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.
A container security tool should cover the image, the registry, and the running workload — not just spit out a CVE list. Here is how to evaluate one without the marketing gloss.
A data container security policy sets the rules for how containers handling sensitive data are built, run, and monitored. Here is what to put in one.
The full form of MCP is Model Context Protocol, the open standard that lets AI models talk to tools and data. Here is what it is and where the security risks live.
An AI code checker uses a language model to review code for bugs and security issues. Here is where it genuinely helps and where trusting it blindly bites you.
A secure Docker container starts with a minimal base image, a non-root user, and a scanned, pinned dependency set. Here's the hardening checklist that actually holds up in production.
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.
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.
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.
Every agent in production has a blast radius. Most teams have not measured theirs. Here is how to measure it and how to bring it under control.
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