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 coding assistants raise data leakage, insecure code and dependency risks. Set data rules, require review and scan the output with this policy checklist.
A toxic flow is a chain of tool calls where untrusted input, private data and an outbound channel meet. Map your MCP tools for that mix and cut one leg.
Treat guardrails like code: keep a versioned set of blocked and allowed cases, run it on every change, and track false blocks too. How to build one.
Guardrails drift when prompts, models and rules change. Build a versioned regression suite with expected outcomes and pass thresholds, and run it in CI.
Treat model output as untrusted input, limit agent permissions, vet models and data, and log everything. Ten practices from NCSC and OWASP LLM guidance.
Adopt AI in security by setting ownership, picking one use case, securing the AI itself, piloting and measuring. Seven steps built on NIST CSF 2.0.
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.
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