The Map: A structured knowledge framework that organizes risk artifacts into interconnected layers, providing the guardrails AI agents need to operate safely.
A risk taxonomy is a structured knowledge map that organizes your organization's risk artifacts into interconnected layers. It defines what entities exist, how they relate, and what governance applies.
In regulated industries, this isn't optional—you're expected to have documented, auditable frameworks. But for AI agents, a well-structured taxonomy becomes something more powerful: a navigable knowledge graph that provides context and guardrails for every decision.
Risk artifacts organized in layers, from strategic requirements down to technical foundations.
Regulatory & Business requirements that drive everything below
Risk taxonomy and risk types classified across all domains
Governing documents that set boundaries and standards
Forums, mandates, and terms of reference that guide decisions
Process maps and procedures that define how work gets done
KRIs, RCSA, and management information that monitors effectiveness
Technical foundation that enables everything above
Comprehensive coverage across all risk domains—no gaps for AI to stumble into
Uniform standards applied everywhere—predictable structure for reliable navigation
Clearly articulated to staff and regulators—transparency builds trust
Each category linked to create a navigable knowledge graph for AI agents.
Risks
Policies
Governance
Processes
Controls
Products
Reports
Feeds
Data
Models
Systems
Banks continuously change, requiring systematic documentation updates during every change project. A stale taxonomy is worse than no taxonomy—it gives AI agents false confidence in outdated information.
Artifacts are locked for editing when change projects begin. The current state is preserved as baseline—creating a clear "before" snapshot.
Updates reflect post-change production state—methodology docs, model libraries, data dictionaries, curve inventories. Everything that changes gets documented.
After validation and sign-off, updated production views are checked in, creating complete audit trails. The taxonomy always reflects reality.
Why This Matters
When artifacts accurately represent current production processes, they become powerful tools for managing change efficiently and maintaining accountability. AI agents can trust the taxonomy—and so can regulators.
The taxonomy provides structure—but structure alone doesn't capture how decisions actually get made. That's where the Context Graph comes in: capturing the real paths people take through this carefully structured map.
Explore Context Graph →