Eval Harness As Release Gate For AI Features
Shipping AI features without an eval harness is shipping without tests. Here is how to build one that actually gates releases without becoming a bottleneck.
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.
Shipping AI features without an eval harness is shipping without tests. Here is how to build one that actually gates releases without becoming a bottleneck.
Chain-of-thought helps LLMs with multi-step problems. For vulnerability reasoning, it helps — but only when the chain is grounded in structured evidence.
AI security protects models, training data, and agentic systems from prompt injection, poisoning, and unsafe autonomy — here's what it covers and how to build a program.
LLM security protects model weights, prompts, outputs, and the AI supply chain from injection, leakage, and compromise—here's what it covers and how to defend it.
Tracking remediation SLAs in spreadsheets is how programmes drift. Here is how to track SLAs in the same system that finds, fixes, and merges vulnerabilities.
Time from contract signature to first meaningful finding is the metric procurement cares about. Griffin AI and Mythos-class tools diverge in week one.
Gemini's pricing table favours long-context workloads. Security scans have long-context structure. The question is how much context fits into the architecture.
The model you think you're calling might not be the model that returns. Model substitution is a quiet supply chain risk that deserves explicit controls.
Prompt injection is OWASP's #1 LLM risk. Learn how it works, real CVEs like EchoLeak, and how to detect and defend against it.
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