AI that organizations can trust.

Independent AI Governance & Operational Risk Frameworks for responsible enterprise adoption.

Learn more

What we do

We help organizations govern AI, manage risk, and operationalize controls at scale.

MindXO is an AI governance and risk management consultancy based in Dubai, UAE, serving banks, insurers, and regulated enterprises across the GCC.
MindXO helps organizations design and operationalize AI governance frameworks, risk management systems, and compliance structures aligned to the NIST AI Risk Management Framework, ISO/IEC 42001, and industry best practice.
MindXO's services include AI  governance framework design, AI risk tiering, and continuous monitoring across the AI system lifecycle.

AI Governance

We assess AI governance maturity across your organization and design the decision structures, accountability frameworks, and lifecycle controls needed to scale AI in regulated environments.

Our governance methodology is aligned to ISO/IEC 42001, the NIST AI RMF, and GCC regulatory expectations and built by a team with direct regulatory experience.

AI Risk & Resilience

We help financial institutions and regulated enterprises identify, tier, and manage AI-specific risks  from model drift and data quality to third-party vendor exposure.

Our risk assessment framework maps controls to the AI system lifecycle and produces audit-ready evidence for regulators, boards, and internal assurance functions.

Why MindXO

We bridge AI governance, risk management and  day-to-day workflows

Responsible AI by Design

Built for regulated environments by ex-regulator experts. Our frameworks align with global responsible AI standards.

✓ EU AI Act & GCC alignment
✓ Responsible AI policies
✓ Audit-ready governance

Enterprise AI Risks

We help organizations identify, tier, and manage AI risks across systems, processes, and people.

✓ Risk taxonomy & tiering
✓ Ownership & controls
✓ Assurance mechanisms

Operational Enablement

We operationalize governance and risk through structured workflows and oversight.

✓ AI systems inventory
✓ Risk & control workflows
✓ Evidence & monitoring

Vendor-Neutral

Governance without bias. We remain independent from AI vendors and system integrators.

✓ Vendor-neutral advice
✓ No delivery lock-in
✓ Long-term governance focus

Governance-First AI

We design governance and decision structures that enable organization to scale AI safely and responsibly.

✓ AI governance frameworks
✓ Decision rights & accountability
✓ Lifecycle oversight

We help you scale your AI adoption with enterprise-grade risk management and governance

Our services

Strategy. Governance. Risk Management.
We help organizations move from AI ambition to governed, risk-managed adoption.

Pillar I

AI Strategy & Governance

AI Maturity Assessment™
AI Governance Framework
Responsible AI Policy Suite

Pillar II

AI Risk Management

AI Operating Model
AI Risk Management
Risk Assessment Framework

Pillar III

AI Lifecycle Management

AI Systems Inventory
Risk Tiering
Continuous Monitoring
Learn more about AI risk management

Everything we deliver is anchored in measurable value, controlled risk, and accountable governance.

Learn more

Let’s talk governance and risk.
‍Whether you’re defining your AI strategy, strengthening governance, or operationalizing risk through tooling, we’ll help you take the right next step.

Get in touch

→ Get in touch

FAQs

Here are some of the most common questions we get.

What AI regulations apply to companies operating in the UAE and GCC?
The regulatory landscape is evolving rapidly. The UAE has published national AI principles and sector-specific guidance, while DIFC and ADGM are developing frameworks forregulated entities.
Saudi Arabia's SDAIA has issued AI ethics principles and data governance standards under the NDMO. Bahrain's Central Bank has integrated technology risk expectations into its rulebook, and Qatar is advancing national data and AI strategies.
Companies in regulated sectors also align with international standards such as ISO/IEC 42001 and the NIST AI Risk Management Framework.
What is the difference between AI governance and AI compliance?
AI compliance isabout meeting specific regulatory requirements: filing documentation, passing audits, satisfying supervisory expectations.
AI governance is broader. It is the system of policies, roles, processes, and controls that determines how an organisation develops, deploys, and monitors AI responsibly.
Compliance is one output of good governance, but governance also covers risk appetite, accountability structures, model lifecycle management, and ethical review.
How do banks in theGCC manage AI risk?
GCC banks typically manage AI risk through existing operational risk frameworks, model risk management practices, and emerging AI-specific policies. Most large banks maintain model validation teams that review AI and ML models before deployment, assessing performance, bias, explainability, and data quality.
Central bank expectations from the CBUAE, SAMA, and CBB increasingly require institutions to demonstrate oversight of algorithmic decision-making, particularly in creditscoring and fraud detection.
The key challenge is moving from ad hoc model reviews to an enterprise-wide AI governance operating model that covers thefull lifecycle and coordinates across the three lines of defence
What is the NIST AI Risk Management Framework and how does it apply in the Middle East?
The NIST AI Risk Management Framework (AI RMF) is a voluntary, risk-based framework organised around four core functions: Govern, Map, Measure, and Manage. Although it is a USA publication, it has become a globally referenced standard.
In the Middle East, it is particularly relevant for multinational companies operating across jurisdictions, for GCC organisations that supply AI-enabled services to USA clients, and as a credible benchmark where local regulations have not yet prescribed specific methodologies.
Many GCC regulators reference international standards without mandating a single framework, making NIST AI RMF a strong foundation that can be adapted to local requirements
What does ISO/IEC42001 require for AI management systems?"
ISO/IEC 42001 is the first international management system standard specifically for artificial intelligence. It requires organisations to establish, implement, and continually improve an AI management system. Key requirements include definingan AI policy and objectives, conducting systematic risk assessments implementing controls across the AI lifecycle, and establishing processes for monitoring and continual improvement.
It follows the Annex SL structure used in ISO 9001 and ISO 27001, making it integrable with existing management systems. For GCC organisations, ISO 42001 certification is increasingly a market differentiator in financial services, government contracting, and healthcare.
How should organisations assess their AI maturity?
An effective AI governance maturity assessment examines capabilities across several dimensions: strategy and leadership commitment, governance and accountability structures, risk management processes, data management practices, technical infrastructure, talent, and stakeholder engagement.
Each dimension is evaluated against defined levels ranging from ad hoc through to optimised. The most useful assessments are honest about gaps and prioritise findings by risk exposure and strategic impact rather than trying to advance every dimension at once.
For GCC organisations at early stages of formalising AI governance, a diagnostic assessment provides a defensible baseline, identifies priority gaps, andcreates a roadmap that delivers both regulatory readiness and operational value.
What is an AI governance operating model?
An AI governance operating model defines how an organisation makes decisions about AI at scale. It specifies who is accountable for AI-related risks, what governance bodies exist and how they interact, which processes govern the AI lifecycle from ideation to retirement, and what tools and reporting mechanisms support oversight.
A well-designed operating model typically includes board-level oversight, clear first-, second-, and third-line responsibilities, a risk-tiered approval process, and defined escalation paths. The operating model turns governance policy into operational reality.
Without it, organisationstend to either over-govern low-risk applications and slow innovation, or under-govern high-risk ones and create exposure that only surfaces when something goes wrong