Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

An AI governance framework tells a financial institution to inventory its systems, assess risk and document decisions. Consumer protection law tells it something different and considerably harder, which is that a model unable to produce specific reasons for a credit denial cannot lawfully be used to make one. ‍ That distinction is what separates AI governance in financial services from AI governance generally.

Healthcare carries the highest average breach cost of any industry and has for well over a decade, and it operates under a rule that has required risk analysis since 2003. Those two facts sit uncomfortably together, and federal regulators have started saying why. ‍ Enforcement has moved from asking whether an organization performed a risk analysis to asking what it did about the findings.

Browser-layer AI monitoring produces events, and the natural next step is forwarding them to the security operations center. Consider what they arrive into. Industry research for 2026 puts false positives at close to half of all alerts, with around forty-two percent going entirely uninvestigated. ‍ Browser AI events are behavioral anomaly alerts, and behavioral anomaly alerts are the category analysts already deprioritize, precisely because they are noisy by nature.