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GitHub code scanning now surfaces AI-powered security detections directly on pull requests, expanding vulnerability coverage to languages and frameworks not currently supported by CodeQL. These detections help teams identify and… The post Code scanning shows AI security detections on pull requests appeared first on The GitHub Blog.
Federal officials have not confirmed a source yet—and there may be multiple sources.
It began with a late-night alert. A critical cloud application, serving thousands of users, had just been flagged for a security violation. No “hack” had occurred; nothing obviously was broken. What appeared to be a minor misconfiguration had quietly exposed sensitive data. The system was still running. The business was still operating. But compliance? Already compromised.
A recap of the latest servicing updates for .NET and .NET Framework for July 2026. The post .NET and .NET Framework July 2026 servicing releases updates appeared first on .NET Blog.
Two flaws in Active Directory and SharePoint Server have been exploited as zero-days, and a BitLocker bug was publicly disclosed. The post Microsoft Patches Record 622 Vulnerabilities, Including Two Exploited Zero-Days appeared first on SecurityWeek.
Microsoft has released the Windows 10 KB5099539 extended security update, which includes the July 2026 Patch Tuesday security updates for 570 vulnerabilities, along with additional security fixes. [...]
This post shows how Thrad.ai deployed a multi-agent system with Strands Agents and Amazon Bedrock AgentCore that automates the pipeline from prospect discovery through personalized email generation. The post compares two orchestration patterns (Swarm and Graph) with head-to-head benchmarks on latency, cost, and email quality. You’ll also learn how the system scores prospects using weighted criteria, intent classification, and temporal decay, plus governance controls for production deployment.
In this post, we show you how to build a semantic ontology that helps your AI assistants navigate enterprise data efficiently. You’ll learn how to structure a property graph store for data relationships, set up vector indexing for semantic search, and implement an automated fact-learning layer that improves use. This bottom-up approach grounds your ontology in the data that exists, building abstractions from observed patterns rather than theoretical models.
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