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.
Before an agent ships, test what it can do, not just what it says. A defensive checklist covering tools, permissions, injection paths and stop controls.
RAG fetches documents at query time. CAG preloads them into the model context and caches them. Compare speed, freshness, access control and failure modes.
Yes. Loading a pickle can execute code, and many .pth model files use pickle. Load only trusted files, prefer safetensors, and scan what you download.
Prompt injection makes an LLM follow attacker text as instructions. It may never be fully fixed, so limit what the model can do and plan for failure.
An agent that browses reads pages written by strangers. This checklist covers isolation, least privilege, approvals, output checks and tests for engineers.
Prompt engineering is writing and testing the instructions you give a model. Teams do it well with success criteria, evals and wary handling of inputs.
AI observability means recording prompts, retrieval, tool calls, versions and costs so you can explain and audit behavior. Log traces with care.
Expose read-only data to an LLM through MCP resources: pick URIs, declare the capability, validate every read, and keep scopes narrow. Steps for Node.js.
The NIST AI RMF is voluntary guidance: Govern, Map, Measure, Manage. Product managers decide scope, owners, risk thresholds and release gates.
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