Accountable Ownership
Make sponsors, system owners, control owners, approvers and risk-acceptance authority explicit.
AI Governance & Risk
Build an AI Governance Framework That Turns Principles Into Accountable Controls
DataConsultant helps enterprise AI, data, technology, risk, privacy, security and business teams create a practical governance system for how AI is proposed, built, bought, evaluated, approved, monitored, changed and retired. The framework can cover predictive AI, generative AI, RAG, copilots, agents and third-party AI services.
AI inventory, accountable owners and risk classification
Proportionate lifecycle gates and approval authority
Policy-to-control mapping across data, model, security and human oversight
Monitoring, incidents, exceptions and evidence for ongoing governance
Engagement scope, timeline, responsibilities and commercial terms are confirmed after scoping. The service supports governance design and implementation; it does not guarantee regulatory compliance, certification or risk elimination.
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InventoryUse case, owner, users, data, model, supplier and deployment context02
ClassifyImpact, autonomy, sensitivity, regulatory exposure and risk tier03
ControlRequired reviews, evidence, human oversight, security and release gates04
AssureEvaluation, monitoring, incidents, exceptions, changes and retirementGovernance decisions
Business and system ownerNamed
Risk tier and review depthDefined
Release and exception authorityAssigned
Evidence retention and review cadenceTraceable
Control lenses
Data & PrivacyPurpose, quality, provenance, access, retention
Security & SafetyThreats, misuse, resilience, safeguards
Model & EvaluationPerformance, fairness, robustness, limits
Human & OperationsOversight, escalation, monitoring, incidents
Proportionate Risk
Apply deeper review where impact, autonomy, data sensitivity and consequences justify it.
Lifecycle Control
Move governance into intake, design, build, acquisition, release, change and retirement decisions.
Decision Evidence
Create repeatable records for evaluations, approvals, exceptions, monitoring, incidents and reviews.
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Why AI Governance Breaks Down as Enterprise Adoption Scales
AI risk is rarely controlled by a policy document alone. Problems emerge when organisations cannot see the full AI portfolio, apply consistent decisions, connect technical evidence to approval authority, or govern rapid model and supplier change.
Shadow and unregistered AI
Teams adopt copilots, APIs, models and SaaS features without a shared inventory of purpose, owners, data, users or operating exposure.
Unclear approval authority
Business, product, legal, risk, security and AI teams review the same use case but nobody is clearly accountable for release, exception or residual-risk decisions.
One-size-fits-all controls
Low-impact productivity tools and high-impact decision systems are forced through the same process, creating either excessive friction or insufficient scrutiny.
Weak data and model evidence
Approval decisions are made without consistent evidence on data provenance, evaluation coverage, limitations, human oversight, security or expected operating conditions.
Third-party AI is under-governed
Supplier models, embedded AI features, data-use terms, sub-processors, model changes and service dependencies are not tied to a repeatable due-diligence process.
Controls stop at go-live
Teams approve an initial release but lack triggers for re-evaluation, change review, monitoring, incident escalation, exception expiry and eventual retirement.
Move From Ad Hoc AI Approvals to a Reusable Governance System
Start by identifying where AI enters the organisation, which decisions are inconsistent, where evidence is missing and which use cases need stricter control.
Direct Definition
What an Enterprise AI Governance Framework Actually Governs
An AI governance framework creates the organisation-wide decision and control system for responsible AI. It defines what must be registered, how risk is classified, which evidence is required, who reviews and approves, what minimum controls apply, how exceptions are handled, what is monitored after release and what changes trigger reassessment.
The framework should operate across business, product, data, model, security, privacy, legal, supplier and assurance responsibilities. It should be specific enough to use in delivery workflows while remaining adaptable across predictive models, generative AI, RAG, copilots, agents and externally supplied AI services.
Governance scopeAI definition, use-case boundary, inventory, policy hierarchy and applicability.
Risk modelRisk tiers, impact criteria, specialist reviews and proportional control depth.
Lifecycle gatesIntake, design, build or buy, validate, release, change, monitor and retire.
Evidence systemAssessments, evaluations, approvals, exceptions, incidents, monitoring and records.
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Business Outcomes an Operational AI Governance Framework Can Support
The framework is intended to improve decision consistency, accountability and traceability. Actual outcomes depend on leadership sponsorship, implementation, evidence quality, technical controls, adoption and the organisation’s risk context.
Visible AI inventory
Create a common view of AI systems, use cases, owners, suppliers, risk tiers, status and review obligations.
Clear decision rights
Define who proposes, reviews, validates, approves, monitors and accepts residual risk for each class of AI.
Proportionate controls
Match review depth to business impact, autonomy, sensitive data, user exposure, failure consequences and obligations.
Faster governed decisions
Replace repeated negotiation with defined intake questions, evidence requirements, routes and escalation paths.
Stronger third-party governance
Use consistent due diligence for external models, embedded AI features, service changes, data handling and dependencies.
Release evidence discipline
Connect intended use, risk assessment, evaluation results, controls, limitations and human oversight to release decisions.
Change and incident traceability
Define what changes require reassessment and how incidents, exceptions, drift and control breaches are escalated.
Better audit readiness
Maintain decision records and control evidence that can support internal assurance, customer reviews and regulatory preparation.
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AI Governance Framework Scope: From Policy to Lifecycle Control
Final scope is tailored to the AI portfolio, risk appetite, operating model and obligations. A comprehensive framework typically connects the following capability areas rather than treating them as separate documents.
Principles & policy model
Define governance principles, policy hierarchy, acceptable-use boundaries and minimum enterprise requirements.
- AI policy structure
- Applicability rules
- Control ownership
AI inventory & taxonomy
Create a consistent register for systems, use cases, models, suppliers, business owners and operating context.
- System registration
- Ownership fields
- Lifecycle status
Risk & impact classification
Design risk tiers and assessment criteria that determine review depth, evidence and approval requirements.
- Impact criteria
- Risk tiers
- Escalation triggers
Operating model & RACI
Clarify executive oversight, governance forums, product ownership, specialist review and risk-acceptance authority.
- Decision rights
- Committee design
- Escalation routes
Lifecycle & release gates
Embed governance at intake, design, build or buy, validation, release, change, monitoring and retirement.
- Stage gates
- Approval criteria
- Change control
Evaluation & model controls
Define evidence for performance, reliability, fairness, safety, robustness, explainability and known limitations.
- Evaluation requirements
- Thresholds
- Release evidence
Data, privacy & security
Connect AI governance to data provenance, quality, access, personal-data controls, secrets, threats and resilience.
- Data controls
- Privacy review
- Security requirements
Third-party AI governance
Apply procurement and supplier controls to external models, APIs, SaaS AI features and downstream dependencies.
- Due diligence
- Contract inputs
- Change notification
Human oversight & safeguards
Define intervention, override, escalation, user disclosure, fallback and prohibited-use controls based on context.
- Human review
- Override paths
- Usage safeguards
Incidents & exceptions
Establish time-bound exceptions, incident taxonomy, severity, escalation, remediation, communication and closure evidence.
- Exception register
- Incident workflow
- Corrective action
Monitoring & reassessment
Define operational indicators, review cadence and material-change triggers for models, prompts, data, tools and suppliers.
- Control monitoring
- Change triggers
- Periodic review
Evidence, reporting & literacy
Standardise decision records, governance reporting, management information, role guidance and AI literacy expectations.
- Evidence templates
- KPI reporting
- Role-based training
Need a Framework That Fits Your AI Portfolio and Risk Appetite?
Scope the governance model around actual use cases, business impact, supplier dependencies, standards, jurisdictions and existing enterprise controls instead of importing a generic checklist.
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Governance Operating Architecture: Who Decides, Who Controls and Who Assures
An effective framework separates oversight, accountable business ownership, specialist control review, technical implementation and independent assurance. Exact roles vary by organisation and should be integrated with existing risk and governance structures.
Board & Executive Leadership
Set AI risk appetite, strategic boundaries, escalation expectations and executive accountability for material AI use.
AI Governance Office or Forum
Own the framework, triage use cases, coordinate specialist reviews, maintain standards and report portfolio-level risk.
Business, Product & Model Owners
Own intended use, users, outcomes, operating controls, evidence, monitoring and day-to-day risk decisions within authority.
Risk, Legal, Privacy & Security
Apply specialist requirements, review material exposure, advise on obligations and challenge control design where required.
Engineering, Data & MLOps/LLMOps
Implement technical controls, evaluation, access, deployment gates, logging, monitoring and change-management evidence.
Independent Assurance & Audit
Where appropriate, independently review whether governance design and operating evidence meet defined requirements.
Illustrative governance gates across the AI lifecycle
1. Idea & Intake
2. Risk Classify
3. Design / Buy
4. Build & Evaluate
5. Approve & Release
6. Monitor & Change
7. Retire
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Deliverables That Turn AI Governance Into an Operating Capability
The exact pack is defined by scope. Deliverables should connect policy, decision rights, controls, evidence and implementation rather than leave governance as a high-level principles document.
DELIVERABLE 01
AI governance charter
Purpose, scope, principles, ownership, policy hierarchy, decision forums and responsibility boundaries.
DELIVERABLE 02
AI inventory & taxonomy
Registration fields, system categories, owners, suppliers, risk information, status and lifecycle records.
DELIVERABLE 03
Risk-tiering method
Criteria, scoring or decision logic, escalation triggers and proportionate review requirements.
DELIVERABLE 04
Operating model & RACI
Executive, governance, business, control, engineering and assurance roles with decision rights.
DELIVERABLE 05
Lifecycle-gate design
Intake, assessment, approval, evaluation, release, change, monitoring and retirement checkpoints.
DELIVERABLE 06
Policy & control library
Minimum controls across intended use, data, model, security, privacy, human oversight and operations.
DELIVERABLE 07
Supplier governance pack
Due-diligence questions, evidence expectations, change controls, responsibility and escalation inputs.
DELIVERABLE 08
Monitoring & incident model
Signals, material-change triggers, exceptions, incidents, review cadence and corrective-action workflow.
DELIVERABLE 09
Evidence & reporting templates
Assessment, evaluation, approval, exception, risk-acceptance and governance reporting artefacts.
DELIVERABLE 10
Implementation roadmap
Priorities, owners, dependencies, pilots, workflow enablement, training, tooling and review milestones.
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How the Engagement Moves From AI Inventory to Working Governance
The delivery sequence is adapted to existing policies, model-risk processes, AI maturity and implementation needs. Stages may overlap when evidence and stakeholders are available.
Align & Scope
Confirm objectives, sponsors, AI definition, risk appetite, jurisdictions, standards and required decisions.
Inventory & Discover
Review AI systems, use cases, suppliers, policies, workflows, committees, incidents and available evidence.
Classify & Assess
Identify material risks, impact factors, gaps, obligations, control overlaps and risk-tier requirements.
Design
Define policy structure, operating model, RACI, risk tiers, assessment methods, controls and lifecycle gates.
Operationalise
Convert the framework into intake, review, approval, evidence, exception, supplier and reporting workflows.
Validate & Train
Apply the framework to representative AI use cases, resolve usability gaps and train accountable roles.
Monitor & Improve
Establish review cadence, metrics, incident feedback, change triggers, assurance and continual improvement.
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Standards, Regulatory and Control Mapping for AI Governance
The framework can map enterprise controls to relevant external references without treating standards as interchangeable or presenting consulting as legal advice or certification. Applicability depends on organisation, role, sector, system purpose, jurisdiction and current effective dates.
NIST AI Risk Management Framework
NIST AI RMF 1.0 organises AI risk-management activity around four functions: Govern, Map, Measure and Manage. A DataConsultant framework can use these functions as a cross-reference for governance outcomes, risk identification, evaluation and risk treatment while tailoring controls to the organisation.
Review the NIST AI RMF source ↗ISO/IEC 42001:2023
ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining and continually improving an AI management system. Governance design can align responsibilities, processes, risk and opportunity management, operational controls, performance evaluation and continual improvement to relevant management-system needs.
Review ISO/IEC 42001 ↗ISO/IEC 23894:2023
ISO/IEC 23894 provides guidance for organisations that develop, produce, deploy or use AI to manage AI-related risk and integrate risk management into organisational activities. It can inform risk processes alongside existing enterprise risk practices.
Review ISO/IEC 23894 ↗EU Artificial Intelligence Act
The EU AI Act became generally applicable on 2 August 2026 with phased exceptions. Current Commission guidance includes transparency obligations applying from August 2026, while certain high-risk-system requirements have later application dates. Governance should map roles, classifications, evidence and controls to the organisation’s actual obligations.
Review the European Commission AI Act overview ↗DPDP Act 2023 & DPDP Rules 2025
AI governance involving digital personal data should connect to the organisation’s privacy responsibilities. The DPDP Rules, 2025 use a phased commencement schedule, so implementation should distinguish provisions already in force from later effective dates and align with authorised legal interpretation.
Existing Risk, Security and Model Governance
A new AI framework should reuse existing enterprise mechanisms where they are effective: information security, privacy, model risk, procurement, records, change management, incident response, internal control and audit. The aim is a coherent control system, not duplicate governance for every technology.
Boundary: standards mapping and regulatory readiness support do not constitute legal advice, statutory audit, certification, conformity assessment or a guarantee of compliance.
Turn Governance Design Into Working Lifecycle Controls
Connect policy and standards mapping to intake workflows, risk tiers, evaluation evidence, release gates, monitoring, exceptions, supplier reviews and accountable operating forums.
Engagement & Pricing
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Choose the AI Governance Engagement That Matches the Decision You Need to Make
DataConsultant does not publish a fixed fee for this AI Governance Framework service. Current public India pricing for AI-governance work varies materially by scope—from narrow policy and audit work to multi-system enterprise implementation—so an aggregated market range would not be decision-useful for this page. Pricing is therefore quote-led.
Commercial basis: scope is shaped by AI portfolio size, business units, jurisdictions, standards mapping, stakeholder groups, control depth, workshops, implementation needs, onsite requirements and ongoing support. Timeline is confirmed after scoping.
Governance Framework Diagnostic
For organisations that need a clear view of current governance gaps, risk exposure and priority decisions before designing the target framework.
Commercial treatmentRequest a Quote
Can include- AI governance maturity and gap review
- Portfolio and stakeholder discovery
- Priority risk and control findings
- Target-state recommendations
Enterprise AI Governance Framework
For organisations that need the full policy, operating model, risk-tier, lifecycle, control and evidence architecture.
Commercial treatmentRequest a Quote
Can include- Framework, charter and policy model
- Risk tiering and assessment method
- RACI, committees and decision rights
- Lifecycle controls and evidence templates
Governance Implementation & Controls
For organisations that have a target model and need workflows, tooling patterns, governance gates, training and pilot operationalisation.
Commercial treatmentRequest a Quote
Can include- Intake and approval workflows
- Inventory and evidence templates
- Evaluation and monitoring integration
- Pilot, training and rollout support
Retained AI Governance Advisory
For governance teams that need recurring specialist input on framework updates, difficult use cases, suppliers, exceptions and control evolution.
Commercial treatmentRequest a Quote
Can include- Governance office support
- Use-case and exception review
- Standards and control updates
- Governance reporting and improvement
Pricing note: no public DataConsultant fee for this exact service was verified for this page. Comparable public Indian offerings use materially different scope, depth and delivery models, so competitor figures have not been presented as a DataConsultant price or as a market benchmark.
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Use This Service When the Need Is Enterprise Governance, Not a Single Technical Test
The framework is strongest when the organisation needs repeatable governance across multiple AI systems, teams or suppliers. Narrow technical or legal needs may require a different specialist service.
Good fit for AI governance framework work
- Your AI portfolio is expanding across multiple teams or business units.
- In-house and third-party AI need one consistent governance model.
- Current policy is too high-level to drive approvals and release decisions.
- Generative AI, RAG or agents introduce new autonomy, data or monitoring risks.
- Roles between business, AI, legal, risk, security and privacy are unclear.
- You need repeatable evidence for customers, internal assurance or regulatory readiness.
May need another or additional service
- A single model only needs a focused technical evaluation or test.
- The requirement is limited to penetration testing or security testing.
- You need a formal legal opinion, statutory audit or certification-body decision.
- No accountable executive or business owner can participate in governance decisions.
- The expectation is a guarantee that AI will be risk-free or always accurate.
- The requirement is only vendor configuration with no governance-design need.
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What DataConsultant Needs From Your Organisation
Better evidence produces a framework that fits real workflows and avoids duplicating existing controls. Missing information should be recorded as a limitation rather than silently assumed.
AI portfolio and use cases
Known AI systems, pilots, copilots, agents, vendor tools, model dependencies, intended users and business owners.
Policies and operating model
Current AI, risk, privacy, security, procurement, model-risk, data and acceptable-use policies plus committee structures.
Architecture and data flows
System diagrams, model and provider information, retrieval sources, data classifications, APIs, tools and deployment patterns.
Risk and regulatory context
Jurisdictions, sectors, customer requirements, risk appetite, internal control frameworks and material compliance assumptions.
Evaluation and assurance evidence
Test plans, benchmark results, model cards, impact assessments, red-team findings, privacy or security reviews and sign-offs.
Incidents, exceptions and audit findings
Known failures, near misses, control exceptions, supplier issues, complaints, internal audit findings and remediation plans.
Typical exclusions unless separately scoped: legal opinions, formal certification, accredited conformity assessment, statutory audit, penetration testing, model development, data remediation and enterprise tooling implementation are not automatically included in an AI governance framework design engagement.
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Why Consider DataConsultant for an AI Governance Framework
The service is positioned around the operating connection between business decisions, AI engineering, data, risk, controls and assurance rather than a governance document in isolation.
Business-led risk decisions
Start with intended use, users, outcomes, consequences, risk appetite and accountable business decisions before selecting controls.
Framework-to-control traceability
Connect principles and policy to concrete lifecycle gates, technical evidence, review authority, exceptions and monitoring.
Technology-agnostic design
Build governance around system purpose, risk and evidence so it can work across clouds, model providers, AI platforms and tooling choices.
Transparent responsibility boundaries
Document where DataConsultant advises and where client owners, legal counsel, certification bodies or specialist testers must decide or assure.
Operationalisation, not policy only
Design the supporting intake, assessment, approval, supplier, exception, incident, monitoring and reporting processes needed to make governance usable.
Knowledge transfer built into scope
Use templates, role guidance, workshops and handover so internal teams can own and improve the framework after delivery.
Need a Governance Scope and Commercial View Built Around Your Actual AI Estate?
Share the number of AI systems or use cases, current governance maturity, jurisdictions, stakeholder groups and expected deliverables so the engagement can be scoped without invented assumptions.
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AI Governance Framework FAQs
Answers to common buyer questions about scope, ownership, risk tiering, generative AI, standards, regulation, deliverables, pricing, implementation and supplier governance.
What is an AI governance framework?
An AI governance framework is the organisation-wide system of principles, roles, decision rights, risk classification, lifecycle gates, minimum controls, evidence requirements, monitoring, incident handling and review practices used to govern how AI is proposed, built, bought, deployed, changed and retired.
What is included in DataConsultant’s AI Governance Framework service?
Scope can include executive and stakeholder discovery, AI inventory design, risk-tiering criteria, governance principles and policy structure, target operating model, RACI and committee design, AI impact and risk assessment methods, lifecycle gates, control requirements, third-party AI governance, monitoring, incident and exception processes, evidence templates, training requirements and an implementation roadmap. Final scope is agreed after discovery.
Who should own AI governance?
Accountability usually spans an executive sponsor, business or product owners, AI and data leaders, risk, legal, privacy, security, architecture, engineering and assurance functions. The framework should make decision rights explicit instead of assuming that one central team owns every AI risk or release decision.
Does the framework cover generative AI, RAG, copilots and AI agents?
Yes, these can be included. The control design should reflect the system’s actual purpose, data, model dependencies, retrieval sources, tools, autonomy, user population and consequences of failure. Generative AI and agents may require additional controls for prompt and retrieval behaviour, content safety, tool permissions, data leakage, human escalation and continuous evaluation.
How are AI systems risk-tiered?
Risk tiering is tailored to the organisation and may consider intended use, decision impact, affected people, autonomy, data sensitivity, legal or regulatory exposure, model capability, external access, third-party dependencies, explainability needs, reversibility, safety consequences and the severity and likelihood of failure. The result should determine proportionate review and evidence requirements.
Can the framework align with the NIST AI Risk Management Framework?
Yes. Controls and operating practices can be mapped to the NIST AI RMF functions Govern, Map, Measure and Manage where useful. NIST AI RMF is a risk-management reference, not a certification issued by DataConsultant, and the mapping should be tailored to the organisation’s context and current framework version.
Is this the same as ISO/IEC 42001 certification?
No. ISO/IEC 42001 specifies requirements for an AI management system. DataConsultant can help design governance, controls, evidence and operating practices that may support an organisation’s management-system objectives, but this service does not itself issue certification or replace an accredited certification-body audit.
How can the EU AI Act be considered in the framework?
The framework can include role, inventory, risk-classification, transparency, documentation, human-oversight, monitoring, supplier and evidence requirements relevant to AI Act readiness. Applicability varies by role, system, jurisdiction and current phased dates, so legal interpretation and formal compliance conclusions should be made by authorised legal or compliance specialists.
How do India’s DPDP Act and DPDP Rules affect AI governance?
Where AI uses digital personal data, the governance design can map privacy responsibilities, purpose and data-use controls, access, retention, supplier responsibilities, incident processes and evidence to the organisation’s obligations under the Digital Personal Data Protection Act, 2023 and the phased Digital Personal Data Protection Rules, 2025. Legal interpretation remains the client’s responsibility with appropriately qualified advisers.
What deliverables can we expect?
Typical deliverables can include an AI governance charter, policy and control library, AI inventory and taxonomy, risk-tiering method, AI risk or impact assessment template, target operating model and RACI, lifecycle and release-gate design, supplier assessment pack, monitoring and incident model, evidence and reporting templates, training plan and phased implementation roadmap.
How long does an AI governance framework engagement take?
A reliable timeline is confirmed after scoping. Duration depends on the number and diversity of AI systems, business units and jurisdictions, stakeholder availability, maturity of existing risk and policy processes, required standards or regulatory mapping, evidence quality, workshop and review cycles, and whether implementation or pilot operationalisation is included.
How is AI governance framework pricing calculated?
DataConsultant does not publish a fixed fee for this service. Pricing is scope-led and confirmed through a Request a Quote process after the number of AI systems and use cases, stakeholder groups, jurisdictions, current governance maturity, standards and regulatory mapping, control depth, workshops, deliverables, implementation needs and ongoing advisory requirements are understood.
Can DataConsultant help implement the framework after it is designed?
Yes. Implementation support can be scoped for AI inventory workflows, intake and approval gates, assessment templates, policy and control rollout, evaluation and monitoring integration, supplier governance, reporting, training, MLOps or LLMOps alignment and governance operating cadence. Client decision owners remain accountable for approvals and risk acceptance.
Can the framework govern third-party AI services and foundation models?
Yes. Supplier governance can cover intended use, data handling, model and service dependencies, contractual responsibilities, documentation, security and privacy review, evaluation evidence, change notification, exit considerations, monitoring and escalation. The exact due-diligence depth should be proportionate to the use case and risk.
Which platforms and technologies can the framework cover?
The governance framework can be designed to work across cloud AI services, foundation-model providers, machine-learning platforms, model registries, MLOps and LLMOps pipelines, RAG and vector-store architectures, API gateways, agent tools, observability platforms, data-governance tooling and enterprise applications. The control design remains requirements-led and vendor-neutral unless a specific platform is explicitly in scope.
What information should we prepare before the engagement?
Useful inputs include an AI system or use-case inventory, business objectives, existing AI or acceptable-use policies, organisation and committee structures, risk appetite, architecture and data-flow information, model or vendor documentation, evaluation results, privacy and security assessments, incidents, audit findings, procurement processes, regulatory context and access to accountable stakeholders.
AI Governance Framework Enquiry
Request an AI Governance Framework Scope Review
Share your contact details and requirement. DataConsultant can review likely scope, evidence needs, stakeholder involvement, governance deliverables and the appropriate next step.