Security teams are not struggling to find vulnerabilities. They are struggling to deal with them in a way that actually reduces risk. Most environments generate thousands of new findings every month. While vulnerability scanners, cloud tools, and endpoint platforms all contribute, that data does not come together in a way that is actionable. Teams end up with long lists of vulnerabilities, limited context, and no clear way to determine what should be fixed first.

In large enterprises, the hardest security decisions are rarely made in the SOC. They are made in board meetings, budget reviews, audit discussions, customer escalations. The most dire are often represented in the moments when leaders have to decide what matters now, what can wait, and what risk the business is actually taking on. The real GRC problem is no longer how to manage more work. It is how to help the business make better decisions with higher confidence. CISOs do not need another workflow.

There is a saying you will hear from veterans in the Black Hat Network Operations Center (NOC): “Threat hunting on the Black Hat network is like trying to find a needle in a stack of needles." With dozens of training classes running live exploit chains, capture-the-flag traffic, and researchers probing every corner of the internet, our Corelight sensors generate a rich set of Zeek logs, many of which can look suspicious in varying degrees.

In a landscape where executive teams demand immediate AI integration, engineering and security leaders find themselves navigating a complex operational balancing act. To explore how organizations can accelerate delivery pipelines without introducing fatal security risks, JFrog recently hosted a virtual panel discussion titled “Agentic Software Delivery in 2026.

Non-human identities (NHIs) authenticate pipelines, connect microservices, pull from secret managers, and provision cloud resources around the clock. They are also, for most security teams, almost completely invisible. Because there has never been a single place to see all of them at once.

AI coding agents are changing the pace of software development. With tools like Claude Code, developers can move from idea to implementation faster than ever, generating code, exploring unfamiliar repositories, refactoring services, and turning plain-language intent into working software. That speed is powerful. But speed without governance = risk. It also creates a new challenge: how can you govern what an AI agent builds, suggests, and pulls in from the internet?

One of the most common questions organisations ask when planning a security assessment is whether penetration testing should be performed against a staging environment or a live production system. At first glance, staging appears to be the safer option. It provides an environment where testing can be conducted without affecting real users, customer data, or operational services.

Least privilege is foundational. It's been a core security principle for decades, and it's no less relevant in agentic AI environments. An agent shouldn't hold permissions beyond what its task requires, and remediating over-permissioned agents is one of the highest-value quick wins available to any agentic AI security program. But here's what the security industry has been slow to acknowledge: correctly implemented least privilege still isn't sufficient.