deliver value with systems your enterprise already uses.
- Apply NIST AI RMF, CISA Secure by Design, MITRE ATLAS, and OWASP LLM Top 10 to deployments. You will be able to map threats and controls to concrete build and run tasks. *Why it matters: *You align engineering choices to recognized controls and threat models.
- Validate AI outputs with automated tests and red teaming. You will be able to write assertions, build regression suites, and exercise adversarial prompts. Why it matters: You prevent regressions and keep trust in production.
- Instrument AI systems for auditability, performance, and cost with clear SLOs. You will be able to track accuracy, MTTA, MTTR, token cost, and data egress against budgets. Why it matters: You keep operations stable and predictable at scale.
- Operationalize change management and incident response for AI components. You will be able to handle model, prompt, and policy changes with rollbacks and runbooks. Why it matters: You minimize downtime and MTTR when models or prompts drift.
