Modern developer environments expose sensitive context across files, prompts, logs, and commands. Learn how layered local controls reduce secrets risk.
AI-enabled systems are becoming more common in operational technology (OT) environments. What many industry analysts call “Physical AI” refers to AI systems embedded in physical environments — such as industrial cameras, robots and edge systems — that can perceive, interpret and act on real-world conditions. In industrial settings, this includes machine vision systems, predictive maintenance models, robotics optimization and edge analytics operating close to production assets.
Anthropic’s announcement of Claude Mythos Preview may end up being remembered as the moment the cybersecurity industry had to stop talking about agentic AI as a future concept and start treating it as a present security variable. The reported results are serious. Anthropic says Mythos Preview identified and exploited zero-day vulnerabilities across major operating systems and browsers during testing.
Opera and artificial intelligence may not seem like natural companions, but they share one important truth: The best performances are revealed over time. Early scenes set the stage, introduce the themes, and create a sense of anticipation. The audience leans in, waiting for the big moments still to come. AI in financial services has followed the same structure.
In the latest episode of This Month in Datadog, Jeremy shares how to run autonomous Cloud SIEM investigations, remediate vulnerabilities with auto-generated fixes, and use natural language to explore Datadog. Later, Sumedha Mehta spotlights the Datadog MCP Server, which gives AI agents real-time access to Datadog’s observability data. Then, Chetan Sharma walks through Datadog Experiments, which measures how product changes impact the user journey.
Here's a number that should stop you cold: unplanned equipment failures cost organizations billions every year, and most teams don't catch the bleeding until it's already serious. I've seen facilities limp through reactive maintenance cycles for years, convinced it was "just how things work." It isn't. Predictive maintenance software has become the clearest answer to that problem, and in 2026, the platforms doing it best are genuinely transforming how asset-heavy operations run.
Most hiring teams are currently working with software that was built for a different era of technology. These legacy systems are reliable for storing data but they often lack the smart features that modern recruiters need to stay competitive in a fast market. It is a common struggle that leads to frustration.
The deepest threat is not that AI finds one clever bug, it is that it can do offensive work at a scale humans never could. Tasks that once demanded time, money and elite skill can now be accelerated, repeated and widened far beyond the limits of a traditional red team.
Security hardening is the practice of systematically reducing the ways an attacker can get into, move through, or persist in your environment.The reason this is a priority is that the gap between "deployed" and "properly configured" is where most breaches actually live. This guide covers the core principles of hardening, how it maps to compliance requirements, and how automated tooling has changed the way mature security teams operationalize it at scale.