Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every security vendor shipping an AI product in 2026 makes the same promises. Faster triage. Shorter response times. Fewer false positives. Reclaimed analyst hours. But, six months after deployment, most security leaders still cannot answer a straightforward question from the board: Is this thing actually working?

Most organizations have a reasonable handle on their sanctioned SaaS apps. Model Context Protocol - hit 10,000 public servers within a year of launch, with 97 million monthly SDK downloads. None of those numbers capture the servers your developers configured locally. Those don't appear in any registry. They were added at the IDE level, one developer at a time, with no approval step and nothing that touches a central system. That's the inventory problem. It comes before any question of enforcement.

AI agents fall into five foundational categories: simple reflex, model-based reflex, goal-based, utility-based, and learning agents. Each is defined by how much environmental awareness and decision-making complexity the system can handle, from fixed condition-action rules to feedback-driven self-improvement.

A patch management policy is a formal organizational document that defines how software updates are identified, prioritized, tested, and deployed across an organization's systems to address security vulnerabilities that attackers commonly exploit to gain initial access.

This post covers a supply chain attack introducing a capability not seen before: using an AI coding assistant’s own GitHub access to commit malicious code to a corporate repository.