The operating model
A system, not a service catalogue.
Every MindXO service maps to a specific function within the enterprise AI GRC operating model. Together they form a complete system for governing, measuring, and assuring AI risk.
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.
I: AI Governance Architecture
Define direction, accountability, and guardrails. Build the governance infrastructure that AI risk measurement runs on.
AI Risk Appetite Definition (Flagship)
We help your organization define what it wants to achieve with AI and how much risk is acceptable to get there. The engagement assesses current governance readiness, aligns leadership on AI objectives, and produces a formal risk appetite statement - the foundation that every subsequent risk management decision references.
Key outcomes
- Formal AI risk appetite statement aligned to enterprise risk appetite framework
- Governance readiness profile with scoring across key dimensions
- Priority roadmap sequenced by risk, regulatory exposure, and business impact
AI Governance & Risk Management Framework
A tailored framework defining how AI is governed and how risk is managed across the full lifecycle. We design accountability structures, risk taxonomies, operating models, decision rights, escalation paths, and three-lines-of-defense integration - aligned to NIST AI RMF, ISO 42001, and your regulatory environment.
Key outcomes
- Governance architecture with clear ownership and decision rights
- Enterprise AI risk taxonomy mapped to your control environment
- Operating model defining roles, workflows, and escalation across business, technology, risk, and compliance
Responsible AI Policy Suite
A practical, enforceable policy foundation for responsible AI adoption. We define policies that govern how AI systems are approved, developed, used, and overseen - embedding risk tiering, accountability, and compliance obligations directly into operational language rather than high-level ethics statements.
Key outcomes
- Operational Responsible AI policy aligned to ISO 42001, NIST AI RMF, and applicable regulation
- AI-specific acceptable use, development, and procurement policies
- Policy integration guide for embedding into existing corporate policy architecture
II: Quantitative Risk Measurement & Operations
Identify, measure, and treat AI risk using the structured risk management workflow established in high-risk industries - extended for organizations deploying AI systems.
MindXO Evaluation Methodology (Cross-cutting methodology)
Every service in this pillar is powered by a structured evaluation methodology covering nine risk categories mapped simultaneously to NIST AI RMF, NIST AI 800-2, ISO 42001, EU AI Act, OWASP LLM Top 10, and MITRE ATLAS. The methodology follows the risk management workflow - risk identification, risk analysis and evaluation, risk treatment - and produces decision-grade findings.
Nine risk categories: Task performance · Faithfulness · Robustness · Safety · Security · Fairness · Privacy · Oversight · Agentic behavior.
AI Risk Identification & Modeling
We identify and model the risks specific to your AI deployment archetypes - RAG assistants, customer-facing chatbots, agentic workflows, code assistants, embedded SaaS AI. For each deployment, we map risk scenarios step by step: how could this system cause harm, through what pathway, and with what probability and severity.
Key outcomes
- Deployment-specific risk model with scenario pathways and severity classification
- Red-team findings mapped to OWASP LLM and MITRE ATLAS with reproduction steps
- Risk register foundation with identified risks, owners, and initial severity ratings
AI Risk Assessment & Measurement
We operationalize your organization's AI risk tolerance into measurable indicators. For each AI system, we define deployment-specific Key Risk Indicators with thresholds and corresponding Key Control Indicator targets. We then run the full quantitative evaluation - testing the end-to-end system as configured for production.
Key outcomes
- KRI library with defined metrics, thresholds, and KCI targets per deployment and risk tier
- Quantitative evaluation results across nine risk categories with uncertainty quantification
- Risk-tier deployment recommendation per system
AI Risk Treatment & Monitoring
We verify that deployed mitigations - guardrails, output filters, scope enforcement, human-in-the-loop controls - actually meet the required KCI thresholds. We then design the continuous monitoring program: which KRIs and KCIs to track, at what frequency, with what tooling, and what governance responses to trigger on threshold breach.
Key outcomes
- Mitigation effectiveness report verifying KCI thresholds are met
- Continuous monitoring design: KRI/KCI dashboards, frequency, tooling integration
- Governance escalation protocol: breach → response → re-evaluation trigger matrix
III: Continuous Assurance
Maintain visibility, control, and evidence as AI systems scale and change. Turn point-in-time assessments into an ongoing operating rhythm.
AI Systems Inventory & Classification
A centralized, auditable register of all AI systems across your organization - models, deployment configurations, data sources, APIs, owners, and risk classifications. A structured, maintained asset that ties each system to its governance obligations and serves as the foundation for risk tiering and monitoring.
Key outcomes
- Complete AI systems register with governance attributes per system
- Risk classification aligned to your tier framework
- Maintenance process with triggers for new and changed systems
Runtime Risk Monitoring
Continuous monitoring of deployed AI systems against the KRI thresholds and KCI targets designed in Pillar II. We track risk indicators over time, detect threshold breaches, and trigger the governance responses defined in the escalation protocol - producing the ongoing evidence trail that audit, compliance, and board reporting require.
Key outcomes
- Live risk dashboard tracking KRIs and KCIs across your AI portfolio
- Threshold breach alerting with governance escalation paths
- Continuous evidence generation for regulatory and audit requirements
Continuous Assurance Program (Flagship)
A retainer engagement combining inventory maintenance, ongoing risk monitoring, periodic posture reassessment, and evidence production into a single operating rhythm. Designed for organizations that need AI governance to be a sustained capability - with regular reassessment cycles as systems, models, and regulations evolve.
Key outcomes
- Defined assurance cadence with scheduled reassessments and evidence reviews
- Audit-ready evidence pack updated continuously
- Quarterly risk posture report for board and executive committee
Three illustrative engagements
The cases below are illustrative. They show how our methodology applies to situations we consider typical in regulated organizations; they do not describe specific clients or engagements.
Illustrative case · Retail bank · GCC · Pillar I, Governance Architecture
From 40 ungoverned AI use cases to a board-approved risk appetite
A mid-size retail bank has accumulated 40+ AI use cases across fraud, credit decisioning, and customer service, built by different teams with no shared inventory and no formal risk appetite. The board is asking who approves what, and the regulator has started asking questions about algorithmic decision oversight.
The engagement: governance readiness assessment and leadership alignment; a formal AI risk appetite statement anchored to the enterprise risk appetite framework; governance architecture with ownership, decision rights, and escalation paths aligned to NIST AI RMF and ISO 42001. Indicative duration: 8–10 weeks.
You walk away with: a board-approved AI risk appetite statement, a governance framework with clear accountability, and a priority roadmap sequenced by risk and regulatory exposure.
Illustrative case · Insurer · UAE · Pillar II, Risk Measurement & Operations
Evidence, not assurances, before a GenAI deployment goes live
An insurer is preparing to deploy a customer-facing GenAI assistant and an underwriting decision agent. Internal audit asks for evidence that the risks were measured rather than described. No AI-specific KRIs exist, and the approval committee has nothing quantitative to decide on.
The engagement: deployment-specific risk modeling for each archetype including structured red-team assessment against OWASP LLM Top 10 and MITRE ATLAS; risk tolerance operationalized into measurable KRIs with thresholds and KCI targets; full evaluation across nine risk categories consolidated into a signed Risk Posture Assessment dossier. Indicative duration: 6–8 weeks per system family.
You walk away with: a signed evaluation dossier with residual risk statements, KRI/KCI thresholds ready for continuous monitoring, and a risk-tier deployment recommendation the committee can act on.
Illustrative case · Government entity · GCC · Pillar III, Continuous Assurance
A periodic assurance review that re-tests AI systems
A public institution runs dozens of production AI systems, each reviewed once at deployment. Models have since been updated, vendors have shipped new versions, and the original evaluations no longer describe what is actually running. The audit committee wants recurring proof that controls still hold, grounded in fresh testing rather than document review.
The engagement: design of the periodic assurance review with scope, cadence by risk tier, and re-review triggers such as model updates, vendor changes, and threshold breaches; per cycle, quantitative re-evaluation across the nine risk categories and structured adversarial re-testing against OWASP LLM Top 10 and MITRE ATLAS, verifying mitigations still meet KCI targets; findings consolidated into residual risk statements, an audit-ready evidence pack, and a posture report to the governance committee. Indicative: 5–6 weeks to design, then per-cycle execution.
You walk away with: a documented assurance review program with cadence and risk-tier triggers, refreshed evaluation and test results each cycle scored against KRI/KCI thresholds, and audit-ready evidence packs with recurring posture reporting.