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.
Studies show developers trust AI-generated code more than human code, even though it's often less secure. Here's what's driving the AI code trust gap.
Studies show 40-45% of AI-suggested code contains exploitable flaws, and models hallucinate fake packages developers install. Here's what the data says.
AI coding assistants write fast but fail differently than humans do. Learn why scanning AI-generated code needs new heuristics for hallucinated dependencies.
The NIST standards are final, the deadlines are real, and the harvest-now-decrypt-later clock is running. Here is an honest look at what enterprise PQC migration looks like in 2026 — and why crypto-agility matters more than picking an algorithm.
Apache tomcat vulnerabilities keep surfacing because Tomcat sits directly in the request path of so many Java applications; here is what the Coyote connector does and which vulnerability classes recur most.
An honest, opinionated guide to the best Snyk alternatives in 2026 — Endor Labs, Socket, Mend, Aikido, Semgrep, Sonatype, Trivy, and Safeguard — with a fair blurb and a 'best for' line for each, plus where reachability and remediation actually matter.
Not all software comes with source code. Binary analysis techniques can extract component information from compiled artifacts, firmware, and commercial software to produce SBOMs where traditional tools cannot.
MITRE's 2023 CWE Top 25 ranks the software weaknesses behind 43,996 CVEs. Here's how it's scored, what moved, and how to prioritize fixes.
AI coding assistants read untrusted files as instructions, not data. Here's how prompt injection sneaks malicious code into your commits — and how to catch it before it ships.
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