AI Security
Cost Per Finding: Griffin AI vs Mythos
Token spend per scan is the wrong metric. Cost per actionable finding is the right one — and it's where engine-plus-LLM economics dominate pure-LLM economics.
Mar 8, 20265 min read
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Dependency Confusion: Griffin AI vs Mythos
Dependency confusion is older than most of the AI tooling trying to detect it. The attacks have adapted to the defences — detection needs to keep up.
Mar 8, 20263 min read
AI Security
Griffin AI vs Poolside for Enterprise Security
Poolside's on-prem code AI is a credible enterprise offering. For security-specific workflows, Griffin AI's grounding architecture targets different ground.
Mar 8, 20262 min read
AI Security
MCP Authentication Patterns for Enterprise
Enterprise MCP deployments need more than a static API key. The protocol is evolving toward OAuth 2.1 and dynamic client registration, and understanding which pattern fits which workload decides whether your rollout survives the first audit.
Mar 8, 20267 min read
AI Security
Enterprise AI Red Team Program Design
AI red teaming is not a one-off exercise. Programmatic red-teaming of AI systems requires specific structure — and most organisations don't have it yet.
Mar 7, 20262 min read
AI Security
Elastic Scale Behaviour: Griffin AI vs Mythos
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
Mar 7, 20266 min read
AI Security
Griffin AI vs Open Weights: The Eval Gap
Frontier models pass eval benchmarks that open-weight models miss by specific measurable margins. For security workflows, the gap matters.
Mar 7, 20263 min read
AI Security
The Reproducibility Crisis In AI Security Evals
ML research has a reproducibility crisis. AI security evaluation inherits it. Vendors publishing numbers that can't be reproduced are the norm — not the exception.
Mar 6, 20262 min read
AI Security
Griffin AI vs Claude Prompt Caching: Security
Claude's prompt caching gives you 90% discount on cached tokens. Security workloads have massive cacheable surface area. Griffin AI takes advantage; direct API use often does not.
Mar 6, 20262 min read
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