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.

You track your web applications. You inventory your APIs. But is anybody monitoring your AI servers? Just last week research found that there were more than 175,000 exposed versions of Ollama, an AI server popular for self-hosting LLMs. Across enterprises, self-hosted model servers are being deployed on cloud VMs and GPU-backed instances to power copilots, internal automation, and experimental AI features.

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 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.