Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Short correlation windows miss insider risk because misuse develops gradually, often over longer periods than detection models track. Short correlation windows miss insider risk because misuse often spans longer periods than detection models track. When context resets at fixed intervals, small behavioral changes fail to accumulate into visible risk. When context resets at fixed intervals, behavior is evaluated in disconnected segments.

Insider threats often don’t trigger alerts because the activity relies on valid credentials, approved tools, and authorized workflows. When viewed as individual events, this behavior looks normal and stays below traditional rule thresholds. Risk accumulates across otherwise valid actions without producing a signal that meets alert thresholds.

Every CISO has prepared for a budget conversation by building the strongest possible business case. The right data, the right framing, the right numbers. But the security leaders who consistently earn CFO support are not necessarily the ones with the most polished decks. They are the ones who built the relationship that made the ask credible before it ever landed on the table. That distinction came through clearly in a recent conversation between Exabeam CISO Kevin Kirkwood and Exabeam CFO Mike Byron.

By mid-2026, the question is no longer whether AI belongs in the enterprise. It’s already embedded in daily work, supporting research, development, customer engagement, and operations. AI agents now act on behalf of employees, automate decisions, and interact directly with enterprise data and systems. This shift creates a new security challenge.

Most security platform comparisons begin and end with the wrong number. Two vendors submit proposals. One comes in lower. Finance notes the delta, flags the savings, and the conversation shifts. What rarely makes it into that comparison is everything that determines what the platform actually costs once deployed, staffed, scaled, and operating effectively in production. That gap between sticker price and real cost is where security investment decisions quietly go wrong.

There is a huge amount of excitement right now about AI and security operations. Across the industry, we are seeing rapid innovation in areas such as behavioural analytics, AI-assisted investigation, and increasingly agent-based capabilities designed to help security teams process large volumes of activity more effectively. Security teams need that help. The scale of alerts, identities, and telemetry they must manage today has grown far beyond what humans alone can realistically handle.

The industry has spent the past few weeks focused on Claude Mythos Preview and the rise of autonomous offensive AI. As outlined in Claude Mythos, Project Glasswing, and the Machine-Speed Security Race, this shift is not only about faster attacks. The same AI-driven acceleration that helps attackers discover weaknesses faster can also help defenders validate exposure sooner. For security operations teams, the challenge is turning that strategic shift into action.

Every CISO has sat in a budget meeting where the conversation quietly pivoted from risk to price. Not because the chief financial officer (CFO) was being difficult. Not because security stopped mattering. But because at some point in the discussion, two platforms started to look identical, and when things look identical, cost becomes the deciding factor. That pivot is where security investment decisions go wrong. Security leaders do not lose budget because financial leaders undervalue security.

As autonomous and semi-autonomous AI systems take on more responsibility within the enterprise, they shift from being “features” of software to becoming true internal actors. They make decisions, take actions, call tools, orchestrate workflows, and influence other AI agents. With this evolution, we must confront an uncomfortable truth: the metrics and response patterns we built for deterministic software no longer work.

Behavior Intelligence is a security operations model that detects risk by analyzing behavior, automates investigation and response using AI, and measures whether security outcomes are improving over time. It focuses on how users, systems, and AI agents operate rather than relying only on predefined rules or knowns indicators of compromise. This shift matters because modern attacks rarely look malicious at first. They look normal.