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.
Race conditions are the hardest class of vulnerabilities for static analysis. Specific architectural capabilities separate tools that find them from tools that claim to.
Data residency for AI workloads has moved from nice-to-have to contractually required. The shape of the requirement is specific and worth knowing before procurement.
A false positive is not free. It costs engineer attention, trust in the tool, and eventually the security programme's credibility. We price the difference.
Injection vulnerabilities are not really about the sink. They are about the path from untrusted input to the sink. The path is where Griffin AI and Mythos-class tools diverge.
Open-weight models let you run everything locally. The tradeoff is quality, cost, and operational overhead. Griffin AI provides a different answer to the same on-prem need.
Fine-tuning inherits every problem of the base model and adds dataset provenance as a new one. Here is how detection actually works in practice.
Using an LLM to score another LLM's output is expedient and dangerous. The judge has its own biases — ones that affect security evaluations specifically.
Claude's Batch API gives you 50% off for async workloads. Griffin AI uses it internally. The question is whether your team should use the Batch API directly or consume it through Griffin.
Frontier models offer impressive enterprise features. Security programs need deeper controls than chat can provide—controls that live in the engine around the model.
Weekly insights on software supply chain security, delivered to your inbox.