Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every security leader can describe their network security architecture in confident detail: how traffic should flow, where segmentation boundaries sit, which access should never be permitted. What almost none can tell me with certainty is whether their live controls are actually enforcing that design right now, at this very hour. That gap between what we intend and what is actually running in production is where modern breaches live, and it widens with every change we make.

AI agents now retrieve data, generate recommendations, and trigger actions across enterprise systems with little human review in between. That speed is the point, and it is also the problem. A single manipulated prompt or a poisoned data source can push an AI system toward a decision no one signed off on, and most security teams have never tested for it. Building a red team exercise for AI workflows is how you find that gap before an attacker does.

On July 7, 2026, the European Central Bank sent a letter to the CEO of every bank it directly supervises with an unambiguous instruction: build a formal action plan against AI-enabled cyberattacks, and submit it to your supervisory team by October 31.

Managing an artificial intelligence (AI) footprint has emerged as one of the most complex challenges for modern enterprise security and risk teams. As shadow AI, autonomous agents, and embedded third-party models infiltrate corporate environments, traditional methods of software tracking have broken down. Organizations are quickly realizing that maintaining an accurate inventory is not just an IT best practice.