Advisory services

From AI governance frameworks to measurable risk management.

We help regulated organizations define governance structures, identify and measure AI system risks quantitatively, and build continuous assurance programs that produce board-ready evidence.

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The AI risk measurement chain, from runtime trace to risk committee

How AI risk becomes measurable, traceable, and auditable.

We identify, quantify, and monitor AI risk within your organization's specific context. Eight stages that connect what your systems actually do to the evidence your board and regulators need.

01: AI Risk: named, scoped, owned
02: KRI: indicator from the taxonomy
03: Metric: operational definition
04: Automated Test: evals, probes, traces
05: Observability: OTEL · runtime stream
06: Threshold: breach detection
07: Enforcement: policy · runtime guardrail
08: Evidence: board · auditor · regulator

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

AI Risk Posture Assessment

Where do your AI systems actually sit?

A live sample from a posture assessment. Twelve enterprise AI systems plotted against the taxonomy. Click any system to see the dominant KRI, the breach signal, and the recommended governance response.

Y axis, Impact: Minor, Moderate, Significant, Major, Severe
X axis, Likelihood: Rare, Unlikely, Possible, Likely, Almost certain
Tier scoring (x + y): ≤3 low · 3–4 watch · 5–6 moderate · 7–8 high · ≥9 critical
KRI categories: SEC (Security), MDL (Model), OPS (Operational), CMP (Compliance), SYS (Systemic)

S-01: Customer support copilot, SEC · KRI-SEC-014 (likelihood: Likely, impact: Significant)
S-02: Underwriting decision agent, MDL · KRI-MDL-007 (likelihood: Possible, impact: Severe)
S-03: AML triage assistant, CMP · KRI-CMP-009 (likelihood: Likely, impact: Major)
S-04: HR resume screener, MDL · KRI-MDL-011 (likelihood: Unlikely, impact: Major)
S-05: Internal RAG · policy search, OPS · KRI-OPS-002 (likelihood: Possible, impact: Moderate)
S-06: Fraud-detection cascade, SYS · KRI-SYS-003 (likelihood: Almost certain, impact: Severe)
S-07: Marketing copy generator, OPS · KRI-OPS-005 (likelihood: Unlikely, impact: Minor)
S-08: Code-completion · prod tooling, SEC · KRI-SEC-021 (likelihood: Possible, impact: Significant)
S-09: Document extraction · KYC, CMP · KRI-CMP-014 (likelihood: Likely, impact: Severe)
S-10: Treasury research assistant, MDL · KRI-MDL-018 (likelihood: Unlikely, impact: Significant)
S-11: Customer churn predictor, OPS · KRI-OPS-009 (likelihood: Possible, impact: Moderate)
S-12: Vendor due-diligence agent, SYS · KRI-SYS-007 (likelihood: Almost certain, impact: Major)

Recommended response

Instrument runtime traces against the dominant KRI
Set tier-aligned threshold; route breach to second-line
Add to Continuous Assurance retainer scope

Request a Posture Assessment

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

Continuous Assurance

Risk posture, monitored in real time.

A live posture snapshot from a sample continuous assurance program. Each KRI streams against its threshold, with trend signals and breach alerts surfaced as they happen, feeding directly into the audit-ready evidence pack regulators expect.

KRI-SEC-014: Prompt-injection success rate, Critical · value 3.8% · target ≤ 1.0% · trend up (+0.6)
KRI-MDL-007: Calibration error · protected slices, High · value 0.18 · target ≤ 0.10 · trend up (+0.02)
KRI-OPS-002: Pilot-to-production ratio, Watch · value 1:14 · target 1:6 · trend flat (0)
KRI-CMP-009: ISO 42001 control coverage, OK · value 92% · target ≥ 90% · trend up (+2pp)

Streaming · OTEL traces · live signals/min

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

Ready to make AI governance measurable?

Whether you are defining governance, evaluating a deployment, or building continuous assurance.

We will help you take the right next step. Start with a single use case or plan an enterprise-wide program.

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