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.
In a 300-run benchmark, the best LLM scanner hit 75.4% F1 while a deterministic SAST baseline hit 100% — but the real story is in what varies between runs.
AppSec used to mean scanning code for known bugs. Here's why that's no longer enough, what CVE-matching tools like Snyk miss, and what a real supply chain security program requires.
CodeQL's default setup is fast but limited; advanced setup adds control but more YAML to maintain. Here's how the two compare, and where Safeguard fits in.
GHAS matches secrets against ~200 partner patterns. We break down where that coverage ends and how Safeguard's layered detection catches what pattern lists miss.
How GitHub Secret Protection's push protection, custom patterns, and validity checks actually work post-GHAS split, and where the coverage gaps still leave secrets exposed.
GitHub split Advanced Security into Code Security and Secret Protection in 2025. Here's how CodeQL SAST actually works, what it misses, and how Safeguard fills the supply-chain gap.
GitHub spent nine months clearing 20,000+ secret scanning alerts across 15,000 repos, finding 90% were noise. Here's how they beat alert fatigue, and how Safeguard automates it.
Regex-based secret scanners like GitHub Advanced Security flood teams with false positives. Here's how context-aware LLM reasoning cuts the noise without missing real leaked credentials.
SAST, DAST, and IAST each test different things. Here's how Checkmarx positions its platform, and where Safeguard's supply chain approach fits alongside it.
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