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.
Poor AI data quality is not just an accuracy problem — it's an attack surface. Here's how data integrity, provenance, and validation shape the security of the models you ship.
Container malware scanning inspects image layers for malicious binaries, backdoors, and tampered dependencies before they ever run in your cluster.
A practitioner's walkthrough of the OWASP Top 10 for LLM Applications: what each risk looks like in a real system, which ones bite first, and the mitigations that hold up.
Anthropic Claude Code security rests on permission gating, sandboxed execution, and human approval for risky actions — the same fundamentals any AI coding agent needs before it's allowed to run commands or edit code unattended.
Container image solutions span how you build, store, scan, and sign images. Choosing them well is what turns a container pipeline into a secure supply chain rather than a liability.
Agent runaway is no longer a theoretical risk — it is a line item on quarterly variance reports. The 2026 trend in agentic AI is less about model capability and more about who pays when an agent loops.
A repeatable onboarding flow for adding MCP servers to an enterprise registry without becoming the team that says no to everything.
Prompt traces and offline evals are standard hygiene for ML teams, but almost nobody treats them as supply chain telemetry. They should be. Here's how traces and evals plug into SBOM and reachability as a fourth security signal.
MCP servers run with your credentials and your filesystem unless you say otherwise. Containerizing them with read-only mounts, dropped capabilities, and egress controls turns an open-ended trust grant into a bounded one.
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