evals
Safeguard articles tagged "evals" — guides, analysis, and best practices for software supply chain and application security.
24 articles
Building an Eval Suite for Your Security LLM Workflows
If you use an LLM anywhere in your security program — triage, remediation, detection — you need an eval suite with the same rigor as your test suite. Here is a concrete harness: datasets, thresholds, CI gates, and drift detection.
LLM Traces and Evals: The Missing Layer in AI Supply Chain Security
Prompt traces and offline evals are standard hygiene for ML teams, but almost nobody treats them as supply chain telemetry. They should be. Here's how traces and evals plug into SBOM and reachability as a fourth security signal.
Safeguard Griffin AI: Eval Benchmarks Published
Griffin AI's evaluation harness results published for the first time. Benchmark methodology, comparison against baselines, and what the numbers mean for production use.
Regression Gate Design Patterns For Security LLMs
A release gate that fails on regression is the most important operational control for AI-for-security tools. The design patterns are specific and worth copying.
Real-World Vs Synthetic Eval Gap In Security
Synthetic eval benchmarks are controllable. Real-world data is messy. The gap between performance on each is usually large, and vendors prefer one over the other for a reason.
Ensemble LLMs For High-Precision Security Findings
One model's confident answer is a guess. Multiple models agreeing is evidence. Ensemble approaches raise precision for security-critical findings.
Griffin AI vs Open Weights: The Eval Gap
Frontier models pass eval benchmarks that open-weight models miss by specific measurable margins. For security workflows, the gap matters.
The Reproducibility Crisis In AI Security Evals
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.
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.
LLM-As-Judge Pitfalls In Security Evals
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.
The Eval Culture Shift in AI Security
Two years ago, AI vendors shipped without evals. In 2026, the posture has shifted. Customers expect benchmarks. Vendors without them lose deals.
Golden Dataset Design: Griffin AI vs Mythos
Benchmark scores are only as honest as the dataset behind them. Griffin AI publishes golden-dataset design notes; Mythos-class tools rarely explain theirs.