Governance from principle
to practice.
A practical framework for governing AI from strategy and policy through evidence, monitoring, and ongoing oversight.
From principles to practice.
DecisionX translates AI governance expectations into practical processes organizations can implement, measure, evidence and continuously improve.
Govern
Establish accountable governance structures, policies, roles, risk appetite, decision rights and escalation.
Map
Build the inventory. Understand use cases, ownership, data, vendors, dependencies and regulatory context.
Measure
Classify risk, define controls and metrics, test and evaluate, and collect evidence.
Manage
Monitor systems, manage changes and exceptions, report risk, maintain documentation and improve.
Practical governance built around your needs.
AI Governance Strategy & Program Design
Define governance models, roadmaps, committees, charters and integration with existing risk structures.
AI & Model Inventories
Discover, classify and document AI use cases, ownership, vendors, data and lifecycle information.
AI Risk Assessment & Classification
Establish risk criteria, tier use cases and determine governance obligations proportionate to impact.
Policy & Framework Development
Create tailored policies, standards, risk-taxonomy and governance artifacts.
Control Design & Implementation
Map requirements to controls and operationalize governance across the lifecycle.
Testing, Metrics & Evidence
Assess both existence and actual values so governance can be demonstrated—not merely asserted.
Independent Validation & Assurance
Provide objective challenge, readiness review and validation of governance effectiveness.
Regulatory & Audit Readiness
Prepare organizations for boards, investors, auditors, regulators and counterparties.
Board, Executive & Staff Education
Build the governance literacy needed to make sound AI decisions at every level.