Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

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.

You've done the work. Logs are flowing. Rules are enabled. Agents are deployed. Dashboards exist. But if your CISO, an auditor, or a red team report asked if your SIEM is ready to detect and respond to the threats that matter, could you answer confidently? Most teams can't.

BlueVoyant Security Operations Center (SOC) and Threat Fusion Cell (TFC) security researchers have been tracking an emerging, rapidly maturing threat group conducting a global SEO-poisoning campaign that distributes trojanized Microsoft Teams installers. These installers ultimately deploy a multi-stage shellcode loader and backdoor BlueVoyant has designated Lorem Ipsum.

A software patch is a targeted code update that vendors release to fix cybersecurity flaws, correct bugs, or address performance issues in an existing application or operating system without replacing the software entirely.

Deepfake-driven fraud has caused $2.19 billion in losses globally, with $1.65 billion reported in 2025 alone, according to an analysis by Surfshark. More than half of these losses were due to investment scams using deepfakes of high-profile figures.

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.

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.

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.

The latest National Institute of Standards and Technology (NIST) draft guidance on mobile driver’s licenses(mDLs) is about more than one use case or credential type. While the draft primarily focuses on the financial sector due to its high-assurance requirements, the bigger takeaway is that government-issued identity can be cryptographically verified and shared more selectively.