griffin-ai
Safeguard articles tagged "griffin-ai" — guides, analysis, and best practices for software supply chain and application security.
180 articles
ROI Timeline: Griffin AI vs Mythos
The honest answer to "when does this pay back?" is where sales decks and procurement reality diverge. Griffin AI and Mythos-class tools have different ROI shapes.
Griffin AI vs Open Weights: Supply Chain Risks
Open-weight models give you total deployment control. They also give you a new supply chain to secure. The tradeoff is worth being explicit about.
Griffin AI vs Claude Citations: Advisory Work
Claude's citations feature makes the model say where its claims come from. Griffin AI uses it for advisory workflows where traceability is the entire point.
Griffin AI vs OpenAI Pricing: Security Workloads
Per-token pricing on the OpenAI API looks cheap on a single call and expensive on a year-long security workload. Griffin AI's pricing reflects the architecture.
Zero-Day Triage Without Drowning Engineers
A zero-day discovery pipeline is only as useful as the triage process around it. Here is what triage looks like when the pipeline gives engineers something they can defend.
Griffin AI vs Gemini On-Device: Developer Tools
Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.
Grounded Reasoning vs Hallucinated: Griffin AI vs Mythos
The difference between grounded reasoning and hallucinated reasoning is not eloquence — it's citation. A look at how Griffin AI anchors every claim.
Evidence-Attached Fix PRs Reviewers Trust
Reviewers trust fix PRs that come with evidence. Here is how to attach the right evidence so AI-assisted remediation gets approved on the first pass.
Breaking Change Awareness: Griffin AI vs Mythos
An auto-fix that closes a vulnerability and breaks the build is not a fix. Breaking-change awareness separates auto-PRs that ship from auto-PRs that get reverted.
Audit Trail Quality: Griffin AI vs Mythos
An audit trail is only useful if you can answer questions from it. Quality is not about volume — it's about the ability to reconstruct decisions after the fact.
Sanitizer Detection: Griffin AI vs Mythos
A vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
Cross-Vendor SBOM Normalization: Griffin AI vs Mythos
Your SBOMs come from a dozen vendors, three scanners, and two CI systems. Normalising them into one queryable graph is where SBOM programs actually succeed or fail.