Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A sales rep opened Glean—an AI-powered enterprise search platform that connects to your company's SaaS apps and lets anyone query across all of them in natural language—typed "Who are my top 10 customers?" and got a clean, formatted list pulled from Salesforce, cross-referenced with HubSpot, and confirmed against data sitting in Google Drive. They copy-pasted that list into a personal Gmail draft. No alerts fired. No policies triggered. No one noticed. This isn't a hypothetical.

If you’re like me, you’ve been enthralled with the recent story, expertly written by Sean Hollister at The Verge, about how Sammy Azdoufal built a remote control for his DJI Romo vacuum with a PlayStation controller, and ended up in control of 7,000+ robovacs all over the world. On the surface, it sounds like vibe coding gone slightly sideways. I mean, really, what could a vacuum possibly do? Turns out… a lot.

AI coding assistants are quietly resurrecting millions of abandoned open source packages. For the last decade, developers relied on a simple heuristic for open source security: Prevalence \= Trust. If a package was downloaded millions of times a week (lodash, react, requests), we assumed it was "safe enough" because thousands of eyes were on it. If it was obscure, we approached with caution.

AI tools are quickly becoming part of everyday business workflows. From chatbots to automation tools, large language models now handle sensitive tasks and data. But with this growth comes new security risks. One of the biggest emerging threats is the prompt injection attack, in which attackers manipulate inputs to cause AI systems to ignore their original instructions. Unlike traditional cyberattacks, this method exploits weaknesses through language rather than code.

AI compliance ensures AI systems follow laws, ethics, and standards by managing risks like bias, privacy violations, and lack of transparency through robust governance, documentation, and continuous monitoring, using frameworks like the EU AI Act and NIST AI Risk Management Framework (RMF) to build trust and avoid penalties in developing, deploying, and operating AI.