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 bills of materials moved from voluntary best practice to regulatory requirement in 2026. Multiple jurisdictions now require disclosure of model, data, and component lineage for high-impact AI systems.
CMMC 2.0 rollout has made flow-down expectations concrete. AI-for-security tools used by DIB contractors are in scope, and the pass-through story matters.
Most scanners stop at five or six levels of transitive depth. Real production graphs run sixty levels deep, and the most interesting vulnerabilities live in the long tail.
AI acceleration is compressing both software delivery and attacker tradecraft. A security look at what changes when AI speeds up your pipeline and theirs.
Practical security for Docker containers: minimal base images, non-root users, image scanning, and runtime hardening you can apply to any Dockerfile today.
Training data is a supply chain component. Knowing what went into a model is the precondition for knowing what could come out of it. Few tools track this; the few that do matter disproportionately.
CVSS alone is a bad prioritisation signal in 2026. Reachability plus EPSS gives teams a defensible order to fix the vulnerabilities that actually matter.
Container compliance means proving your images and runtime meet the controls auditors ask for, continuously, without turning every deploy into a manual review.
Securing Docker containers is less about one setting and more about a chain of defaults: slim base images, non-root users, scanned layers, and locked-down runtime privileges.
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