Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Exabeam expands Behavior Intelligence to address risks introduced by agentic AI. This release introduces open-source projects for agent verification and telemetry, expanded AI observability with Anthropic Claude support, more than 50 new Agent Behavior Analytics (ABA) detections (bringing total to 90), Exabeam Nova Content Creator, and OWASP Agentic Top 10 coverage scoring in Outcomes Navigator, enabling teams to continuously verify, observe, analyze, and improve AI agent security.

Approved access shouldn’t mean you’re done waiting. For most developers, it just means the friction is about to start. You request access to a database. It gets approved. Now what? You open the portal, navigate to your request, find the session, click into Access Details, hunt for the right tab, copy a hostname, switch to your database client, create a new connection profile, paste in the hostname, go back for the username, go back for the password. And finally, connect. Whew.

Identities, permissions, exposed resources, and sensitive data can all contribute to risk regardless of whether they reside in AWS, Microsoft Azure, or Google Cloud. However, security teams often encounter uneven visibility and coverage across disparate cloud environments, and face difficulty in consistently understanding risk across a multi-cloud estate.

Corelight Sensor v29.1 and Fleet Manager v29.1.1 fundamentally expand what a Corelight Sensor delivers. The release turns existing network evidence into a shared source of truth for SecOps, NetOps, triage, and forensic investigation. Network performance monitoring and asset classification unlock new value from traffic you're already collecting.

India’s data protection landscape is changing. With the Digital Personal Data Protection Act bringing new compliance obligations to the fore, businesses across the country are being forced to think more...

Frontier AI is poised to change cybersecurity faster than most organizations can adapt. It’s accelerating vulnerability discovery, which puts new pressure on security teams to handle more vulnerabilities, in less time, with workflows built for much slower technology. The primary challenge of the frontier AI era is not the increase in vulnerabilities. It’s understanding which exposures are most critical and how to address them before adversaries target them.

Identity security has long been built around a simple premise: Authenticate a user, grant access, and trust that decision until their next login. While for many this model worked well enough when identities were primarily human and access patterns were predictable, that’s no longer the case for humans and definitely not the case for AI agents.

Right now, somewhere in your organization, a service account token is sitting in a CI/CD environment variable with access to your entire cloud environment. The job it was created for got deleted three sprints ago, and nobody knows it's still there.

A new CVE drops into a package you depend on. With the JFrog power for Kiro installed, your next move is a single prompt in your IDE, not a tab switch to the JFrog UI and thirty minutes of hand-rolled REST calls. This is what governed agentic development looks like in practice.

Modern IT and security teams no longer evaluate platforms in isolation. They ask how a platform fits into the architecture they run, the workflows they trust, and the outcomes they need to improve. Enterprise stacks are not isolated; they are interdependent. Identity shapes access, endpoint posture influences policy, while SIEM tools drive investigations and rely on shared data and context. AI tools introduce new layers and patterns of usage, risk, and data movement across the network.