Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Written AI governance policies and ethics charters look reassuring on paper—until a customer-facing AI assistant fabricates binding financial advice, leaks confidential records via prompt injection, or triggers a regulatory inquiry. Modeled after cybersecurity war-gaming, this 1-2 day interactive tabletop simulation brings together executives, legal, compliance, security, and AI product leads to stress-test their governance controls against escalating crisis scenarios.

As timed scenario injects unfold, cross-functional teams must make real-time operational, legal, and communication decisions: When do we pull the kill switch? What audit logs exist? Who notifies regulators under GDPR or the EU AI Act? Participants leave having uncovered blind spots in their current governance posture and having co-authored a practical AI Incident Response Playbook.

Who Should Attend?

  • Executives and technology leaders including CIOs, CISOs, CTOs, CDOs, and Heads of AI
  • Legal counsel, compliance officers, Data Protection Officers (DPOs), enterprise risk, and internal audit leads
  • Product owners and engineering managers operating customer-facing or high-impact internal AI systems
  • Corporate communications and crisis response leaders

Key Takeaways

  • Expose hidden gaps in AI approval gates, monitoring, and ownership through realistic pressure-tested scenarios
  • Practice cross-functional decision-making during hallucination incidents, data leaks, biased automated decisions, and rogue agent actions
  • Apply regulatory obligations under the EU AI Act, GDPR/KVKK, and sector regulations regarding risk classification, logging, and incident reporting
  • Establish clear RACI decision rights, severity tiers, and kill-switch protocols across Product, IT, Security, Legal, and PR
  • Leave with a customized AI Incident Response Playbook and pre-deployment governance checklist

Curriculum

The Enterprise AI Risk Landscape and Regulatory Baseline
  • How probabilistic AI and autonomous agents differ from traditional software and cybersecurity incidents
  • Operationalizing the EU AI Act risk tiers, GDPR/KVKK data privacy rules, and sector-specific mandates
  • Mapping vulnerabilities across Shadow AI, third-party model APIs, RAG permissions, and tool-calling agents
Scenario 1 (Tabletop): Viral Hallucination and Contractual Liability
  • Simulated crisis: a customer-facing AI assistant invents binding policy concessions and screenshots go viral
  • Real-time injects: deciding between partial throttling vs. full kill switch, handling legal exposure, and drafting public statements
  • Hot-wash debrief: identifying missing guardrails, disclaimers, and pre-launch red-teaming checks
Scenario 2 (Tabletop): Indirect Prompt Injection and Sensitive Data Exfiltration
  • Simulated crisis: an internal RAG/agent tool ingests a poisoned document and exposes restricted payroll or client records
  • Containing the breach, reconstructing audit logs, and evaluating 72-hour regulatory breach notification triggers
  • Hot-wash debrief: fixing document ACL inheritance, tool-calling permissions, and runtime telemetry
Scenario 3 (Tabletop): Algorithmic Bias Allegation and Regulatory Audit
  • Simulated crisis: a whistle-blower or regulator challenges an AI-assisted screening or scoring system for discriminatory outcomes
  • Assembling model documentation, evaluation datasets, human-oversight logs, and impact assessments under time pressure
  • Hot-wash debrief: closing documentation and continuous monitoring gaps for high-risk AI systems
Authoring the AI Incident Response Playbook and Go-Live Gates
  • Translating simulation lessons into AI incident severity definitions (Sev 1-4) and escalation trees
  • Locking in the cross-functional RACI matrix and first-24-hour response checklists
  • Establishing lightweight yet mandatory pre-production AI Go/No-Go governance gates

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