AI governance consulting that approves good work faster instead of stopping it
Our AI governance consulting builds the inventory, risk tiers, controls and evaluation practice that let you approve AI with confidence, aligned to NIST AI RMF, ISO/IEC 42001 and the EU AI Act, and embedded in how your teams already ship.
Governance, risk and security
Why most AI governance programs stall
Many organizations respond to AI risk with a policy document and a committee. The policy is broad, the committee meets monthly, and every use case, from a meeting summarizer to a credit decision model, goes through the same queue. Teams either wait months or route around the process, and the organization ends up with less control than it had before.
Meanwhile the obligations keep growing. Regulators, auditors and customers expect evidence: what AI systems you run, which ones affect people, how they were tested, who approved them and how they are monitored. Frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 describe what good looks like, but they do not tell you how to wire it into a delivery pipeline.
Effective AI governance is proportionate and operational. Low-risk uses move quickly on standard controls. High-risk uses get deeper review, testing and monitoring. The evidence is produced as a byproduct of building and releasing the system, not reconstructed from emails and meeting notes when an auditor or regulator asks for it months later.
What you get
AI inventory and risk tiering
A register of AI systems in use and in development, including third-party and embedded AI, with owners, purposes, data, affected parties and a risk tier for each, using criteria your legal, risk and technology leaders agree on.
Governance framework and control set
Policies, roles, decision rights and a control catalog mapped to NIST AI RMF functions (Govern, Map, Measure, Manage), ISO/IEC 42001 requirements and, where relevant, EU AI Act obligations, scaled by risk tier.
Model risk management extension
For regulated firms, how generative AI and third-party models fit your existing model risk program: validation expectations, documentation standards, ongoing monitoring and how to treat models you cannot inspect.
Evaluation and testing standard
Required tests by risk tier for accuracy, groundedness, bias, robustness and safety, with acceptance thresholds, evaluation datasets and red-team expectations, plus how results are recorded as release evidence.
Governance workflow in the delivery pipeline
Built with your teams: intake, tiering, review and approval integrated with your ticketing and release tooling, so required evidence is captured automatically and approvals are traceable to a specific system version.
The operating architecture for AI governance
Governance works when it is built into systems rather than documents. These are the components we design so that AI governance produces evidence continuously and scales with the number of AI systems.
- AI system inventory
- The system of record for every AI use case, model and vendor AI feature, with owners, purpose, data categories, risk tier, approval status and links to evaluation evidence.
- Risk tiering and intake
- A short intake questionnaire that routes each use case to a risk tier based on impact on people, autonomy, data sensitivity and regulatory exposure, and assigns the matching review path.
- Control catalog and mappings
- Controls stated once and mapped to NIST AI RMF, ISO/IEC 42001, the EU AI Act and sector rules, so one piece of evidence can satisfy several frameworks.
- Evaluation service
- Shared tooling and datasets for accuracy, bias, robustness and safety testing, with results versioned and attached to the specific model and prompt version that was approved.
- Production monitoring
- Ongoing checks for drift, quality, incidents and user complaints, with thresholds that trigger re-review, rollback or retirement of a system.
- Evidence and audit trail
- Decisions, approvals, test results and changes recorded automatically from delivery tooling, producing the records auditors and regulators ask for without a scramble.
How we deliver it
Assess
We inventory the AI already in use, including shadow and vendor-embedded AI, review existing policies, model risk and privacy processes, and measure them against NIST AI RMF, ISO/IEC 42001 and the regulations that apply to you.
Blueprint
We design the governance operating model: risk tiering criteria, decision rights, control catalog, evaluation standards and review boards, sized to your risk appetite so low-risk work moves fast and high-risk work gets real scrutiny.
Build
We implement the inventory, intake workflow, evaluation tooling and evidence capture with your risk and engineering teams, then run the first set of real use cases through it and adjust based on what actually happens.
Govern
We can serve as ongoing architecture authority on your AI review board, support model validation, refresh controls as regulations change and prepare evidence for internal audit, regulators or an ISO/IEC 42001 certification audit.
What changes
- You know which AI systems you run, who owns them and how risky each one is.
- Low-risk AI use cases get approved on standard controls without waiting for a committee.
- High-risk systems carry documented testing, approval and monitoring evidence tied to specific versions.
- One control set maps to NIST AI RMF, ISO/IEC 42001 and the EU AI Act instead of three separate programs.
Related services
- AI Security ArchitectureSecurity for LLM apps and agents: threat modeling, prompt injection and data leakage controls, agent identity and red teaming.
- AI Operating Model and Center of ExcellenceOperating model, AI center of excellence, architecture review board and intake process designed so teams ship AI faster and safely.
- Data Architecture for AIData foundations for AI workloads: unstructured data pipelines, semantic layers, lineage, quality and access control for models and agents.
AI Governance and Risk: common questions
What does AI governance consulting include?
AI governance consulting typically covers building an inventory of AI systems, defining risk tiers and decision rights, writing policies and controls mapped to frameworks such as NIST AI RMF and ISO/IEC 42001, setting evaluation and monitoring standards, and putting the approval workflow into practice. Good engagements leave working processes and tooling, not just a policy document.
What is the difference between NIST AI RMF and ISO/IEC 42001?
NIST AI RMF 1.0, published in January 2023, is a voluntary framework organized around four functions: Govern, Map, Measure and Manage. ISO/IEC 42001:2023 is an international standard for an AI management system that organizations can be certified against. Many companies use NIST AI RMF for risk practice and ISO/IEC 42001 for a certifiable management system.
Does the EU AI Act apply to US companies?
It can. The EU AI Act, Regulation (EU) 2024/1689, entered into force on 1 August 2024 and applies to providers placing AI systems on the EU market and to deployers whose AI outputs are used in the EU, regardless of where they are based. Obligations phase in over several years; after the 2026 Digital Omnibus amendments, most high-risk obligations apply from 2 December 2027, so plan now and confirm dates with counsel.
How does model risk management apply to generative AI?
Model risk management applies, but it needs adapting. Generative models are often third-party, cannot be fully inspected and produce open-ended outputs. Banks and insurers extend their existing model risk programs with use-case level validation, evaluation against task-specific test sets, controls on prompts and retrieval, vendor due diligence and ongoing output monitoring rather than relying on model internals.
How do you govern AI without slowing teams down?
Make governance proportionate and automated. Tier use cases by risk, publish standard controls that low-risk work can adopt without review, and reserve committee time for high-impact systems. Integrate intake, testing and approval into the tools teams already use so evidence is captured as they build. Clear rules usually speed delivery because teams stop guessing.
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