In this episode, we’re diving into one of the most critical challenges in artificial intelligence—data security. From supply chain risks and maliciously modified data to data drift that can quietly erode accuracy, protecting information throughout the AI system lifecycle is essential.

We’ll explore insights from global cybersecurity agencies, including best practices and mitigation strategies designed to safeguard the integrity of data that powers AI and machine learning systems. Because in the end, the quality and security of data determine the trustworthiness of AI itself.

So, let’s unpack how securing data can strengthen the future of AI.

In this episode, we explore the NIST Artificial Intelligence Risk Management Framework, also known as the AI RMF 1.0. Released in January 2023, this free resource from NIST is designed to help organizations manage the unique risks of AI while promoting responsible and trustworthy use.

We’ll break down the seven characteristics of trustworthy AI—like safety, security, accountability, fairness, and more—and dive into the four core functions: Govern, Map, Measure, and Manage. These principles guide organizations through the entire AI lifecycle, ensuring AI systems are not only powerful but also reliable and ethical.

So, if you’re looking to strengthen your understanding of AI risk management and build trust in the future of AI, you’re in the right place. Let’s get started with the NIST AI RMF 1.0.

In this episode, we’re diving into how Goldman Sachs, one of the world’s leading investment banks, manages cyber risk. Forget the buzzwords—this is about real-world strategies in operational resilience, business continuity, and disaster recovery. You’ll hear how these practices protect clients, stabilize markets, and keep the firm running through disruption. Our goal? To give you a clear shortcut to understanding Goldman’s multi-layered approach to digital security and operational stability.