AI Security for Business Data: Mastering NIST AI RMF, LLM Risk Management, Red Teaming & Data Privacy in the Era of Generative AI

Is AI actually secure for your business data? As artificial intelligence transitions from a novelty to a tool embedded in nearly 80% of business functions, the stakes for data security have never been higher. In this episode, we dive deep into the contemporary paradox of escalating AI capability and expanding vulnerability, exploring how your organization can harness AI safely without compromising its most sensitive assets.We move beyond the hype to examine the specific technical, operational, and data risks inherent in modern Large Language Models (LLMs) and agentic systems. From prompt injection and data poisoning to the "black box" problem and unintentional privacy leakage, we identify the failure modes that traditional cybersecurity measures often miss. You will learn why 91% of organizations believe they must do more to reassure customers that their data is handled legitimately within AI systems.Key topics we cover include:• The Blueprint for AI Governance: Why securing AI is a "collective responsibility" that extends from the C-suite to data scientists. We break down the roles of Chief Data Officers (CDOs) and CISOs in establishing a culture of risk management.• The NIST AI Risk Management Framework (AI RMF): A step-by-step guide to the four core functions—Govern, Map, Measure, and Manage—and how they provide a flexible foundation for building trustworthy AI.• Adversarial Resilience through Red Teaming: Discover the power of structured, proactive testing where expert teams simulate attacks to uncover vulnerabilities before malicious actors do. We discuss the latest tools like PyRIT, Garak, and Giskard used to stress-test your defenses.• Advanced Architectures for Factual Integrity: How Advanced Retrieval-Augmented Generation (RAG) and GraphRAG reduce hallucinations by nearly 43% compared to standard fine-tuning, ensuring your outputs are grounded in verifiable business facts.• The "30% Rule": Why dedicating 30% of your total AI resources to ongoing monitoring and maintenance post-deployment is essential to prevent model drift and performance degradation.• Defensive Prompt Engineering & Guardrails: Learn how to implement Zero Trust principles and real-time guardrails to screen inputs and outputs for PII exposure and jailbreak attempts.Whether you are navigating the EU AI Act compliance mandates or building custom internal AI agents, this episode provides the frameworks and best practices needed to turn AI into a secure competitive advantage. Join us as we bridge the gap between theoretical AI safety and practical, enterprise-grade security.Essential for: CISOs, CTOs, Data Architects, Compliance Officers, and any business leader looking to scale AI with confidence.