Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Sophos ITDR now delivers deeper AI identity visibility, expanded Microsoft support, faster response, and seamless deployment. Identity has become one of the most targeted attack surfaces in cybersecurity, with attackers increasingly relying on stolen credentials, compromised sessions, excessive privileges, and identity misconfigurations to gain access.

In the recently released 2026 SANS AI in Cybersecurity Survey, nearly two out of three security professionals said they have been given AI-generated guidance that later turned out to be wrong. Not from an experimental tool off in a sandbox, but from systems built into the security stack they were told to rely on. Security teams should not have to live with that.

AI coding agents need access to real development context to be useful. They read repositories, modify files, execute commands, call tools, and communicate with external models. Docker Sandboxes gives those agents an isolated environment where they can work without unrestricted access to the developer’s host. But consider a simple scenario. An agent running entirely inside its sandbox reads a credential from a project configuration file and includes it in an otherwise legitimate request to its LLM.

Over time, we have become increasingly accustomed to using AI in our daily work. We send customer data to ChatGPT for analysis, share customer emails to draft responses, or ask Claude to generate code for enterprise applications. All of this can happen within an enterprise environment. This is what generative AI is designed to do. It creates content for us, from text and code to images and videos.