Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A mid-sized SOC can have endpoint telemetry in one platform, cloud logs in another, vulnerability findings in a third, and identity alerts somewhere else entirely. Analysts switch consoles, normalize the same event repeatedly, and still miss the incident that required context from several systems. The problem usually isn't a lack of detection technology. It's the absence of a shared data model, disciplined correlation, and evidence that security and compliance teams can use together.

Have you ever tried to answer “Is this container safe to run?” If yes, then you know how tricky that question is. Securing containers is one of the toughest jobs in security, and hunting vulnerabilities across an estate of them gives security folks the same vibe as hunting in the fifth domain. Container vulnerabilities can live almost anywhere, from the image layers down to the kernel, and every week, a new base image, a new dependency, or a new manifest change opens a fresh gap.

Somewhere right now, someone is spinning up a fresh Kubernetes cluster, feeling pretty good with a basic firewall, skimmed a hardening guide, and maybe even applied a few CIS benchmarks. What could possibly go wrong? Roughly 18 minutes. That’s the average time before a newly exposed Kubernetes cluster receives its first malicious probe or attack attempt. So there’s zero room for error when it comes to securing Kubernetes.

Today, Cyberhaven is joining the Open Secure AI Alliance to help advance a future in which enterprises can adopt open-source AI without compromising security, compliance, or control over their data. Cyberhaven is betting on a world where companies are free to choose from many models, agent frameworks, and open harnesses, as well as run them on infrastructure they control. That choice matters. Enterprises will not standardize on a single model or AI platform.