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AI for GRC analysts
Learn how to link GRC frameworks to SOC and engineering realities. You will build a practical playbook for translating AI risk frameworks into controls, evidence, monitoring expectations, and audit-ready reporting, and a short implementation plan you can execute with product, platform, security, and compliance partners.
Course Content
AI risk is not only about code and configurations. Models adapt with data and user behavior, so governance must account for intent, context, and continuous feedback. You will apply a lifecycle view that starts at problem framing and runs through deployment, monitoring, and retirement.
The NIST AI RMF gives the anchor language across Govern, Map, Measure, and Manage. CISA Secure by Design informs secure defaults, supplier expectations, and deployment patterns. MITRE ATLAS supplies adversarial tactics for threat modeling and testing. OWASP Top 10 for LLMs drives concrete application mitigations. EU AI Act and ISO 42001 clarify documentation, transparency, and management system obligations that shape evidence and oversight.
You will link these frameworks to SOC and engineering realities to build a practical playbook for translating AI risk frameworks into controls, evidence, monitoring expectations, and audit-ready reporting, and a short implementation plan you can execute with product, platform, security, and compliance partners.
Course Objectives
By the end of this Course, you will be able to:
Map AI Risks To Your Control Framework You will be able to align AI risks to existing enterprise controls to govern consistently across products and vendors. Why it matters: reduces fragmented assurance and speeds decisions.
Select And Justify Safeguards For Model, Data, And Integration Risks You will be able to show risk reduction and budget value for each safeguard. Why it matters: leaders see clear tradeoffs and fund the right controls.
Design measurable guardrails and service level objectives (SLOs) You will be able to set accuracy, latency, token cost, and privacy thresholds with auditable service level objectives (SLOs). Why it matters: teams know when to ship, throttle, or roll back.
Validate Controls With Evidence. You will be able to link tests, results, and exceptions to risk acceptance. Why it matters: audits pass on first review and exceptions stay traceable.
Validate Monitoring and Incident Response You will be able to define monitoring requirements, review evidence of misbehavior or drift detection, and track mean time to acknowledge (MTTA) and mean time to resolve (MTTR). Why it matters: faster containment and fewer customer impacts.
Evaluate Platform Capabilities Wisely You will be able to document vendor-neutral requirements and evaluate platform capabilities as evidence sources without creating tool lock in. Why it matters: you get coverage and flexibility.
Report AI Risk Posture To Executives You will be able to connect risk to enterprise objectives and regulatory expectations. Why it matters: informed decisions and sustained compliance.
