AI inventory and ownership
Many organisations still cannot answer which AI systems are live, who owns them, and which business decisions they influence.
AI governance advisory
Governance for AI that is already close to the business.
OCG Dubai helps leadership teams clarify what needs control, what needs evidence, and what should not scale until oversight is stronger.
Typical trigger
AI activity is moving faster than governance, and leadership needs a clearer operating position.
Oversight and escalation
The weak point is often not policy language but the absence of clear review thresholds, interruption rules, and escalation paths.
Cross-border obligations
Onshore UAE requirements, DIFC and ADGM regimes, and EU exposure can all land differently depending on the workflow and customer context.
Evidence and monitoring
Leadership teams need an evidence model that can survive client diligence, internal audit, and regulatory questions.
How we engage
Three common entry points.
The work is usually narrower than a broad responsible-AI programme. Most organisations need clarity on ownership, controls, and regulatory posture first.
Service line
Governance readiness assessment
A structured review of live and planned AI use cases, current controls, ownership gaps, and regulatory exposure.
Typical work includes
- AI inventory across business functions and vendors
- Risk classification and control-gap review
- Ownership map across business, legal, risk, data, and engineering
- Executive summary with priority actions
Service line
Policy and control design
Practical governance design for organisations that need clearer review thresholds, evidence requirements, and escalation paths before broader deployment.
Typical work includes
- Human-oversight and escalation model
- Logging, evidence, and monitoring expectations
- Policy language linked to operating practice
- Control model for customer-facing and commercially material use cases
Service line
Regulatory and board readiness
Support for organisations that need a cleaner position on PDPL, DIFC, ADGM, and cross-border AI obligations before clients, auditors, or regulators raise the bar.
Typical work includes
- Readiness pack for governance and data-protection questions
- Board and executive briefing materials
- Implementation roadmap with accountable owners
- Recurring review structure for live systems
Delivery model
Practical governance work, not policy paperwork alone.
Focused working sessions with business, legal, risk, data, and engineering owners.
Document review of policies, AI use cases, data flows, and current control material.
A narrower governance position on what needs action now, what can wait, and what should not proceed without stronger controls.
A practical roadmap rather than a policy deck with no operating owner.
Next step
Start with governance readiness.
If AI activity is already touching customer, pricing, workflow, or regulatory questions, begin with a focused review of controls, ownership, and evidence.