Скачать или смотреть AI Vulnerability Management: Why You Can't Patch a Neural Network

Traditional vulnerability management is simple: find the flaw, patch it, and verify the fix. But what happens when the "asset" is a neural network that has learned something ethically wrong? In this episode, Sapna Paul (Senior Manager at Dayforce) explains why there are no "Patch Tuesdays" for AI models .

Sapna breaks down the three critical layers of AI vulnerability management: protecting production models, securing the data layer against poisoning, and monitoring model behavior for technically correct but ethically flawed outcomes . We discuss how to update your risk register to speak the language of business and the essential skills security professionals need to survive in an AI-first world .

The conversation also covers practical ways to use AI within your security team to combat alert fatigue , the importance of explainability tools like SHAP and LIME , and how to align with frameworks like the NIST AI RMF and the EU AI Act .

Questions asked:
00:00 Introduction
02:00 Who is Sapna Paul?
02:40 What is Vulnerability Management in the Age of AI?
05:00 Defining the New Asset: Neural Networks & Models
07:00 The 3 Layers of AI Vulnerability (Production, Data, Behavior)
10:20 Updating the Risk Register for AI Business Risks
13:30 Compliance vs. Innovation: Preventing AI from Going Rogue
18:20 Using AI to Solve Vulnerability Alert Fatigue
23:00 Skills Required for Future VM Professionals
25:40 Measuring AI Adoption in Security Teams
29:20 Key Frameworks: NIST AI RMF & EU AI Act
31:30 Tools for AI Security: Counterfit, SHAP, and LIME
33:30 Where to Start: Learning & Persona-Based Prompts
38:30 Fun Questions: Painting, Mentoring, and Vegan Ramen

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