AI Code Security Solutions: What to Evaluate Before Buying
AI code security solutions range from AI-assisted scanning to AI-generated fixes — here's what to actually test before trusting one with your pipeline.
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.
AI code security solutions range from AI-assisted scanning to AI-generated fixes — here's what to actually test before trusting one with your pipeline.
An AI accelerator is hardware built to speed up machine learning math. Once you offload models onto one, the security work shifts to the software and data around it.
A 4 GB safetensors file deserves the same signing, hashing, and provenance discipline as a container image. How to actually do it with Sigstore, OCI registries, and AIBOMs.
A security-focused buyer comparison of AI coding assistants in 2026: code quality risk, data exfiltration controls, license exposure, and policy enforcement.
New survey data on the state of agentic AI adoption shows enterprises racing to deploy autonomous agents faster than security teams can govern them.
Safeguard's 2026 AI Trust Report surveyed 1,412 developers and finds 91% use AI coding tools weekly, but only 34% trust the code it produces.
Jailbreaks against frontier models keep getting more sophisticated. The defense architectures that have proven durable, and the ones that get bypassed in weeks.
Jailbreak has two meanings today: removing restrictions on a device, and tricking an AI model into ignoring its safety rules. This guide covers both.
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.
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