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.
The most grounded concerns about AI are not sci-fi scenarios; they are prompt injection, data leakage, supply chain risk in models, and opaque decisions. Here is how each one actually shows up.
Prompt injection remains the LLM01 entry on the OWASP LLM Top 10 for a reason. A pragmatic look at the defense architectures that hold up in production this year.
The container security vulnerabilities that actually get exploited, where they hide across the image lifecycle, and a practical order for fixing them without rebuilding everything at once.
A practitioner's guide to picking a container scanning tool: what it should detect, where it fits in the pipeline, and how to avoid drowning in false positives.
When the test set is in the training set, the benchmark is broken. Security eval contamination is widespread and the mitigations are specific.
Anthropic's Claude Agent Skills let you package tools and context for Claude. Here's how that primitive compares to Griffin's security-specific workflow scaffolding.
A senior engineer's side-by-side look at Griffin AI and Mythos — why engine-grounded reasoning beats pure-LLM security intuition when the audit clock starts.
AI security solutions now span two very different categories — securing AI systems and using AI to secure everything else — and buyers who conflate them end up with the wrong tool.
The best AI tool for resume building is the one that improves your document without quietly harvesting the personal data on it. Here is how to judge these tools on privacy and security, not just polish.
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