data governance

AI applications in regulated sectors such as healthtech face growing pressure to prove they are safe, explainable and compliant, without slowing down ...

A wrong decision can cost money. Most organizations know this. But what is often forgotten is that making no decision at all can sometimes be even more ...

Important decisions about your data pipeline that separate an AI pilot from a system in production. Every AI project arrives, sooner or later, at the same ...

For most of the past decade, the business case for data spaces has rested on architectural models and individual pilot projects. Both were necessary. Neither ...

Ask what makes AI responsible, and most people point to the model's answers: is the output accurate, is it fair, is it drifting over time. These are real ...

Cybersecurity has fundamentally changed. Not slightly, and not gradually. The combination of increasingly complex infrastructure, AI-driven attacks, and a ...

More and more companies are running AI systems in production, ranging from predictive models to autonomous agents that perform tasks themselves. Yet the ...

Most organizations are not limited by what their models can do. They are limited by which data they are allowed to use. That is a governance problem before it ...

My work as a journalist and copywriter has changed significantly with the advent of AI. Where I would often have to scan long reports and other documents in ...

Every week, it seems a new language model is released that is supposedly smarter, faster, or cheaper than everything that came before it. At least, that’s what ...