griffin-ai
Safeguard articles tagged "griffin-ai" — guides, analysis, and best practices for software supply chain and application security.
180 articles
Zero-Day Discovery ROI: CISO Board Deck
How to talk to your board about zero-day discovery without overpromising. The metrics, the framing, and the slides that hold up under follow-up questions.
Griffin AI vs GitHub Copilot for Vulnerability Fixing
GitHub Copilot suggests fixes. Griffin AI generates fix PRs with taint paths and disproof attached. The difference is review burden.
Continuous Eval & Release Gating: Griffin AI vs Mythos
Evals that run once are marketing. Evals that run on every build are infrastructure. Griffin AI runs the harness on every change; Mythos does not describe one.
Race Condition Detection: Griffin AI vs Mythos
Race conditions are the hardest class of vulnerabilities for static analysis. Specific architectural capabilities separate tools that find them from tools that claim to.
From CVE To PR: The Full Remediation Pipeline
A complete walkthrough of the modern remediation pipeline, from advisory ingestion through merged and deployed fix, with every stage that actually matters.
False Positive Cost: Griffin AI vs Mythos
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 Path Detection: Griffin AI vs Mythos
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.
Griffin AI vs Open Weights: On-Prem Tradeoffs
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.
Griffin AI vs Claude Batch API for Scanning
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.
Griffin AI vs GPT-5: Enterprise Controls
Frontier models offer impressive enterprise features. Security programs need deeper controls than chat can provide—controls that live in the engine around the model.
Why Engine-Plus-LLM Beats Pure-LLM: Griffin vs Mythos
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
Griffin AI vs Gemini Multimodal: Security
Gemini's multimodal capabilities are genuinely useful for some security workflows. For most security workflows, the modality is code and text, not images.