Welcome to "Securing the Future," the podcast dedicated to navigating the complex world of AI security. In this episode, we unpack the vital role of AI security frameworks—acting as instruction manuals—in safeguarding AI systems for multinational corporations.

These frameworks provide uniform guidelines for implementing security measures across diverse nations with varying legal requirements, from Asia-Pacific to Europe and North America.

We explore how these blueprints help organizations find weak spots before bad actors do, establish consistent rules, meet laws and regulations, and ultimately build trust with AI users. Crucially, they enable compliance and reduce implementation costs through standardization.

This episode delves into four leading frameworks:
NIST AI Risk Management Framework (AI RMF): We break down its comprehensive, lifecycle-wide approach, structured around four core functions: Govern, Map, Measure, and Manage.

This widely recognized framework is often recommended for beginners due to its clear steps and available resources. Its risk-based approach is adaptable for specific sectors like healthcare and banking, forming the backbone of their tailored safety frameworks.

Microsoft’s AI Security Framework: This framework focuses on operationalizing AI security best practices. It addresses five main parts: Security, Privacy, Fairness, Transparency, and Accountability. While integrating with Microsoft tools, its principles are broadly applicable for ensuring AI is used correctly and protected.

MITRE ATLAS Framework for AI Security: Discover this specialized framework that catalogues real-world AI threats and attack techniques. We discuss attack types like data poisoning, evasion attacks, model stealing, and privacy attacks, which represent “novel attacks” on AI systems. ATLAS is invaluable for threat modelling and red teaming, providing insights into adversarial machine learning techniques.

Databricks AI Security Framework (DASF) 2.0: Learn about this framework, which identifies 62 risks and 64 real use-case controls. Based on standards like NIST and MITRE, DASF is platform-agnostic, allowing its controls to be mapped across various cloud or data platform providers.

It critically differentiates between traditional cybersecurity risks and novel AI-specific attacks like adversarial machine learning, and bridges business, data, and security teams with practical tools.

We discuss how organizations can use parts from different frameworks to build comprehensive protection, complementing each other across strategic risks, governance, and technical controls.

Case studies from healthcare and banking illustrate how these conceptual frameworks are tailored to meet strict government rules and sector-specific challenges, ensuring robust risk management and governance.

Ultimately, AI security is an ongoing journey, not a one-off project. The key takeaway is to start small and build up your security over time.


For more information, read our “Best AI Security Frameworks for Enterprises” blog: