Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

An AI agent on a developer's laptop has read access to a code repository, a set of internal documents, and an external model. Nobody approved that specific combination, and nobody is watching what the agent does with it session to session. The agent is not malicious, however, it is doing exactly what it was configured to do. But, that configuration is the exposure, and most security teams do not have a way to see it, let alone stop it before sensitive data leaves the environment.

A nasty shock is hitting finance leaders across every industry right now: AI token bills that run ten, twenty, even a hundred times over what they forecasted, blowing holes straight through quarterly budgets. These leaders are all asking the same questions: How could this happen if they didn't approve it? Why didn't any of their systems alert them to the spike? And most importantly, what can they do now?

Unknown block type "undefined", specify a component for it in the `components.types` option Every conversation about AI and early-career employees seems to start in the same place: will it weaken foundational skills by doing too much of the work? That's a reasonable concern. It's also not the question that ended up mattering most this summer.

Scammers can use AI tools to find your location in photos you post to social media, according to researchers at McAfee. This information can then be used in targeted social engineering attacks. The researchers found that free AI models can correctly identify a photo’s location with around 90% accuracy.

Employees paste code into ChatGPT. They drop customer lists into Gemini for a quick summary. They upload a contract to an AI note-taker before a meeting starts. None of it feels risky in the moment. But all of it can walk sensitive data straight out of your organization. AI DLP exists to close that gap.