Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A vendor sends over a SOC 2 report. It lands in the queue, someone reads the cover page, sees the auditor's name and a clean-looking opinion letter, and marks the assessment complete. The reviewer moves on to the next vendor. Multiply that by a few hundred vendors a year, and it becomes less of a decision and more of a reflex.

Endpoint detection, cloud security posture management, email security, identity and access management, network segmentation. Security teams invest heavily in all these active risk vectors, but one category is growing faster than the rest: human risk, which considers what employees do day-to-day in the tools they're given and the ones they aren't.

Security and engineering teams contend with a constant stream of signals about vulnerabilities, incidents, misconfigurations, identity risks, control gaps, and other findings across their environments. But an individual finding’s severity does not always reflect its potential organizational impact.

Organizations have invested heavily in technologies that detect suspicious activity. Yet insider incidents continue to rise, and security teams are often left to investigate isolated alerts without sufficient context to determine whether an event poses a meaningful risk. Behavioral analytics helps close that gap.

Gartner predicted in mid-2025 that more than forty percent of agentic AI projects would be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Treat the figure as a forward-looking estimate rather than a measurement, since canceled projects tend to be quietly renamed, absorbed or left to lapse rather than formally closed. ‍

Quantifying cyber risk in an enterprise IT environment starts from frequency. Incidents of a given type happen at some rate, that rate is observable across enough organizations to be estimated, and severity follows from what was affected. ‍ Operational technology inverts both halves. Frequency data barely exists, and the consequences are already documented in detail by people who have never thought about cyber. Working with that inversion rather than against it is what makes the modeling tractable.

It's 11:40 am. A security questionnaire just hit your inbox. You open the file and quickly realize you can't finish this alone. Legal needs to review the data processing language. Product has to complete the architecture section. Security is the only team that can sign off on incident response. So you split up the questionnaire, Slack each department their section to answer, and wait... and wait... and wait.

Not every threat matters equally to every organization. A newly discovered APT or ransomware group targeting European automotive manufacturers is definitely worth paying attention to, especially if you’re in that sector. But if you’re a financial services firm in California, it’s probably not an immediate priority. You might look into it, track its activity, and assess whether it could become relevant.

Black Hat USA 2026 wrapped in Las Vegas with a clear verdict: the industry has stopped debating whether AI changes the threat landscape and started building for the fact that it already has. Nearly a third of the conference's briefings dealt directly with AI security, agent exploitation, or LLM-assisted offensive research — and the conversations happening in partner meetings and dinners around the show floor tracked right alongside it.