Every AI model is, at its core, a direct product of its training data. Long before a system answers its first prompt or makes its first real-world prediction, decisions about data collection, sourcing, and processing have already permanently shaped its behavior. ...
In Part 1 we looked at how little organizations actually know about where their AI training data comes from. This time, we look at what happens once that unverified data gets baked directly into a model. ...
Parts 1 and 2 of this series covered the data going into a model. This time we look at the technical choices made while training and optimizing it — decisions that introduce their own, separate set of security trade-offs. ...
So far this series has covered the data going into a model and the technical choices made while training it. Now: what happens when your organization doesn’t build a model at all, but fine-tunes someone else’s. ...
The first four parts of this series covered where AI training data comes from, what gets permanently baked into a model, how training and optimization choices introduce risk, and how fine-tuning inherits all of it. This part covers a harder problem: you often can’t check any of it yourself. ...
Understanding AI Frameworks and the Risks of Artificial Intelligence A blog post drawing on current frameworks, research, and the MIT AI Risk Repository Introduction Artificial Intelligence is no longer a niche technology. It is embedded in healthcare diagnostics, hiring decisions, financial systems, law enforcement tools, and the everyday software most of us use without a second thought. With that reach comes serious responsibility — and serious risk. Governments, standards bodies, researchers, and international organizations have responded by developing a growing ecosystem of frameworks designed to identify, categorize, and manage those risks. ...
The EU Artificial Intelligence Act: The World’s First Comprehensive AI Law A deep dive into the landmark regulation reshaping how AI is built and deployed globally What Is It? The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) is the world’s first comprehensive, legally binding regulation governing artificial intelligence. Enacted by the European Union, it establishes a common regulatory and legal framework for AI across all EU member states. ...
ISO/IEC 42001: The International Standard for AI Management Systems A deep dive into the world’s first certifiable AI governance standard What Is It? ISO/IEC 42001 is an international standard published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). It is the first international standard for managing AI systems responsibly — and unlike advisory frameworks, it is certifiable, meaning organizations can formally demonstrate compliance through accredited third-party audits. ...
The MIT AI Risk Repository: Mapping the Full Landscape of AI Risks A deep dive into the most comprehensive academic catalog of AI risks ever assembled What Is It? The MIT AI Risk Repository is a living research database developed by MIT FutureTech. It is not a governance framework, a regulation, or a standard — it is a research tool: a comprehensive, publicly accessible catalog of over 1,700 distinct AI risks, extracted from 74 existing frameworks, taxonomies, and academic classifications. ...
NIST AI Risk Management Framework (AI RMF 1.0): The U.S. Standard for Responsible AI A deep dive into the voluntary framework that became the de facto baseline for AI governance in the United States ...