Read more about author Veronika Trukhan. Being overwhelmed by ever-increasing volumes of data, businesses currently struggle to store, organize, govern, and utilize it efficiently, which results in data breaches, inaccuracies, analytics errors, and other data-related issues. To overcome these complexities and maximize the value of enterprise data for companies, business leaders seek new, more efficient ways to manage their information, which drives the popularity of such technologies as data fabrics…

Read more about author Samuel Bocetta. The U.S. National Institute of Standards and Technology (NIST) has launched its comprehensive Cybersecurity, Privacy, and AI program, marking a pivotal moment in how organizations must prepare for AI-infused cyber and privacy risks. NIST’s key concern is how advancements in the broad adoption of AI may impact current cybersecurity and privacy risks and risk management approaches. This initiative aims to harmonize AI risk management…

Read more about author Nahla Davies. Synthetic data sounds like something out of science fiction, but it’s fast becoming the backbone of modern machine learning and data privacy initiatives. It enables faster development, stronger security, and fewer ethical headaches – and it’s evolving quickly.  So if you’ve ever wondered what synthetic data really is, how it’s made, and why it’s taking center stage in so many industries, buckle up. You’re about…

Read more about author Gautam Singh. The enterprise AI landscape has reached an inflection point. After years of pilots and proof-of-concepts, organizations are now committing unprecedented resources to AI, with double-digit budget increases expected across industries in 2025. This isn’t merely about technological adoption. It reflects a deep rethinking of how businesses operate at scale. The urgency is clear: 70% of the software used by Fortune 500 companies was developed over two decades…