A practical exploration of how well reasoning LLMs identify vulnerabilities in real-world code, comparing results across models and against a traditional SAST tool (Semgrep).

A practical perspective shift for security professionals: Learn why focusing on concrete mitigations rather than abstract threats leads to better developer engagement and more secure software. Featuring hands-on examples using AI-powered security analysis tools and real-world project implementations.

Discover how the new Deep Analysis Mode in AI Security Analyzer provides in-depth security insights, with practical examples using Google's Gemini 2.0 Flash Thinking Experimental model.

An in-depth look at how I leveraged Gemini 2.0 to create a massive security documentation repository, complete with practical examples and lessons learned.

Preview of the AI Security Analyzer - a new tool that leverages AI to automatically generate comprehensive security design documentation for your projects.

Introducing the Fabric Agent Action - a GitHub Action that automates complex workflows using AI-powered agents and Fabric Patterns.

How I use Fabric patterns to create, review and refine design documents.

The Fabric framework enhances AI-powered threat modeling with a new pattern, offering detailed, actionable security insights.

With new version of Claude model, I would like to compare it to GPT-4 in threat modeling.

The quality of input data is crucial for LLMs to perform effectively. Learn how you can use these LLMs to improve your architectural descriptions. Explore the new feature in my ai-threat-modeling-action GitHub action.