Identify & classify
Maintain an inventory of AI use cases, tools, owners, data, vendors, intended users, business purpose, and risk tier.
AI Governance & Risk
Create accountable AI adoption before scale creates unmanaged exposure.
DTXI helps organizations establish policies, decision rights, use-case oversight, risk assessment, approval, monitoring, and reporting for responsible AI use.
Governance Lifecycle
Manage AI from idea to retirement.
The governance model should apply proportionate controls throughout the lifecycle rather than rely on a one-time approval.
Assess & approve
Evaluate impact, data use, security, privacy, legal, ethical, operational, and third-party considerations before deployment.
Implement controls
Apply policies, human oversight, access, testing, documentation, transparency, monitoring, and incident requirements.
Monitor & improve
Review performance, risk indicators, changes, incidents, vendor updates, control effectiveness, and retirement decisions.
Service Areas
The components of an enterprise AI governance capability.
DTXI adapts the model to the organization’s AI maturity, regulatory environment, use-case risk, and existing governance structures.
AI Governance Framework
Principles, governance bodies, decision rights, responsibilities, escalation, reporting, and integration with existing risk and technology governance.
AI Policies & Standards
Acceptable use, development and procurement requirements, data handling, human oversight, documentation, monitoring, incident, and exception expectations.
AI Use-Case Inventory
Structured registration of AI systems and use cases, business purpose, owners, data, vendors, risk tier, approval status, and review schedule.
AI Risk Assessment
Assessment criteria and risk registers covering impact, data, security, privacy, bias, reliability, transparency, legal, operational, and third-party concerns.
Approval & Oversight Workflow
Proportionate review gates, evidence requirements, accountable approval, conditions of use, residual-risk acceptance, and change triggers.
Monitoring & Reporting
Risk indicators, control status, incidents, material changes, vendor developments, performance concerns, and executive reporting.
Operating Model
Place accountability where decisions are made.
AI governance should connect business ownership, technology, information security, privacy, legal, risk, procurement, and internal assurance without creating an impractical approval bottleneck.
Business ownerPurpose, value, impact, process integration, and accountable use
Technical ownerArchitecture, implementation, performance, change, and support
Control functionsSecurity, privacy, legal, risk, procurement, and compliance review
Governance forumRisk-tier decisions, exceptions, escalation, monitoring, and oversight
Typical Deliverables
Artifacts that turn principles into repeatable decisions.
Deliverables are designed to integrate with existing governance rather than create a separate document set that teams cannot operate.
Governance foundation
AI governance charter, operating model, roles, committee or forum design, decision rights, reporting, and implementation roadmap.
Policy framework
AI acceptable-use policy, standards, control requirements, procurement criteria, development guidance, and exception process.
Risk and approval toolkit
Use-case inventory, classification model, risk assessment, approval workflow, evidence checklist, risk register, and acceptance criteria.
Monitoring and assurance package
Control register, review schedule, metrics, incident triggers, change criteria, vendor-monitoring requirements, and executive reporting templates.
Framework References
Recognized AI governance sources, adapted to context.
Frameworks provide structure, but the operating model must still reflect the organization’s actual decisions, use cases, and accountability.
NIST AI Risk Management FrameworkISO/IEC 42001ISO/IEC 23894OECD AI PrinciplesResponsible-AI principles and regulatory expectationsExisting enterprise risk, privacy, security, and procurement controls
