Editor’s note: This article was originally published by Craig Riddell on LinkedIn. It has been republished here with the author’s permission. Boards are giving AI security more airtime than ever. What they’re not giving is the right framing. A year or two ago, AI was mostly a question of experimentation risk. Today, it’s tied directly to revenue, customer experience, operational efficiency, and competitive advantage. The urgency is real, and it’s translating into aggressive deployment timelines.…
The Model Context Protocol (MCP) is a de facto standard for providing structured access to privileged systems for AI agents…
As API and AI adoption grows across the Middle East, so do the expectations around how data is handled. For…
Most organizations treating AI security as a model problem are defending the wrong layer. Security teams filter prompts, patch jailbreaks,…
Your legal team just handed you a 400-page document and said “figure out compliance.” The EU AI Act is live,…
Every secure API draws a line between code and data. HTTP separates headers from bodies. SQL has prepared statements. Even…
TL;DR AI risk doesn’t live in the model. It lives in the APIs behind it. Every AI interaction triggers a…
Dimitris Georgiou has been a self-professed computer geek since the early 80s. At university, he studied the convergence of educational…
Your board wants AI. Your developers are building with it. Your budget committee is asking for an ROI timeline. But…
AI systems are no longer just isolated models responding to human prompts. In modern production environments, they are increasingly chained…
