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.
Securing Docker containers is less about one setting and more about a chain of defaults: slim base images, non-root users, scanned layers, and locked-down runtime privileges.
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.
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.
Poolside's on-prem code AI is a credible enterprise offering. For security-specific workflows, Griffin AI's grounding architecture targets different ground.
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.
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.
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
Frontier models pass eval benchmarks that open-weight models miss by specific measurable margins. For security workflows, the gap matters.
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.
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