Model Online / On-Site
Duration
Modules 6 modules

Course Objective & Scope

As AI systems move into credit scoring, recruitment, customer interaction and autonomous workflows, regulatory compliance, data privacy, algorithmic bias and security become first-order operational risks. This multi-module academy translates the EU AI Act, ISO/IEC 42001 (AI Management System), NIST AI RMF and GDPR/KVKK into practical lifecycle controls that bridge legal, risk, security and engineering teams.

Participants work through AI system inventorying and risk tiering, Fundamental Rights and Data Protection Impact Assessments (FRIA/DPIA), technical assurance and red teaming (bias, robustness, OWASP LLM Top 10), third-party AI vendor due diligence and incident response—graduating with a reusable governance and assurance toolkit tailored to their organisation.

Who Should Attend?

  • Compliance, legal, enterprise risk management, internal audit and internal control professionals
  • Information security (CISO/AppSec), data governance and privacy (DPO) teams
  • Model risk management (MRM), responsible AI and AI product/platform leads
  • Business and technology leaders serving on an enterprise AI governance committee

Key Takeaways

  • Build an enterprise AI inventory and classify systems across EU AI Act risk tiers and provider/deployer obligations
  • Design lifecycle stage-gates, roles and accountabilities aligned with ISO/IEC 42001 and NIST AI RMF
  • Specify technical assurance, fairness, explainability and red-teaming requirements for both predictive ML and generative/agentic AI
  • Run structured third-party AI vendor due diligence covering training data rights, retention, security and model documentation
  • Deliver a ready-to-adopt AI governance charter, impact assessment template and AI incident response playbook

Curriculum

Regulatory and Standards Landscape: EU AI Act, ISO 42001 and NIST AI RMF
  • EU AI Act risk tiers (prohibited, high-risk, GPAI, transparency) and provider versus deployer duties
  • Mapping ISO/IEC 42001 clauses and NIST AI RMF (Govern, Map, Measure, Manage) into one control set
AI Inventory, Risk Tiering and Impact Assessments
  • Discovering and cataloguing in-house models, LLM workflows and embedded SaaS AI features
  • Executing Data Protection (DPIA) and Fundamental Rights Impact Assessments (FRIA)
Lifecycle Stage-Gates, Human Oversight and Documentation
  • Governance checkpoints from intake and design to deployment and decommissioning
  • Data lineage, model cards, system cards and audit-ready technical documentation
Technical Assurance: Fairness, Red Teaming and LLM/Agent Security
  • Testing for algorithmic bias, robustness, explainability and production drift
  • OWASP Top 10 for LLM Applications, adversarial red teaming and guardrail verification
Third-Party AI Procurement and Incident Response
  • Vendor due diligence checklists: zero data retention, IP indemnity, sub-processors and SLAs
  • AI incident response playbooks, rollback/kill-switch triggers and regulatory reporting
Capstone: Enterprise AI Governance and Assurance Blueprint
  • End-to-end risk and assurance review of a real predictive or generative AI use case
  • Presenting the governance charter, stage-gate checklist and control matrix

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