An operating framework for enterprise AI governance
Assign ownership, risk classification, review evidence, access policy, monitoring, incident handling, and retirement without turning governance into a static checklist.
Read articleBack to all guidance
Guidance library
AI security
Bound agent access and reduce prompt, data, identity, and tool-execution risk.
6 articlesGuidance library
Reviewed enterprise architecture guidance
Narrow the library by controlled topic, audience, pillar, or API product.
Security architecture for enterprise AI agents
Bound identities, context, tools, operations, outputs, and audit evidence across the path from a user request to enterprise systems.
Read article
Using Claude Code in a governed engineering workflow
Verify installation and commands against current documentation, then place repository access, approvals, secrets, testing, and audit boundaries around the tool.
Read article
A decision framework for local LLM deployment
Evaluate self-managed inference through capability, data handling, hardware, reliability, patching, observability, and team ownership.
Read article
MCP in enterprise AI architecture
Use Model Context Protocol as a connection standard while keeping identity, authorization, business contracts, tool safety, and source operations explicit.
Read article
Evaluating OpenClaw for controlled agent workflows
Review a self-hosted agent gateway through its channel access, tools, credentials, operating permissions, isolation, review points, and recovery path.
Read article