Custom governance architectures that satisfy regulators, enable innovation, and integrate with your existing quality management systems. Designed for your specific regulatory environment, organizational culture, and AI maturity level.
The Challenge
The gap between "we need AI governance" and "we have AI governance" is where most organizations get stuck. The challenge is not awareness. It is architecture. Boards have read the headlines about the EU AI Act. Leadership teams have seen the ISO 42001 standard referenced in procurement questionnaires. AI teams have been asked to "do something about governance." But translating that mandate into an actual governance framework — one that works for your specific organization, regulatory context, and AI maturity level — requires a design discipline that most organizations simply do not have in-house.
Downloading a generic AI governance template and bolting it onto your organization. It won't reflect your regulatory context, risk profile, or operational reality — and auditors will see through it immediately.
Building AI governance that operates independently from your existing quality, compliance, and risk management systems. The result is duplicate processes, conflicting requirements, and governance fatigue.
Creating governance processes so burdensome that AI teams route around them. If every AI experiment requires a 40-page impact assessment, governance becomes the enemy of innovation rather than its enabler.
Publishing an AI ethics statement and calling it governance. It looks good on the website but fails the first regulatory audit, customer due diligence questionnaire, or incident investigation.
The solution: custom governance architecture designed by someone who understands both management systems methodology and AI regulatory requirements — built for your specific regulatory environment, organizational culture, and AI maturity level.
Our Methodology
Every governance framework we design follows a rigorous five-pillar methodology. Each pillar addresses a critical dimension of AI governance — from organizational structure and policy architecture through process design, technical controls, and multi-framework compliance mapping. The result is a governance system that is comprehensive, auditable, and operationally practical.
The organizational architecture that defines who has authority, responsibility, and accountability for AI governance decisions. Without the right structure, policies exist on paper but have no enforcement mechanism.
We design the charter, membership criteria, voting procedures, meeting cadence, and decision authority for your AI governance committee. The composition balances technical AI expertise with legal, compliance, risk management, and business domain representation. We define quorum requirements, escalation thresholds, and the committee's relationship to the board and executive leadership. The charter establishes the committee as the authoritative decision-making body for AI governance — not an advisory group that can be overruled by individual business units.
Clear role definitions for every position in the AI governance structure: Chief AI Officer (or fractional CAIO), AI Risk Officer, Model Owners, Data Stewards, AI Ethics Leads, and domain-specific roles. Each role gets a RACI matrix mapping responsibilities to specific governance processes. We define the competency requirements for each role, the training pathways to build internal capability, and the performance metrics that demonstrate governance effectiveness. This prevents the common failure mode where "everyone is responsible" — which means nobody is.
We map the information flow from AI development teams through governance layers to executive leadership and the board. This includes regular reporting cadences (monthly AI risk dashboards, quarterly governance reviews, annual board-level AI strategy updates) and escalation paths for incidents, threshold breaches, and high-risk deployment decisions. The reporting structure ensures that the right information reaches the right decision-makers at the right time — without creating information overload at the top or bottlenecks in the middle.
AI governance does not exist in a vacuum. Your organization already has quality management committees, compliance teams, risk management functions, and data governance programs. We design the AI governance structure to integrate with these existing bodies through cross-membership, shared reporting, aligned review cycles, and coordinated audit programs. This prevents governance fragmentation and leverages institutional knowledge that already exists in your quality and compliance teams.
The documented rules and expectations that guide AI development, deployment, and management across the organization. Policies translate governance principles into enforceable requirements that auditors can verify and employees can follow.
Defines what AI tools and applications employees may use, how they may use them, and what organizational data may be processed by AI systems. Covers both internally developed AI and third-party AI services including generative AI tools. Addresses intellectual property protection, confidentiality obligations, and prohibited use cases specific to your industry and regulatory environment.
Establishes the methodology for identifying, assessing, mitigating, and monitoring risks associated with AI systems. Defines risk categories (safety, fairness, privacy, security, reliability), risk scoring methodology, risk appetite thresholds, and required controls for each risk tier. Aligned with ISO 42001 risk management requirements and NIST AI RMF Map and Measure functions.
Governs the full lifecycle from concept through development, validation, deployment, monitoring, and retirement. Defines stage-gate requirements, documentation standards, validation criteria, deployment approval processes, and retirement procedures. Ensures every AI system has a defined owner, a documented purpose, and a clear lifecycle management plan.
Establishes governance requirements for AI systems acquired from third-party vendors. Covers due diligence criteria, contractual requirements for transparency and auditability, ongoing vendor monitoring, and incident notification obligations. Addresses the supply chain governance requirements in both ISO 42001 and the EU AI Act for deployers who rely on third-party AI components.
Defines validation requirements before deployment and continuous monitoring requirements after deployment. Covers performance metrics, fairness metrics, drift detection thresholds, revalidation triggers, and the process for responding to performance degradation. Particularly critical for organizations subject to FDA AI/ML guidance or financial services model risk management requirements.
Addresses the specific data governance requirements for AI systems: training data provenance, data quality standards, bias assessment in training datasets, consent and privacy requirements, data retention for model reproducibility, and synthetic data governance. Integrates with existing data governance frameworks and extends them to address AI-specific data lifecycle requirements.
The operational workflows that turn policies into daily practice. Policies say what must be done. Processes define exactly how, by whom, and when. Without well-designed processes, policies become aspirational documents that nobody follows.
A stage-gated process covering the complete AI system lifecycle: concept and use case definition, data acquisition and preparation, model development and training, validation and testing, deployment approval, production monitoring, periodic review, and retirement. Each stage has defined entry criteria, required activities, documentation requirements, and exit criteria. The process scales with risk — low-risk systems move through streamlined gates while high-risk systems undergo rigorous review at each stage.
A structured methodology for assessing AI-specific risks including safety, fairness, privacy, security, reliability, transparency, and accountability. Defines the assessment tools, scoring rubrics, risk categorization thresholds, and required controls for each risk level. Connects directly to our AI Risk Assessment service and can be performed as a standalone engagement or integrated into the broader governance framework.
Governs modifications to deployed AI systems including model retraining, feature changes, data source changes, and performance threshold adjustments. Defines change categories (routine, significant, major), impact assessment requirements, testing requirements, approval authorities, and rollback procedures. Addresses the EU AI Act's requirements for substantial modification documentation and the FDA's predetermined change control plan concept for AI/ML-based medical devices.
A defined process for detecting, reporting, investigating, and remediating AI system failures and adverse outcomes. Covers incident classification severity levels, immediate containment procedures, root cause analysis methodology, corrective and preventive action (CAPA) requirements, regulatory notification obligations, and lessons-learned integration. Designed to work within your existing incident management framework rather than creating a parallel system.
Defines the continuous monitoring requirements for deployed AI systems: performance metrics to track, monitoring frequency, drift detection methodologies, alert thresholds, and response procedures when performance degrades. Covers both technical monitoring (model accuracy, data drift, concept drift) and outcome monitoring (fairness metrics, impact assessments, user feedback). Establishes the periodic review cadence for all deployed models.
The technical infrastructure and tooling that enables governance at scale. Manual governance does not scale. As your AI portfolio grows, you need automated controls, registries, audit trails, and monitoring systems that enforce governance requirements without creating bottlenecks.
Specifications for a centralized model registry that tracks every AI model in your organization: model purpose, owner, training data lineage, version history, validation results, deployment status, and risk classification. The registry serves as the single source of truth for your AI inventory and provides the foundation for regulatory reporting, audit trails, and lifecycle management. We define the data model, metadata requirements, and integration points with your existing IT asset management systems.
Defines the logging, documentation, and explainability requirements for AI system decisions. Covers what must be logged (inputs, outputs, model version, confidence scores, override actions), retention periods, access controls for audit data, and explainability requirements calibrated to the risk level and regulatory context. Addresses the EU AI Act's transparency requirements, ISO 42001's documented information requirements, and sector-specific auditability standards.
Specifications for the testing and validation infrastructure needed to assess AI systems before deployment and continuously after deployment. Covers unit testing for model components, integration testing for AI-enabled workflows, fairness testing across protected categories, adversarial testing for robustness, and regression testing for model updates. Defines acceptance criteria, test data requirements, and the relationship between testing results and deployment approval decisions.
Defines the technical mechanisms for human oversight of AI systems, calibrated to risk level. Covers human-in-the-loop requirements (human approval before AI action), human-on-the-loop requirements (human monitoring with override capability), and human-in-command requirements (human ability to intervene and override at any time). Specifies the interface requirements, alert mechanisms, and override procedures that ensure humans can exercise meaningful oversight rather than rubber-stamping AI outputs.
The regulatory alignment layer that maps every element of your governance framework to applicable regulatory requirements. This ensures your framework satisfies multiple frameworks simultaneously and provides auditors with clear evidence of compliance across every applicable standard and regulation.
Every governance element mapped to the specific ISO 42001 clause it satisfies: context of the organization (Clause 4), leadership (Clause 5), planning (Clause 6), support (Clause 7), operation (Clause 8), performance evaluation (Clause 9), and improvement (Clause 10). Plus Annex A controls and Annex B implementation guidance. This mapping ensures your framework is certification-ready and provides a clear compliance narrative for third-party auditors.
For organizations subject to the EU AI Act, we map governance elements to specific articles and requirements: risk classification (Title III), high-risk AI system requirements (Chapter 2), conformity assessment procedures (Chapter 3), transparency obligations (Title IV), and provider/deployer obligations. This mapping provides a compliance roadmap that directly connects your governance activities to specific regulatory requirements.
Governance elements mapped to the four NIST AI RMF core functions: Govern (organizational practices for AI risk management), Map (understanding context and risks), Measure (analyzing and assessing AI risks), and Manage (prioritizing and acting on AI risks). Includes sub-category-level mapping to provide granular compliance evidence for organizations using NIST AI RMF as their primary governance reference.
Beyond horizontal AI frameworks, we map governance elements to sector-specific requirements: FDA AI/ML guidance and GxP requirements for healthcare and pharma, OCC and Federal Reserve model risk management guidance (SR 11-7) for financial services, DoD AI ethics principles for defense, and other relevant sector regulations. This ensures your framework addresses the full regulatory landscape rather than just the AI-specific layer.
Framework Intelligence
No single framework covers everything. The best governance programs draw from multiple frameworks simultaneously, using each for its unique strengths. Regulated AI Consulting designs governance architectures that satisfy multiple frameworks at once — reducing compliance overhead rather than multiplying it.
The international standard for AI management systems, published in December 2023. ISO 42001 follows the Annex SL high-level structure shared by ISO 9001, ISO 27001, and other management system standards — making it the natural governance framework for organizations that already operate under ISO management systems. Its management-systems approach (Plan-Do-Check-Act) provides a mature methodology for continuous governance improvement. Crucially, it is certifiable: organizations can obtain third-party certification to demonstrate AI governance maturity to customers, regulators, and partners.
ISO 42001 implementation servicesThe world's first comprehensive AI regulation, with a risk-based classification approach. Unlike voluntary frameworks, the EU AI Act is law — with enforcement deadlines, conformity assessment requirements, and substantial penalties for non-compliance. Organizations that develop, deploy, or distribute AI systems in the EU must classify their systems by risk tier and implement corresponding governance requirements. High-risk AI system requirements take effect August 2, 2026, making preparation urgent for affected organizations.
EU AI Act compliance servicesThe U.S. federal framework organized around four core functions: Govern (organizational context and culture), Map (understanding risks in context), Measure (assessing and analyzing risks), and Manage (prioritizing and acting on risks). While voluntary, NIST AI RMF is increasingly referenced in federal procurement requirements, regulatory guidance, and industry standards. Its flexible, risk-based approach makes it particularly valuable as an organizational backbone for AI risk management practices, and it pairs well with ISO 42001 for organizations seeking both U.S. and international alignment.
A family of IEEE standards addressing ethical considerations in system design, including IEEE 7000 (Model Process for Addressing Ethical Concerns During System Design), IEEE 7001 (Transparency of Autonomous Systems), IEEE 7002 (Data Privacy), and IEEE 7010 (Well-Being Metrics). While less widely adopted than ISO 42001 or NIST AI RMF, the IEEE 7000 series provides valuable methodological guidance for organizations that want to embed ethical considerations into their AI development processes from the design stage rather than bolting on ethics reviews after the fact.
Regulated AI Consulting does not ask you to pick one framework. We design governance architectures that satisfy multiple frameworks simultaneously. A single well-designed policy can address ISO 42001 Clause 6.1, NIST AI RMF Govern 1.1, and EU AI Act Article 9 — rather than requiring three separate policies for three separate compliance obligations. This multi-framework mapping approach reduces compliance overhead, eliminates duplicate processes, and ensures your governance program is resilient to regulatory evolution.
Key Differentiator
Most AI governance consultants build frameworks in isolation from existing quality and compliance systems. This creates parallel governance structures that multiply overhead, confuse employees, and fragment your compliance posture. Our approach is fundamentally different.
If your organization already operates under ISO 9001, ISO 13485, ISO 27001, or similar management system standards, you have already built the governance infrastructure that AI governance requires: document control, management review, internal audit, corrective action, training management, and continuous improvement. AI governance is not a separate discipline — it is an extension of quality management applied to a new technology domain.
This is precisely why Jared Clark's quality systems background — certified by ASQ as a Manager of Quality and Organizational Excellence — produces governance frameworks that are operationally superior to those designed by cybersecurity or data science practitioners. The quality management methodology (Plan-Do-Check-Act, process-based approach, risk-based thinking) maps perfectly to what AI governance actually requires.
The PDCA cycle that drives continuous improvement in quality management systems is the same cycle that drives AI governance maturity. We design AI governance processes that leverage your existing PDCA infrastructure rather than creating a parallel improvement methodology.
Your existing document control system manages AI governance policies, procedures, and records. Your existing records management practices handle AI audit trails, validation reports, and model documentation. No separate document management infrastructure required.
AI governance audits integrate into your existing internal audit program. AI governance metrics feed into your existing management review process. This gives leadership a unified view of organizational governance maturity rather than fragmented reports from siloed compliance functions.
AI incidents feed into your existing corrective and preventive action system. AI governance improvement initiatives are tracked through your existing continuous improvement program. This ensures that AI governance lessons learned propagate across the entire management system, not just within an AI silo.
What You Receive
Every governance framework engagement produces a comprehensive set of deliverables designed to be immediately implementable. These are not abstract strategy decks — they are working governance documents, process maps, and compliance tools your organization can deploy from day one.
Complete AI governance committee charter defining scope, authority, membership composition, decision-making procedures, meeting cadence, and reporting requirements. Includes role descriptions for all governance positions with RACI matrices mapping responsibilities to governance processes.
Five to seven governance policies covering the full AI lifecycle: acceptable use, risk management, development lifecycle, vendor procurement, model validation and monitoring, and data governance for AI. Each policy is drafted in your organization's document format and aligned with your existing policy hierarchy.
Visual workflow diagrams for every key governance process: AI system lifecycle management, risk assessment, change management, incident management, and performance monitoring. Each process map includes swimlanes showing role responsibilities, decision points, documentation requirements, and integration points with existing organizational processes.
A comprehensive requirements mapping matrix that traces every governance element to the specific clauses, articles, and requirements of each applicable framework (ISO 42001, EU AI Act, NIST AI RMF, sector-specific regulations). This matrix serves as your audit-ready compliance evidence and framework gap tracker.
A phased rollout plan that sequences governance implementation based on risk priority, regulatory deadlines, and organizational readiness. Defines milestones, resource requirements, dependencies, and success criteria for each phase. Includes quick wins that demonstrate governance value early and build organizational support for the full program.
Role-specific training content for governance committee members, AI development teams, model owners, data stewards, and executive leadership. Covers governance framework overview, role-specific responsibilities, process walkthroughs, and regulatory context. Designed to build internal governance capability so your organization can sustain and evolve the framework independently.
Is This Right for You?
Your organization has been developing and deploying AI for years, but governance has grown organically — ad hoc policies, informal approval processes, tribal knowledge about risk management. You need to formalize what exists, fill the gaps, and create a structured governance program that can withstand regulatory scrutiny and scale with your AI portfolio.
The EU AI Act enforcement deadline is approaching. The FDA is tightening AI/ML guidance for medical devices. Financial regulators are expanding model risk management expectations. You know regulatory scrutiny is coming and need a governance framework that will be defensible when it arrives — not a last-minute compliance exercise that auditors can see through.
You have decided to pursue ISO 42001 certification — or are seriously evaluating it — and need a governance framework that satisfies the standard's requirements. Our framework design is ISO 42001 clause-aligned from the start, ensuring every governance element maps to a specific standard requirement and your certification audit has a clear compliance narrative.
Your organization is moving into AI-enabled products or services in regulated markets — an AI-powered diagnostic device, an algorithmic trading system, an autonomous manufacturing process, or an AI-driven clinical decision support tool. You need governance that satisfies both horizontal AI requirements and sector-specific regulations from day one, not governance bolted on after product development is complete.
FAQ
Start with a free 30-minute consultation. We will discuss your organization's AI landscape, regulatory obligations, existing governance maturity, and what a custom governance framework looks like for your specific situation. No sales pitch — just a candid assessment of where you stand and what comes next.
Or email support@certify.consulting