Ensemble LLMs For High-Precision Security Findings
One model's confident answer is a guess. Multiple models agreeing is evidence. Ensemble approaches raise precision for security-critical findings.
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.
One model's confident answer is a guess. Multiple models agreeing is evidence. Ensemble approaches raise precision for security-critical findings.
Compliance posture is about what you can prove, not what you can do. GPT-5 has impressive capabilities; Griffin AI is engineered to be defensible.
Dependency confusion attacks are still landing in 2026 because scoped packages, registry config, and provenance checks are misconfigured by default. Here is the fix.
The Local Runner is a command-line agent that runs Safeguard workflows against your working tree. Think claude-code-for-security, but for supply chain.
UNC5221 chained Ivanti Connect Secure zero-days through 2024 and 2025. The campaign reads like a masterclass in living off trusted edge appliances.
Pure-LLM security analysis hallucinates findings at rates between 20% and 70% depending on the task and model. Grounding is the architectural answer.
Gemini has FedRAMP-authorised deployment options. Griffin AI builds on FedRAMP-aligned infrastructure. The comparison is about what the customer has to build.
Why pure-LLM security products generate false positives that engine-grounded platforms like Griffin AI structurally cannot — with CWEs and real triage data.
Support tier comparisons look identical on paper. The real difference shows up at 2am during an incident, and the shape of that difference is worth understanding before signing.
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