Documentation
Guides for modeling, governing, and operating AI systems with Model Monster.
- Create, archive, unarchive, and delete systems Create, archive, restore, and permanently delete systems while understanding each lifecycle choice.
- System Versioning: Drafts, Releases, and History Understand system drafts, release versions, and inspect read-only version history.
- Review and release a system version Submit a Draft system version for review, record reviewer decisions, and verify the released signoff record.
- Review a system's audit log Inspect a system's recorded activity, read event context, and narrow the timeline with search and filters.
- System workspace and Blueprint Navigate a system workspace, interpret its governance Blueprint, maintain general settings, and choose the right detailed guide.
- Modeling resources and operations Inventory, add, and maintain resources, then model component-resource interactions with accurate operations and directions.
- Modeling components in a System Blueprint Choose useful component boundaries, add reusable definitions to a System Blueprint, and maintain component details and connections.
- AI-Assisted Blueprint Modeling Use an authorized MCP-connected agent to turn system evidence into a structured, reviewable Model Monster Blueprint.
- Creating and managing teams Create and manage teams, configure auto-approval, and archive, restore, or permanently delete teams.
- Invite and manage organization members Invite people to an organization, manage pending invitations, change organization roles, and remove organization access safely.
- Set up Okta SSO Connect Okta to Model Monster, configure access and role behavior safely, and verify that single sign-on works.
Governance Approach
Learn the concepts behind Model Monster's approach to AI governance.
- CORE: A blueprint for AI governance Learn how CORE maps an AI system's components, operations, resources, and execution into a shared System Blueprint for governance
- PRO: Turning system evidence into governance Learn how PRO brings policies, risk, and outcomes together to turn system evidence and context into reviewable governance work.