AI Code Review for Security: How Effective Is It Really?
AI-powered code review tools promise to catch vulnerabilities faster than humans. We tested the claims against reality.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
AI-powered code review tools promise to catch vulnerabilities faster than humans. We tested the claims against reality.
A practitioner's guide to picking a container scanning tool: what it should detect, where it fits in the pipeline, and how to avoid drowning in false positives.
A senior engineer's side-by-side look at Griffin AI and Mythos — why engine-grounded reasoning beats pure-LLM security intuition when the audit clock starts.
Anthropic's Claude Agent Skills let you package tools and context for Claude. Here's how that primitive compares to Griffin's security-specific workflow scaffolding.
When the test set is in the training set, the benchmark is broken. Security eval contamination is widespread and the mitigations are specific.
AI security solutions now span two very different categories — securing AI systems and using AI to secure everything else — and buyers who conflate them end up with the wrong tool.
The best AI tool for resume building is the one that improves your document without quietly harvesting the personal data on it. Here is how to judge these tools on privacy and security, not just polish.
Automated vulnerability patching sounds ideal until you consider what happens when the automation gets it wrong. Here's a realistic look at autonomous remediation.
Reasoning models have arrived in security tooling. Evaluating them requires different methodology from evaluating classification or generation models. Here is what good evaluation looks like.
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