Independent AI Governance & Risk Management
Intelligence, governed.
Dedicated to making organizational AI accountable, governed, and safe.
Applied research, governance design, and advisory services for regulated organizations.
See our work
Explore our services
- NIST AI RMF
- ISO/IEC 42001
- OECD AI Principles
- OWASP AI Exchange
- EU AI Act
- SaferAI Frontier AI RMF
- IEEE AISC
The thesis
AI governance is moving from policy to proof.
Frameworks such as ISO 42001, NIST AI RMF, the OECD AI Principles and the EU AI Act define expectations for responsible AI.
But deployers still need an operating layer: what to measure, which thresholds to set, how to monitor systems in production, and how to prove that controls are working.
MindXO turns governance into an operating system: KRIs, metrics, tests, escalation workflows and board-ready evidence for deployed AI systems.
Frameworks provide
- Principles and categories
- Accountability structures
- Risk-management process
- Policy requirements
Deployers still need
- KRIs and thresholds
- Automated tests
- Runtime monitoring
- Board-ready evidence
Our risk management methodology is based on a measurement chain that carries every AI risk from a live runtime signal to the evidence a risk committee can act on.
The measurement chain
- AI Risk
- KRI
- Metric
- Automated Test
- Observability
- Threshold
- Enforcement
- Board Evidence
Our focus
What we do.
We research how AI systems create risk. We shape the standards that govern them. We help regulated enterprises and government entities implement both.
01 · Intellectual Foundation
Research
Applied research on emerging AI risks, the enterprise KRI taxonomy, and translating frontier AI safety into deployment-grade governance for regulated organizations.
Flagship: Enterprise AI KRI Taxonomy
- Frontier-to-enterprise risk translation
- Quarterly insight reports
- ABO/ISCIL inter-system risk
Read the research
02 · Ecosystem Shaping
Policy & Standards
We work across standards, open-source governance initiatives, and public-sector policy programs to help translate AI governance principles into implementable national and institutional frameworks.
Flagship: National AI Governance Playbook
- Standards and open-source contributions
- National AI governance program design
- Public consultations on AI regulation
Explore policy & standards
03 · Application in Organizations
Advisory
Three service pillars (Governance Architecture, Quantitative Risk Measurement, and Continuous Assurance) operationalize both research and policy in client environments.
Flagship: MindXO AI GRC Framework
- Pillar I: Governance architecture
- Pillar II: Risk measurement
- Pillar III: Continuous assurance
See all services
Open resources
Our open-source contribution.
We treat AI safety as shared infrastructure. The tools we build for our own work, we release openly and free to use, as a contribution to everyone working to make AI governable.
- Deployer AI Risk Register (open access): 82 deployment risks and 61 security sub-risks, consolidated from the MIT AI Risk Repository, ISO/IEC 23894 and 42001, MITRE ATLAS, and the EU AI Act. Open the register · GitHub
- AI Governance Framework Navigator (open access): 51 AI governance, security, safety, and compliance frameworks mapped across the four-layer GRC operating model, filterable by practitioner role. Explore the navigator · GitHub
- AI Safety Organizations Atlas (open access): 50+ organizations shaping AI governance and safety worldwide. Browse the atlas
- Enterprise AI KRI Taxonomy (forthcoming, Q3 2026): a reference taxonomy that translates AI deployment risks into measurable KRIs, thresholds, data sources, and automated test signals. 75+ KRIs across 5 measurement domains. Preview the abstract
Our advisory services
Govern, measure, and assure AI risk.
MindXO helps regulated organizations turn AI governance requirements into an operating model: decision rights, inventories, risk identification, controls, monitoring, and audit-ready evidence.
Our services are organized across three pillars: governance architecture, quantitative risk measurement, and continuous assurance.
ORG: Objectives · Risk tolerance
What do we want to achieve with AI? How much risk is acceptable?
- AI risk appetite definition (Pillar I)
Outcome: AI is a risk-managed enabler for objectives.
GOV
AI systems inventory
What AI, where?
- Systems inventory (Pillar III)
Oversight & decision
Who approves what?
- Governance & risk management framework (Pillar I)
Accountability
Who owns what?
- Responsible AI policy suite (Pillar I)
Outcome: AI systems managed within risk tolerance.
RISK
Risk identification
What are the risks?
- Risk identification & modeling (Pillar II)
Trustworthiness controls
How to measure?
- Risk assessment & measurement (Pillar II)
Continuous monitoring
Within tolerance?
- Risk treatment & monitoring (Pillar II)
- Runtime risk monitoring (Pillar III)
Outcome: Residual risks measured and monitored.
COMP
External requirements
What must we comply with?
- Risk assessment & measurement (Pillar II)
Internal requirements
Internal instruments?
- Responsible AI policy suite (Pillar I)
Compliance evidence
Documented, when, by whom?
- Continuous assurance program (Pillar III)
Outcome: Compliance documented with audit-ready evidence.
AI Risk Posture Assessment: the Pillar II deliverable. A signed dossier spanning identification, measurement, and treatment verification with multi-framework evidence.
We help organizations operationalize risk management for AI through three service pillars. Each owns a stage of the work, from defining the rules to proving they hold in production.
Pillar I: Governance Architecture
Define the rules. We design governance frameworks, policies, and accountability structures that establish how AI is approved, deployed, and overseen in regulated environments.
Flagship: AI Risk Appetite Definition
- AI Governance & Risk Management Framework
- Responsible AI Policy Suite
Pillar II: Risk Measurement & Operations
Quantify the risk. We identify, assess, and monitor AI risk using structured measurement methodologies, so decisions are grounded in evidence, not assumptions.
Flagship (measurement-native): AI Risk Posture Assessment
- AI Risk Identification & Modeling
- AI Risk Assessment & Measurement
- AI Risk Treatment & Monitoring
Pillar III: Continuous Assurance
Prove it holds. We maintain a living inventory of AI systems, monitor residual risks and controls effectiveness in production, and generate the audit-ready evidence regulators expect.
Flagship: Continuous Assurance Program
- AI Systems Inventory & Classification
- Runtime Risk Monitoring
Who are our clients
We build for the teams accountable for AI risk.
We work with risk, governance, security, and data leaders in regulated organizations navigating AI adoption. Each faces a different dimension of the same challenge: making AI governable, measurable, and auditable within their function.
CRO: Chief Risk Officer
Model risk integration. Board-reportable AI exposure. Three-lines-of-defense compatibility, without rebuilding the program.
Risk integration
CGRCO: Chief Governance, Risk & Compliance
ISO 42001, EU AI Act, GCC regulators (CBUAE, SAMA, CBB, DFSA, FSRA). Audit-ready evidence on a cadence regulators recognize.
Audit readiness
CISO: Chief Information Security Officer
Shadow AI, agentic runtime, prompt injection, third-party assurance. MITRE ATLAS and OWASP AI Exchange alignment.
Runtime security
CDO: Chief Data Officer
Adoption velocity with guardrails. Time-to-value. Measurement that accelerates rollout instead of auditing it after the fact.
Adoption with guardrails
Aligned with global standards
The standards our work is built on.
The frameworks that set the bar for responsible AI governance, security, and safety, and the initiatives we actively contribute to.
Referenced in our methods and deliverables.
- NIST AI Risk Management Framework (NIST.AI-100), United States · 2023, voluntary
- ISO/IEC 42001, International · 2023 management system standard
- OECD AI Principles, Adopted 2019 · revised 2024
- EU AI Act (EU/2024/1689), European Union · in force 2024, phased 2025–2027
- SaferAI Frontier AI Risk Management Framework, arXiv:2502.06656 · referenced in our frontier risk work
- OWASP AI Exchange · MITRE ATLAS, Threat patterns for the security category
Contributions and participation
About MindXO
Dubai, UAE · Paris, France
MindXO is an independent AI governance and risk management practice based in Dubai, UAE and Paris, France, working across the GCC, the EU, and internationally.
We research emerging AI risks and help organizations design governance frameworks, manage risk quantitatively, and scale AI responsibly. Our work spans applied research on inter-system risk, governance framework design aligned with NIST AI RMF and ISO 42001, and advisory services for regulated enterprises and public institutions.
We serve organizations across the GCC, the EU, and internationally, with particular depth in financial services, insurance, public sector, and telecommunications.
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