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Keel Pragma
EXPERTISE

AI Controls. Governed like any material risk.

AI brought inside the enterprise risk framework, with clear ownership, defined risk appetite and board-level oversight.

AI is now part of how institutions decide, serve and operate, and boards are accountable for it. We approach AI from the enterprise risk side, not the engineering side: the same disciplines of ownership, appetite, controls and monitoring that govern any material risk, applied to AI use across the institution. Model development and technical testing remain with your technical teams or specialist providers.

OwnershipInventoryRisk classificationAppetiteThird partiesBoard oversight
WHAT WE DO

Services in practice.

Four services, each aligned with a function of the NIST AI Risk Management Framework (AI RMF 1.0).

KEEL PRAGMA
AI inside the
enterprise risk framework
01
AI policy and governance
NIST AI RMF · GOVERN
AI policy inside the ERM and GRC framework
Clear ownership and accountability for each AI use
Committee mandates and escalation routes
Board oversight and reporting
02
AI inventory and risk classification
NIST AI RMF · MAP
Inventory of AI use across the institution
Context and intended purpose of each use
Risk tiering by impact on customers and operations
Benefits weighed against risks
03
AI within the risk appetite
NIST AI RMF · MEASURE
Appetite statements and limits for AI use
Key risk indicators and metrics
Monitoring and reporting against limits
Breach escalation and periodic review
04
Third-party and vendor AI
NIST AI RMF · MANAGE
Due diligence on AI in vendor products
Contract terms: transparency, data use, audit rights
Ongoing vendor monitoring
Incident response and exit plans
TYPICAL DELIVERABLES

What you receive.

  • AI policy aligned with the ERM framework
  • AI use-case inventory and classification method
  • AI statements within the risk appetite
  • Oversight and reporting arrangements for the board
  • Third-party AI due diligence checklist
  • Framework alignment with the NIST AI RMF
RELATED EXPERIENCEGoverning algorithmic decision systemsRead the case study → RELATED TRAININGAI oversight for boards and executivesView training →

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