Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Compliance teams have control over approved corporate systems like enterprise software, managed databases, and internal applications. But they don’t have the same over what employees paste into ChatGPT, upload to Claude, or share with Gemini and other unauthorized AI tools. As such, when auditors review AI usage controls, most organizations discover they can’t prove that employees aren’t exposing regulated data through external AI services.

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.

Most current demonstrations of AI in security operations are lackluster. You ask a chat interface a question, get a summary, and maybe a suggested next step. The operator still does all the work, at human speed. Meanwhile, adversaries are already deploying AI offensively against their targets. AI in SecOps must ultimately be an operator. Otherwise, the gap between adversary and defender will become too wide to bridge. LimaCharlie Co-founder, Christopher Luft, demonstrates a simple way to get started.

AI governance is not a policy problem. It’s a visibility problem. Most enterprises are approaching it from the outside in: writing acceptable use policies, issuing guidelines, and hoping employees comply. That approach fails because it operates on assumption rather than evidence. You cannot enforce what you cannot see and most organizations have no reliable way to see what AI tools are actually running inside their environment.

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.