Services
Comprehensive AI governance solutions that bridge regulatory requirements with operational reality.
AI governance frameworks and practices
Build the structure, accountability, and controls that make AI governance real.
Responsible AI demands clear governance. This service turns governance from aspiration into operational practice.
What's delivered:
- Governance model design: Charters and procedures aligned with ISO/IEC 42001
- Decision frameworks: Stage-gate criteria and approval workflows
- Accountability structures: Clear roles for AI owners and compliance leads
- Monitoring and reporting: KPIs, dashboards, and audit trails
- Policies and codes: Enforceable standards for teams and vendors
Who this is for:
- Businesses formalizing AI operations
- Public agencies with regulatory and oversight duties
- Risk and compliance officers establishing AI governance
- Organizations preparing for regulatory audits
Multi-framework compliance mapping
One assessment and one evidence base for every applicable instrument.
AI systems rarely sit under a single regulation. The same deployment can trigger obligations under the EU AI Act, GDPR, Convention 108+, the Council of Europe Framework Convention on AI, NIS2, DORA, and ISO/IEC 42001, all at the same time. This service maps every applicable obligation into a single, integrated assessment architecture, so you produce one evidence base instead of duplicating work across instruments.
What's delivered:
- Instrument applicability mapping: Which regulations and standards apply, at what organizational and system level
- Gap and duplication analysis: Where obligations overlap and where a single exercise satisfies multiple requirements
- Integrated assessment architecture: A sequenced plan for producing evidence that stands up to every applicable framework at once
Who this is for:
- Organizations operating across multiple jurisdictions or sectors with overlapping AI, cybersecurity, and data-protection obligations
- Financial institutions and critical infrastructure operators facing AI Act, NIS2, and DORA requirements on the same systems
- Any organization looking to avoid parallel, instrument-by-instrument compliance exercises
Bridging the gap between AI regulation and operationalization
From compliance to operational reality — across the entire AI lifecycle.
Compliance with AI regulation applies across the lifecycle: design, development, deployment and decommissioning each produce their own evidence.
What's delivered:
- Full lifecycle mapping: Regulatory requirements translated into actions aligned with ISO/IEC standards and NIST RMF
- Role-based implementation playbooks: Tailored guidance for developers, compliance officers, and operations teams
- Continuous compliance integration: Requirements embedded into existing workflows
- Evidence and documentation frameworks: Specific templates aligned with the AI Act, Council of Europe Framework Convention, UNESCO Recommendation on the Ethics of AI and other relevant guidance
- GPAI model compliance: Transparency and systemic-risk obligations for general-purpose AI model providers under Articles 53 and 55
Who this is for:
- AI developers and tech companies launching AI products
- Businesses operationalizing AI across business units
- Organizations demonstrating lifecycle compliance to customers and regulatory bodies
Risk and impact assessment in the design and use of AI systems
Identify, measure, and mitigate AI risks before they become problems.
Risk and impact assessment establishes who is affected by an AI system, how, and which controls reduce the impact before deployment.
What's delivered:
- Comprehensive risk profiling: Context scoping following the EU AI Act's FRIA (Fundamental Rights Impact Assessment), ISO/IEC 42005 and 23894, NIST AI RMF, the Council of Europe's HUDERIA methodology (including COBRA), and OECD AI Principles
- Stakeholder impact mapping: Identification of who is affected and what safeguards are needed
- Pre-alignment and readiness checks: Data quality, human oversight design, capability gaps
- Control plans: Risk mitigation strategies and go/no-go decision frameworks
- Cybersecurity crossover mapping: Alignment with NIS2 risk-management measures and DORA ICT risk requirements for AI systems in regulated and critical-infrastructure sectors
Who this is for:
- SMEs adopting vendor AI tools
- Public agencies piloting AI-driven decision support
- Businesses launching high-risk AI use cases
- Organizations demonstrating due diligence to regulators and boards
- Financial institutions assessing credit-scoring or insurance-underwriting AI systems
- Healthcare organizations deploying clinical decision-support or triage AI
Change and process management for AI adoption
Redesign processes so that AI systems create value and compliance is built into each step.
Success requires redesigning processes, redefining roles, and managing the human side of change.
What's delivered:
- Process reengineering: As-Is and To-Be mapping with compliance checkpoints
- Change readiness planning: Stakeholder engagement and training programs
- RACI redesign: Clear accountability for AI oversight and decision-making
- Reinforcement mechanisms: Ongoing monitoring and feedback loops
Who this is for:
- Operations leaders integrating AI into core processes
- Private and public organizations adopting AI systems
- Teams delivering productivity gains and regulatory compliance
