AI Governance Advisory: Built for the AI Act Era.
The EU AI Act changes everything. We help you build governance structures that satisfy regulatory requirements, board oversight, and operational reality—without cargo-cult compliance theater.
What Drives AI Governance Needs
AI governance isn't optional anymore. Here's why it matters.
EU AI Act Compliance
The EU AI Act creates explicit requirements for high-risk AI systems: risk assessment, quality management, human oversight, transparency, monitoring. You need documented governance to demonstrate compliance.
Board AI Oversight
Boards now expect governance frameworks for AI risk management. Not knowing what AI systems you have, what they do, and what risks they pose is a governance failure.
Incident Prevention
Structured governance prevents incidents: documented risk assessments catch problems before deployment; testing protocols reduce production failures; monitoring catches drift early.
Insurance & Liability
Liability insurers increasingly require documented AI governance as a condition of coverage. "We use AI but don't know how" isn't acceptable.
Regulatory Audits
Regulators and auditors are asking: what AI systems do you have, how are they governed, what risks do they pose? You need answers ready.
Competitive Advantage
Organizations with strong AI governance move faster with confidence. Weak governance creates risk that slows decision-making and deployment.
Our AI Governance Advisory Services
From assessment to implementation to ongoing monitoring.
AI Systems Inventory & Risk Classification
We catalog your AI systems, classify them by risk level (prohibited, high-risk, general purpose, minimal risk), and map regulatory obligations to each. You get clarity on what you have and what it means legally.
Governance Framework Design
We design AI governance structures that fit your organization: decision rights, approval workflows, testing requirements, oversight bodies, documentation standards. Based on industry best practices and regulatory requirements.
Policy & Procedure Development
We create specific policies: AI development standards, bias testing protocols, model validation procedures, human review requirements, transparency obligations. Not generic templates—tailored to your context.
Risk Assessment & Mitigation
We assess your AI portfolio for risks: bias, safety, explainability, security, regulatory. For each risk, we design mitigation strategies and controls.
Board Reporting & Communication
We develop materials for board oversight: AI risk dashboards, governance status reporting, compliance tracking. Make AI governance visible and manageable at the board level.
Understanding EU AI Act Risk Tiers
The EU AI Act categorizes AI systems by risk level, with different requirements for each.
Prohibited AI Practices
Specific AI practices banned entirely: subliminal manipulation, social credit scoring, real-time facial recognition in public spaces (with limited exceptions). If you're using these, you need to stop.
High-Risk AI Systems
AI used in critical infrastructure, employment decisions, credit assessment, law enforcement, education. These require: risk assessments, quality management, human oversight, testing, monitoring, documentation, transparency.
General Purpose AI
Foundation models, large language models, multimodal models. Providers must document training data, design for EU AI Act compliance, provide technical documentation. Users must assess if their specific application becomes high-risk.
Minimal Risk / Unregulated
Most AI systems that don't fall into other categories. Fewer compliance obligations, but good governance practices still recommended.
Seven Pillars of AI Governance
A comprehensive AI governance framework spans these seven areas.
Portfolio Management
Inventory of all AI systems, their purpose, risk classification, and deployment status. You can't govern what you don't know you have.
Risk Assessment
Systematic evaluation of each AI system for bias, safety, security, fairness, explainability, and regulatory compliance. Risk assessment happens before deployment.
Development Standards
Requirements for how AI systems are built: data governance, model validation, testing protocols, documentation. Standards apply across the organization.
Human Oversight
Decision-making about AI deployment and operation: approval workflows, escalation procedures, human review requirements. Not all decisions should be fully automated.
Monitoring & Maintenance
Post-deployment monitoring for drift, bias emergence, performance degradation. Maintenance procedures when issues are detected. Systems change over time.
Transparency & Communication
Clear communication to users when they're interacting with AI: what it is, what it does, limitations, how to appeal. Transparency builds trust.
Governance Oversight
Board and executive oversight of AI governance: regular reporting, risk escalation, strategy alignment. Governance itself needs governance.
Our Engagement Approach
Three phases from assessment to implementation to ongoing support.
Phase 1: Governance Assessment
2-4 weeksWe assess your current state: what AI systems exist, what governance is in place, what gaps exist relative to regulation and best practices. Deliverable: comprehensive governance assessment report with gap analysis and prioritized recommendations.
Phase 2: Framework Design & Implementation
4-12 weeksWe design and help implement governance structures: policies, procedures, processes, decision workflows, oversight bodies, reporting mechanisms. You get documented, operable governance tailored to your organization.
Phase 3: Ongoing Governance Support
OngoingWe support governance evolution: policy updates as regulations change, training for teams, audit support, governance monitoring and improvement. Governance is never "done."
Who Needs AI Governance Advisory?
Organizations in the EU or regulated sectors
Subject to EU AI Act, GDPR, or sector-specific AI regulations. You need documented governance to demonstrate compliance.
Companies deploying high-risk AI
Using AI in hiring, lending, healthcare, autonomous systems, or other high-consequence domains. Risk governance prevents incidents.
Boards and executives asking AI questions
Your board is asking what AI systems you have, what risks they pose, how they're governed. You need answers and frameworks.
Organizations seeking insurance coverage
Liability insurers require documented AI governance. We help you build the governance that insurers expect.
Fast-growing AI teams
Your AI capability is growing faster than governance. Structures put in place now prevent problems at scale.
Post-incident organizations
You had an AI incident. Now you need governance to prevent recurrence and rebuild trust with stakeholders.
Case Study: European Financial Services AI Governance Transformation
The Situation: A mid-sized European financial services company deployed multiple AI systems for lending decisions, customer segmentation, and fraud detection. They had no documented governance: systems were built by different teams using different standards, risk was unmeasured, oversight was minimal.
The Challenge: An audit by financial regulators raised concerns: lack of AI governance, unclear risk assessment, no documented validation of lending models, no bias monitoring. The organization faced enforcement action if governance wasn't established.
Our Approach: We conducted a comprehensive AI governance assessment, identifying 12 deployed AI systems with varying risk profiles. We designed a governance framework covering portfolio management, risk assessment, development standards, human oversight, monitoring, and board reporting.
The Outcome: Within 6 months, the organization had:
- Documented portfolio of 12 AI systems with clear risk classifications
- Implemented risk assessments for all high-risk systems
- Established bias monitoring for lending models
- Created development standards for new AI projects
- Designed board governance reporting on AI risks
- Passed regulatory re-audit with no findings
With proper governance, they moved from audit risk to competitive advantage: faster AI deployment, lower operational risk, and board confidence in AI strategy.
Frequently Asked Questions
When does the EU AI Act go into effect?
The EU AI Act has a phased implementation: prohibited practices take effect immediately, general purpose AI requirements in mid-2025, high-risk AI system requirements in mid-2026. Different provisions have different timelines, so you need to understand which rules apply to you and when.
Does the EU AI Act apply to non-EU companies?
Yes. If you process data of EU residents or deploy AI systems affecting EU people, you likely need to comply. The Act's scope is broad and applies to many global companies, not just EU-based organizations.
What does "high-risk AI" actually include?
High-risk systems include those used in employment (hiring, performance evaluation), credit/lending decisions, law enforcement, autonomous vehicles, critical infrastructure, biometric identification, and education. If your AI makes consequential decisions about people, it's probably high-risk.
Ready to Build AI Governance That Works?
Start with an assessment. We'll identify your governance gaps and create a roadmap to compliance and competitive advantage.
Schedule Governance Assessment