Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Most security teams treat a high volume of false positives as an analyst problem. Too many alerts, too little time, not enough headcount. So they add analysts, tune a few policies, and move on. That response is understandable, but it misdiagnoses the problem. When data loss prevention (DLP) false positive rates stay high over time, the issue is not a staffing gap. It is a detection accuracy problem, one that sits inside the tool, not the team.

Why do access control challenges exist, despite most companies following it? The gaps could be due to inconsistent permissions, accumulation of accesses, or poor management of user lifecycles. Access control is about governance. It answers two questions: “Who are you?” and “What are you allowed to do?” To add on, in today’s multi-cloud hybrid reality, governance is hard to handle. This isn’t another theoretical deep dive.

As AI systems become more capable and increasingly embedded into business operations, security teams are confronting a familiar challenge in a new form: speed without context. Vulnerability discovery is accelerating toward machine scale, while adversaries continue to adapt in real time. In response, the industry has gravitated toward data‑driven scoring models to help determine what deserves attention first.