A practical, source-backed checklist for scoping GitHub Copilot browser tools, limiting domains and permissions, and reviewing evidence after each agent-driven test.
A practical small-team framework for comparing GitGuardian and Aikido across secret detection, broader AppSec coverage, CI gates, evidence, privacy, and cost.
A practical, evidence-based framework for small teams evaluating Snyk competitors across coverage, workflow, privacy, reporting, maintenance, and cost.
A practical workflow to stop AI coding agents from exposing credentials through context collection, terminal output, logs, generated files, and Git history.
Why AI Coding Tools Silently Add Dependencies GitHub Copilot, Cursor, Claude Code, and other AI coding assistants generate code fast — but they also silently introduce third-party dependencies you never explicitly approved. A single AI suggestion can pull in a package with known vulnerabilities, incompatible licenses, or abandoned maintainers. Your package.json, requirements.txt, or Pipfile.lock grows
AI-generated code is everywhere now — from Copilot suggestions in your editor to full functions from ChatGPT and Claude. The problem is not the code itself; the problem is what slips through when nobody builds a real review gate before merging. Most teams assume they need a cloud-based SAST platform or an enterprise CI pipeline
Why Your Repository Needs a Security Review Before AI Writes More Code AI coding assistants like GitHub Copilot, Cursor, and Claude Code are transforming how developers write software. But every line of AI-generated code that lands in your repository introduces new risk vectors that traditional code review processes were never designed to catch. A structured