Lilly Tech Systems
Introduction to AI Risk Management
AI systems introduce novel risks that traditional risk management frameworks were not designed to address. Understanding these risks and how to manage them is critical for responsible AI deployment.
Why AI Risk Management Matters
AI systems are increasingly making or influencing decisions that affect people's lives — from loan approvals and hiring decisions to medical diagnoses and criminal justice. The consequences of AI failures can be severe and far-reaching:
- Harm to individuals: Biased AI systems can discriminate against protected groups, denying opportunities or services unfairly
- Organizational liability: Companies deploying harmful AI face regulatory fines, lawsuits, and reputational damage
- Systemic risks: Widespread adoption of flawed AI models can create cascading failures across industries
- Regulatory compliance: The EU AI Act, NIST AI RMF, and other frameworks increasingly require formal risk management
- Trust and adoption: Without proper risk management, public trust in AI erodes, slowing beneficial adoption
Categories of AI Risk
| Risk Category | Description | Examples |
|---|---|---|
| Fairness & Bias | Systematic errors that create unfair outcomes for specific groups | Hiring algorithms that discriminate by gender, lending models biased by race |
| Safety & Reliability | System failures that can cause physical or psychological harm | Autonomous vehicle misclassification, medical AI misdiagnosis |
| Privacy | Unauthorized collection, use, or exposure of personal data | Training on PII, model memorization, inference attacks |
| Security | Vulnerabilities that can be exploited by adversaries | Adversarial examples, model extraction, data poisoning |
| Transparency | Inability to explain or understand AI decisions | Black-box models in regulated industries, lack of documentation |
| Accountability | Unclear responsibility for AI outcomes | No human oversight, undefined escalation paths, missing audit trails |
The Regulatory Landscape
EU AI Act
The world's first comprehensive AI regulation. Classifies AI systems by risk level (unacceptable, high, limited, minimal) and imposes requirements accordingly, including conformity assessments for high-risk systems.
NIST AI RMF
A voluntary framework from the U.S. National Institute of Standards and Technology providing structured guidance for managing AI risks through MAP, MEASURE, MANAGE, and GOVERN functions.
Executive Order 14110
The U.S. Executive Order on AI Safety requires federal agencies to manage AI risks, establishes reporting requirements for frontier AI models, and directs NIST to develop AI safety standards.
ISO/IEC 42001
The international standard for AI management systems, providing a certifiable framework for organizations to establish, implement, and continuously improve their AI governance practices.