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.
A detailed comparison of how Griffin AI consumes SBOMs as structured reasoning context while Mythos-class pure-LLM tools skim them as prose — and why that architectural gap determines the quality of every downstream finding.
Prompt injection has evolved from demonstration exploits into a category of attack that runs continuously against production AI systems. Here is what changed in 2026.
Griffin AI publishes a five-family eval harness with concrete numbers. Most Mythos-class competitors ask buyers to trust marketing claims instead of data.
When AI writes code that ships to production, the audit trail is a compliance requirement, not a nice-to-have. Patterns for capturing it without killing velocity.
A candid look at how Griffin AI's three-stage zero-day pipeline compares to pure-LLM Mythos-class bug hunters, and why false positive rates matter more than raw volume.
Why enterprise AI for security requires genuine on-premises deployment, not just a SaaS endpoint with a VPN in front of it.
OpenAI shipped ChatGPT Atlas in October 2025 and admitted by December that prompt injection in AI browsers may never be fully solved. Defenders need a posture, not a patch.
Traditional AppSec was built for static code, not decision-making agents. Here's what agentic AI security actually covers—and why autonomous agents need a new defense model.
AI agents now hold production credentials the way employees do, except most are never offboarded. Here's how AI agent identity and access management closes that gap.
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