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.
Griffin AI's evaluation harness results published for the first time. Benchmark methodology, comparison against baselines, and what the numbers mean for production use.
When your pipeline starts producing zero-days, you inherit responsible disclosure obligations. Here is how to do it well, with the artefacts the pipeline already gives you.
Fixing a transitive dependency is rarely a single bump. It is a cascade. Here is how to manage those cascades without flooding reviewers or breaking builds.
Multi-repo security reasoning is a graph problem, not a retrieval problem. How Griffin AI's engine scales where pure-LLM products flatten into guesswork.
The difference between an engine-plus-LLM bug hunter and a pure-LLM one is not a tuning detail. It is a structural divide that determines whether the findings are usable.
A one-hour cycle from vulnerability finding to merged fix is achievable in 2026, but only with a pipeline designed for it. Here is what that pipeline looks like.
A hijacked tool call is more consequential than a hijacked response. The defence requires the tool layer to police the model, not the other way around.
Cody's codebase-wide context is valuable for security review. Griffin AI adds reachability, taint, and policy grounding that Cody doesn't target.
The honest answer to "when does this pay back?" is where sales decks and procurement reality diverge. Griffin AI and Mythos-class tools have different ROI shapes.
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