Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The platform determines what is known. AI helps determine what it means. Technology solution providers across virtually every industry are racing to add AI, in some fashion, to their offerings. Vulnerability management providers aren’t immune, either. They’re racing just as fast as the rest of them, sometimes faster. These vendors are embedding copilots, assistants, and agents powered by technologies like Anthropic Claude, OpenAI GPT, and Google Gemini.

Remote code execution (RCE) lets an attacker run commands on a system they don't own, usually through a flaw in a web application, CMS, VPN portal, or API. A successful exploit inherits the permissions of the vulnerable service, and many services run with far more access than they need. AI shortens the time from a disclosed flaw to a working exploit and lets attackers test thousands of exposed systems at once. A remote code execution flaw turns a vulnerable server into someone else's computer.

Any SCA tool worth its salt has reachability analysis these days. When a dependency has a known vulnerability, these tools can tell you whether your code calls the affected function. This is pretty much non-negotiable because it helps teams focus their security efforts instead of sifting through noise. Every other step in prioritizing dependency CVEs builds on it. But just because the vulnerable function is reachable doesn’t mean it’s exploitable.

On October 8, 2026, Anthropic announced OSS Scanner, a free, opt-in vulnerability scanning service for eligible open-source projects. The OSS Scanner is powered by their most capable models, including Claude Mythos. It scans codebases, identifies vulnerabilities, proposes patches, and sends reports directly to maintainers. As you read this, it has already identified real, high-severity issues in production software used by millions.

On a recent call, a security lead walked us through two rules his team enforces on every build. Both are sensible. Together, they left his developers with a red pipeline and no version of a library they were allowed to use. The first rule is a cooldown. A new open source release cannot enter the build until it has been public for a set number of days. The second rule blocks any library version with a known high or critical vulnerability.

Mobilization is the fifth and final stage of continuous threat exposure management (CTEM). It turns validated, prioritized exposures into action by assigning clear owners, delivering context-rich work into the tools teams already use, choosing the fastest safe response, tracking SLAs, and verifying that fixes worked. It’s often the hardest stage because security teams find exposures but IT, engineering, and cloud teams fix them.

Interaction monitoring covers four of the ten risks in the 2025 Top 10 for LLMs and generative AI applications well, three partially and three not at all. The split is not arbitrary, and the line it follows tells a buyer more than the coverage count does. ‍ Monitoring observes systems being used. Several entries on the list describe defects in how a system was built, which no amount of observation at the point of use will surface.

On October 14, 2026, Bank of America's CEO said the bank brought in 1,000 contractors to patch software flaws surfaced by frontier AI models, including Anthropic's Mythos. "We put on 1,000 contractors to help us do the patching work in the second quarter," Brian Moynihan told CNBC. Those contractors work on top of the 3,000 people the bank already has in that area (ITPro, The Next Web). That is what the new normal looks like.

Can AI agents fix vulnerable dependencies without breaking the build? The fix itself is rarely the mystery. Over six months in 2026, Snyk Open Source recorded 459 million vulnerability findings across ~300,000 organizations. 94% of these vulnerabilities had a known fixed version, yet only about 10% had been resolved.

AI is changing how software gets built. Coding agents produce more code than teams can review, and current models introduce several times more vulnerabilities per line than human developers. Many of those flaws involve business logic, access control, and authentication, areas where pattern-based scanners have limited visibility.