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.
Microsoft has reported that roughly 70% of the CVEs it patches each year trace back to memory-safety bugs — here's what buffer overflows, use-after-free, and double-free actually look like.
HackerOne alone has paid hackers over $300M since 2012, but most new researchers earn nothing — duplicates, not skill gaps, are the top reason first reports fail.
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.
NIST finalized PQC standards in 2024, but most companies can't even inventory where RSA and ECC live in their stack. Here's a realistic migration roadmap for AppSec teams.
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.
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