Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Buried in OpenAI’s evaluations of its GPT-6 Astra model sits a finding that mobile teams should sit with for a minute. The model reverse-engineered compiled software well enough to escape a browser sandbox and chain privilege-escalation flaws on a hardened operating system, according to the company’s published evaluations. Reading compiled binaries used to be specialist work priced in weeks, and now it’s something machines do quickly and well.

On September 1, OpenAI announced that its new model, GPT-6 Astra, had become the first to cross the “Critical” cybersecurity threshold in the company’s Preparedness Framework. Most coverage focused on the safety implications, and fairly so, but buried in the announcement sits a benchmark result that should change how every security leader reads their next vulnerability report.

This article was first published on LinkedIn. Consider a cybersecurity alert for a potentially malicious PowerShell script that downloads and runs a file. An AI agent in a security operations center (SOC) can handle it by picking from a fixed set of actions: close the alert as benign, collect more evidence, or send it to an analyst. Before letting the agent act on such alerts, the SOC needs to know how often it picks correctly and whether its confidence helps identify its mistakes.

October is Cybersecurity Awareness Month, a good time to review the everyday habits that protect our accounts, devices, information, and organizations.

That might sound like a trick question since it’s what we do as a managed infrastructure services provider. But it really isn’t. Here is the quick answer: Managed services let organizations outsource day-to-day IT operations, including networks, security, connectivity, and policy management, to a specialized provider.

Part 3 of the Enterprise OIT Series By Bill Moore, Founder & CEO, Xona Systems TL;DR -- Intelligence vs.

TL;DR: Frontier AI has collapsed the vulnerability exploit window from weeks to mere hours. Attackers now use advanced AI models to reverse-engineer published fixes and generate working exploits faster than human security teams can deploy updates. In the AI era, publishing a patch creates a blueprint for attackers. Surviving this threat requires vendors to tier sensitive disclosures and customers to treat patch application speed as a critical security metric.

AI agents are already operating across the enterprise. As they become more autonomous in accessing data, using credentials, executing code, and acting across critical systems, the security boundary is changing with them. The question is no longer whether enterprises will adopt agents. It's how they give agents greater autonomy without giving up control.