What is shadow AI and how do you find it in your company?
Shadow AI is AI use your security team has not approved or listed. Learn the data risks, how to find it without a witch hunt, and how to offer a safe path.
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.
Shadow AI is AI use your security team has not approved or listed. Learn the data risks, how to find it without a witch hunt, and how to offer a safe path.
Shadow AI is staff using AI tools nobody approved. It leaks data, adds untracked agents and creates gaps. Find it, set limits and offer a safe option.
Treat AI code as untrusted input: review it, scan it, check packages it names, limit data it sees and restrict what agent tools can do. A practical list.
Generative AI answers the same prompt differently, so one passing test proves little. Use NIST and OWASP risk lists and enforce limits outside the model.
Secrets reach prompts through pasted logs, env files, tool output and retrieved documents, then persist in transcripts. Reduce exposure, scan inputs and rotate quickly.
Run agent commands in a disposable container or microVM with no secrets, a read-only base, limited network and resource caps, and a human gate on anything that leaves it.
Docker can sandbox a local MCP server that would otherwise run with your privileges. Use stdin, a read-only root, no capabilities and limited network.
Docker Model Runner pulls open models and serves them on an OpenAI-compatible API, keeping prompts on your machine. Setup basics and the gaps to close.
Stage vetted models internally, control the cache location, and go offline in production. Here is the setup and what a mirror does not verify.
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