Hernan Huwyler is a Global Risk Management Executive and the Academic Director at IE Law and Business School (Executive Education). With a dual background as an MBA and CPA, Hernan specializes in the intersection of Law, Technology, and Quantitative Risk.

He focuses on "translating" complex regulatory frameworks (EU AI Act, GDPR, ISO 42001) into actionable architectures for engineering and business teams. Unlike traditional compliance officers, Hernan advocates for data-driven risk management, moving beyond "heatmaps" to utilize Monte Carlo simulations, convolution models, and Python-based risk quantification.

Hernan has led Risk, Control, and Audit functions for major multinational corporations across Europe and the Americas. He writes and speaks extensively on Adversarial AI, Algorithmic Auditing, and the financial quantification of Cyber Risk. Past keynote venues include Risk Awareness Conferences (>2,000 risk managers), IIA and ISACA Summits, ProcureCom Europe, IKN Madrid, IE University, and closed-door sessions for international organizations and national regulators. Hernan’s talks consistently rank in the top 5% of conference feedback scores because he makes complex security requirements, business needs and regulations feel like a competitive advantage.

Speaking Topics:

AI Governance: Implementing ISO 42001 and navigating the EU AI Act.
Risk Quantification: Why "Impact x Probability" fails and how to use convolution models.
Adversarial AI: Balancing transparency with security to prevent data poisoning.
AI ROI: Risk-adjusted financial modeling for Generative AI deployments.

Ready-to-Submit Talk Proposals:

Session 1: The Technical/Quant Talk

Title: Kill the Heatmap: Quantifying AI Risk with Python & Monte Carlo Simulations
Abstract: Most organizations manage AI risk using colorful 5x5 "heatmaps." This is mathematically flawed and dangerous for high-stakes AI deployment. In this talk, we will ditch the gut-check methodology. I will demonstrate how to use convolution and Monte Carlo simulations to model the "tail risk" of AI hallucinations and regulatory fines. You will leave with a method to calculate the actual financial exposure of your models—no expensive GRC software required, just math and logic.

Session 2: The Governance/Security Talk

Title: The Transparency Trap: How to Audit AI Without Creating Vulnerabilities
Abstract: New standards like ISO/IEC 12792 demand AI transparency, but in the age of Adversarial Machine Learning, "oversharing" is a security vector. If you reveal your full architecture and training sources, you invite data poisoning and model inversion attacks. This session outlines a "Tiered Disclosure Architecture." We will discuss how to route specific data to auditors, regulators, and users differently, ensuring compliance with the EU AI Act without handing the keys to bad actors.

Session 3: The Business/Strategy Talk

Title: The Real Cost of Intelligence: A Risk-Adjusted Framework for AI ROI
Abstract: Companies are deploying Generative AI based on productivity hype, but they are ignoring the "OpEx Iceberg"—model drift, inference costs, and liability reserves. This talk presents a financial framework for calculating the true ROI of AI. We will cover how to factor in the "Risk Delta" (new risks vs. automated risks), calculate the Levelized Cost of AI (LCOAI), and determine when to build vs. buy based on process uniqueness. A session for leaders who need to justify the cloud bill to the CFO.