
GRACIE™ Framework Reveal – World Economic Forum
Davos, Switzerland • Upcoming: Jan 2026
Select one or multiple frameworks.

Compliance, Risk & Responsible AI Expert
International Compliance Association
Global Council for Responsible AI

Regulatory Compliance Lawyer (Funds & FinTech)
Responsable du contrôle du respect des obligations
Global Council for Responsible AI
Choose your assessment approach
Managed service. Final report included.
A regulator-facing summary of your AI system maturity and compliance posture across EU AI Act, ISO/IEC 42001, DORA, and GDPR.
Centralised log of risks, control gaps, and compliance observations — mapped to frameworks and evidence sources.
Actionable plan to address gaps, strengthen controls, and align with internal audit, 2LoD, and regulatory expectations.
One-click access to supporting documentation, walkthroughs, and TOE evidence — ready for Regulatory review or internal audit reviews.
Embedding risk mitigation into the AI lifecycle.
Regulators • Industry • Practitioners • Researchers

Working sessions focused on embedding risk mitigation into the AI lifecycle.
Embedding risk mitigation into the AI lifecycle.
Regulators • Industry • Practitioners • Researchers

Working sessions focused on embedding risk mitigation into the AI lifecycle.
Training material and content tracks for each framework we support.
High-risk use case identification, Art. 9–15 compliance, post-market monitoring, and traceability under Art. 50.2. Includes AI impact templates and AI governance controls.
How to implement an AI Management System (AIMS), align with control objectives, and prepare for internal audit. Includes Annex A walkthroughs.
Managing ICT risk in AI systems, LLM/API third-party oversight, incident response, and resilience obligations. Includes risk registers and control testing templates.
Art. 22 safeguards, Recital 71 fairness, DPIAs, and AI profiling compliance. Includes privacy + AI joint risk assessment templates.
Expectations from CSSF Circulars, CAA rules, and CNPD guidelines. Covers traceability, explainability, and model accountability in AI systems.
Ethical AI principles: fairness, robustness, transparency, and human-centred oversight. Includes ESG-aligned governance examples and templates.
Risk identification, WCGW mapping, control design testing, and mitigation specific to AI systems. Supports 2LoD assurance and RCSA execution.
Cybersecurity for AI-as-ICT asset, incident reporting, and supply chain oversight. Aligned with NIS2 obligations.
Secure model deployment, adversarial AI threats, access control, and prompt injection safeguards. Layers into ISO 27001-aligned InfoSec frameworks.







"Educating leaders on AI governance at scale - from startup founders to enterprise executives, helping organizations navigate the complex landscape of AI compliance and ethics."
Regular speaker at international conferences, corporate events, and regulatory forums. Topics include AI governance, EU AI Act compliance, responsible innovation, and the future of RegTech.
Includes gap analysis, evidence index, and a remediation roadmap.