Point of view
AI Governance
AI Governance & Risk
Deploy AI with confidence — responsible AI frameworks, model risk management, data privacy, security and regulatory compliance.
Overview
As AI moves into core business processes, regulators, customers and boards expect it to be fair, transparent, secure and well-controlled. Good governance is not a brake on innovation; it is what lets you scale AI safely.
We help organizations put practical guardrails in place — policies, controls, testing and monitoring proportionate to risk — aligned with emerging regulation and recognized frameworks.
Capabilities
What we deliver
Select a capability to see deliverables, technologies and FAQs.
01
Responsible AI frameworks
Principles, policies, roles and review processes that fit how your organization builds and buys AI.
Explore 02Model risk management
Inventory, risk tiering, validation and ongoing monitoring for traditional ML and generative AI models.
Explore 03Data privacy
Privacy-by-design for AI and analytics, including data minimization, consent and cross-border considerations.
Explore 04AI & data security
Threat modeling for AI systems, protections against prompt injection and data leakage, and secure access patterns.
Explore 05Regulatory compliance
Readiness assessments and control mapping against emerging AI regulation and frameworks such as the EU AI Act, NIST AI RMF and ISO/IEC 42001.
Explore 06Evaluation & monitoring
Testing for accuracy, bias, robustness and drift, with dashboards and alerts for production systems.
ExploreOutcomes
What clients can expect
We measure success by business results, and agree how we will measure them before we start. Open an outcome to see how we deliver it and the measures we track.
A clear inventory of AI systems and their risk levels
We build an inventory that covers internally built models, third-party tools and AI features embedded in software you already buy. Each system is risk-tiered so oversight effort goes where it matters.
How we measure it
- Share of AI systems inventoried and risk-tiered
- Systems with a named business and technical owner
- Time to register and classify a new AI use case
Governance proportionate to risk, not paperwork for its own sake
Risk tiers determine which controls and reviews apply, so low-risk uses move quickly and high-risk uses get proper scrutiny. We review the process regularly and remove steps that do not reduce real risk.
How we measure it
- Average approval time by risk tier
- Reviews completed within target timelines
- Stakeholder satisfaction with the review process
Readiness for audits and regulatory scrutiny
Controls are mapped to relevant regulations and standards, with evidence captured as part of normal delivery. When auditors or regulators ask, the documentation already exists.
How we measure it
- Control coverage against applicable frameworks
- Open audit findings and time to remediate
- Completeness of model documentation
Reduced exposure to privacy, security and reputational incidents
Privacy-by-design, AI-specific security controls and red teaming reduce the likelihood of incidents. Monitoring and response playbooks limit their impact when they do occur.
How we measure it
- AI-related incidents and near misses
- Time to detect and respond to AI incidents
- Open findings from red-team and security testing
Faster approval paths for new AI use cases
A standard intake, pre-approved patterns and clear criteria give teams a predictable route from idea to approval. Governance becomes an enabler that helps good ideas reach production.
How we measure it
- Time from intake to approval decision
- Use of pre-approved patterns and components
- Use cases approved per quarter
Approach
How we deliver
Select a step to see the activities, deliverables and typical timing.
Step 1 · 3–6 weeks
Baseline
Inventory AI use, assess current controls and identify the most material risks.
We discover where AI is used across the organization, including unsanctioned tools and AI embedded in vendor software, and assess existing policies and controls against relevant frameworks. Findings are prioritized by materiality.
Activities
- AI use discovery and inventory
- Interviews across business, technology, risk and legal
- Control assessment against relevant frameworks
- Risk prioritization
Deliverables
- AI inventory
- Maturity and gap assessment
- Prioritized risk register
Step 2 · 4–8 weeks
Design
Define policies, roles, risk tiers and control requirements aligned with relevant frameworks.
We design the governance framework, including policy, risk tiering, roles and review processes, and map controls to applicable regulations and standards. Designs are tested against real use cases before rollout.
Activities
- Policy and standards drafting
- Risk-tiering and intake design
- Roles and decision rights definition
- Control mapping to frameworks
Deliverables
- AI governance framework and policy
- Risk-tiering methodology
- Control library mapped to frameworks
Step 3 · 6–12 weeks
Implement
Embed controls into delivery workflows, tooling and model lifecycle processes.
We integrate governance steps into how teams already build and buy technology, including intake, development pipelines, procurement and change management. Teams receive training and templates so controls are easy to follow.
Activities
- Workflow and tooling integration
- Templates, checklists and documentation standards
- Training for builders, buyers and reviewers
- Pilot with priority use cases
Deliverables
- Operational governance workflows
- Templates and training materials
- Pilot review outcomes
Step 4 · Ongoing
Monitor
Establish testing, monitoring and reporting so governance keeps pace with change.
We set up ongoing evaluation, monitoring and reporting so leadership has a current view of AI risk. The framework is reviewed periodically to reflect new regulations, technologies and lessons learned.
Activities
- Production monitoring and alerting setup
- Governance reporting and dashboards
- Periodic control testing
- Regulatory horizon scanning and framework updates
Deliverables
- AI risk dashboard
- Periodic governance reports
- Framework update recommendations
Industries we serve
FAQs
Questions we often hear
Can’t find what you’re looking for? Browse all FAQs or ask our team.
Does AI governance slow innovation down?
Done well, it speeds it up. Clear risk tiers and pre-agreed controls mean low-risk use cases move quickly, while high-risk ones get the scrutiny they need.
Is this relevant if we only use third-party AI tools?
Yes. Many obligations apply to deployers as well as developers, and third-party tools introduce data, security and vendor risks that need managing. AI features embedded in software you already license belong in your inventory too.
Do you replace our legal, risk or compliance teams?
No. We work alongside them, bringing AI-specific risk, controls and technical expertise, and build governance into your existing structures. We do not provide legal advice: your legal counsel remains responsible for legal interpretation, and our role is to turn requirements into practical policies, processes and technical controls.
Which frameworks and regulations do you work with?
We commonly work with the EU AI Act, NIST AI RMF, ISO/IEC 42001, GDPR, HIPAA, SR 11-7 and DORA, along with sector guidance relevant to your industry. We map controls across frameworks so one set of evidence can serve several requirements.
How long does it take to stand up an AI governance program?
A baseline assessment typically takes three to six weeks, and designing the framework takes a further four to eight weeks. Implementation is usually phased over several months, starting with the highest-risk systems and the most active teams.
How do you price governance engagements?
Assessments and framework design are usually fixed in scope and fee. Implementation support, independent validation and ongoing monitoring can be delivered as defined projects, on a time-and-materials basis or as a retained service.
How do you handle our confidential information and model details?
We work under confidentiality agreements and your security policies, access systems through your controlled environments and limit data access to what each review requires. Sensitive findings, such as security test results, are shared only with agreed stakeholders.
How do we get started?
Most clients start with a baseline assessment: an inventory of AI use, a review of current controls and a prioritized view of the most material risks. That provides a clear foundation for designing a proportionate governance program.
Insights
Related thinking
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