Snyk Competitors for Small Teams: A Practical Evaluation Framework
A practical, evidence-based framework for small teams evaluating Snyk competitors across coverage, workflow, privacy, reporting, maintenance, and cost.
AI Agent Secret Leakage: Keep Credentials Out of Context, Logs, and Git
A practical workflow to stop AI coding agents from exposing credentials through context collection, terminal output, logs, generated files, and Git history.
Dependency Audit Workflow for AI-Generated Code: Catch What the AI Missed
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 Code Review Automation: How to Build a Local Review Gate Without Cloud Tools
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
Repository Security Review Checklist for AI-Assisted Development
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
SOC 2 Compliance Evidence for AI-Generated Code: What Auditors Actually Look For
Introduction SOC 2 Type II audits are no longer optional for teams shipping software in 2026. Customers, partners, and procurement departments expect verifiable proof that your development process includes meaningful security controls — and that proof needs to extend to AI-generated code. If your team uses tools like GitHub Copilot, Cursor, or Claude Code to
SBOM Audit for AI-Generated Code: Why Dependency Scanning Isn’t Enough
When your AI coding assistant pulls in a package, who checks whether that package is safe? Most teams assume their existing dependency scanner catches the risk. The reality is more complicated — and more dangerous — than a single scan can address. If you use GitHub Copilot, Cursor, Claude Code, or any AI coding agent,
CodeQL Path Injection Alert: How to Diagnose and Fix It Safely
CodeQL Path Injection Alert: How to Diagnose and Fix It Safely GitHub CodeQL flags path injection alerts when your code constructs file paths from user-supplied input without proper validation. These alerts fall under the CWE-22: Improper Limitation of a Pathname to a Restricted Directory category — also known as directory traversal or path traversal. If
Claude Code Security Checklist: How to Review AI-Generated Code Safely
Claude Code Security Checklist: How to Review AI-Generated Code Safely Claude Code and other AI coding assistants generate impressive code at speed. But that speed comes with risk. AI models can introduce security vulnerabilities, expose secrets, generate insecure defaults, and create dependencies you never explicitly approved. The NCSC’s 2026 warning about vibe coding highlighted 35
Prompt injection in AI-generated code: how to spot and prevent malicious prompts
AI coding agents are transforming how developers write software. Tools like GitHub Copilot, Cursor, Claude Code, and Codex generate millions of lines of code daily. But there is a growing threat that most developers have never considered: prompt injection in AI-generated code. Prompt injection — when an attacker manipulates the instructions given to an AI