As AI systems move into high-stakes enterprise and societal decisions, failures are no longer hypothetical—they are inevitable. Yet most organizations are unprepared to respond when AI systems behave unpredictably, produce harmful outputs, or operate outside intended constraints.

This session introduces a novel, practitioner-driven framework for managing AI failures and “rogue AI” scenarios through structured ethical decision-making. Rather than focusing on abstract principles, we present a real-world operational model that helps organizations detect, assess, and respond to AI failures in real time.

At the core of this session is the “Ethical Failure Response Model (EFRM),” a decision framework that integrates risk severity, stakeholder impact, reversibility, and system autonomy into a clear response strategy. Attendees will learn how to classify AI failure modes—including hallucinations, adversarial manipulation, emergent behaviors, and model drift—and apply appropriate containment and remediation actions.

We will also explore the emerging concept of “ethical blast radius,” helping leaders quantify the downstream impact of AI failures across customers, employees, and regulatory exposure. Through realistic scenarios, we will demonstrate how organizations can balance speed, accountability, and transparency when responding to AI incidents.

Key takeaways:

* A structured taxonomy of AI failure and rogue behavior patterns
* The Ethical Failure Response Model (EFRM) for real-time decision-making
* A practical method to quantify ethical and reputational impact (“blast radius”)
* Governance patterns for incident response, escalation, and auditability

This session is designed for CISOs, risk leaders, AI engineers, and product executives who need actionable strategies to manage AI failures responsibly—before they become organizational crises.