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.
You cannot secure what you cannot enumerate. Griffin AI maintains a typed inventory of every model, version, and deployment across a tenant. Mythos-class tools approximate the inventory in prose.
Poisoned AI models are a supply chain threat that traditional security tools can't detect. Here are the emerging techniques for identifying compromised models.
Cursor Tab is excellent at in-editor autocomplete. For security review, the workflow is different enough that the right answer is to use both.
CSRF in 2026 is not the 2012 attack. SameSite cookies, fetch metadata, and modern frameworks changed the landscape. Detection needs to keep up.
Benchmark scores are only as honest as the dataset behind them. Griffin AI publishes golden-dataset design notes; Mythos-class tools rarely explain theirs.
Securing container software means controlling the whole chain, base image, dependencies, build, registry, and runtime, not just scanning the final image. Here is a working model for each layer.
Container runtime scanning watches workloads while they run, catching drift, new CVEs, and active exploitation that build-time scans miss entirely.
AI-for-security procurement covers more than feature comparison. The due diligence checklist that surfaces structural differences between vendors.
CWE-502 deserialisation chains are the canonical stress test for AI bug hunters. Why Griffin AI's grounded synthesis finds real chains and Mythos-class scanners hallucinate them.
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