Read more about author Yuval Fernbach. As GenAI and machine learning (ML) become more widespread across industries, their high levels of adoption have created a major challenge: security. While every organization and IT team has its own security protocols and frameworks, many are starting to realize that traditional approaches aren’t enough when it comes to protecting themselves from the potential threats of artificial intelligence (AI) and machine learning systems. This…

Read more about author Sudhi Balan. More and more organizations are adopting generative AI (GenAI)-powered chatbots that have the power to streamline operations by automating repetitive tasks to improve efficiency and lead to faster, data-driven decisions. The capabilities of these chatbots to achieve greater accuracy are continually expanding, raising the standard for them to deliver more precise outputs that effectively harness all valuable data.  The problem is that many of…

Read more about author Shafeeq Ur Rahaman. The first time I was introduced to data governance, someone handed me a thick binder labeled “Data Policies.” It sat on my desk untouched for months. No one referred to it. No one followed it. It was a perfect symbol of what goes wrong when governance is reduced to paperwork without purpose. True data governance is more than policies – it’s how your…

Read more about author Guy Adams. Data products are proliferating in the enterprise, and the good news is that users are consuming data products at an accelerated rate, whether it’s an AI model, a BI interface, or an embedded dashboard on a website. The bad news is that too many data engineering teams still rely on manual methods to keep these data products running, which inhibits growth and the ability…