Your Teams Are Using AI. Nobody's Actually Managing It.
Developers are using Copilot. Product is experimenting with AI features. Marketing is running campaigns with generative AI. Sales is using AI tools for prospecting. It's happening everywhere—and nobody has visibility, controls, or governance in place. Build an AI Management System before AI governance becomes your problem. Get structured, compliant, and audit-ready.
You'll Receive:
- Certified-ready ISO 42001 AIMS with complete policies, procedures, and governance framework
- Comprehensive AI management documentation aligned to ISO 42001 across all 4 core areas
- Risk-based implementation roadmap with prioritized controls and clear accountability structure
- Operational governance framework ready for internal audits and external certification assessments
Trusted by leading organizations worldwide
150+ European organizations served
Get the certification
Our cascading process ensures you are supported at every step
ASSESS
Through a gap analysis we evaluate the tasks required to comply with the criteria
- Gap analysis
- Identify stakeholders
- Conduct interviews
- Collect data
PLAN
We establish with you the roles and responsibilities, define objectives, establish a risk management process
- Establish roles & responsibilities
- Define objectives & priorities
- Perform risk management
- Create project plan
IMPLEMENT
We produce all required documentation and help you implement AI management measures
- Produce required documentations
- Implement AI management processes
- Communicate
OPERATE: Run the implemented measures, monitor and improve, track issues and progress
AUDIT: We establish with you the audit program and provide you with experienced auditors
CERTIFY: We support you in the selection of certification bodies and during the process
AI Governance Maturity Assessment
Answer 6 questions to understand your current AI governance maturity and what ISO 42001 implementation would involve.
Meet Your Compliance Experts
Swiss-trained professionals with decades of combined experience in regulatory compliance, risk management, and strategic advisory
Henri HAENNI
Expert in Business Continuity, Risk Management and Information Security Governance
ISO 27001 Lead Implementer & Auditor • ISO 37301 Lead Implementer • ISO 31000 Lead Risk Manager • Sorbonne University Paris 1 Lecturer
Alexis HIRSCHHORN
Expert in Information and Cyber Security, Cloud Security, Risk Management and Governance
ISO 27001 Lead Auditor • CISSP® Certified • ISO 42001 Lead Implementer • PECB MS Certifying Auditor
Laura Menétrey
Data Protection & Information Security Legal Expert
LLM in Data Protection Law • Certified GDPR Practitioner • Information Security Laws (NIS2, DORA) • Privacy Law Specialist
Jean MUNYARUGERERO
Information Security & Business Continuity Trainer
ISO 27001 Lead Implementer • CISM® Exam Bootcamp • ISO 27005 Risk Manager • NIST Cybersecurity Professional
Trusted by Leading Organizations
Real results from real clients who transformed their compliance operations
Frequently Asked Questions
Everything you need to know about this service
ISO 42001 is the international standard for AI Management Systems (AIMS), published in 2023. It provides a framework for: responsible AI development and deployment, AI risk management, governance and accountability, compliance with AI regulations (including EU AI Act). Think of it as ISO 27001 for AI—a systematic approach to managing AI throughout its lifecycle.
No, but they're aligned. ISO 42001: International standard, voluntary framework, covers all AI governance. EU AI Act: European regulation, mandatory for certain AI, legal requirements. ISO 42001 helps you comply with EU AI Act (and other AI regulations), but they're not identical. We map ISO 42001 implementation to EU AI Act requirements as part of the service.
Yes. ISO 42001 covers AI usage, not just AI development. If your teams use generative AI tools (ChatGPT, Claude, Midjourney), code assistants (GitHub Copilot, Amazon Q), AI-powered analytics or automation, third-party AI services - you need AI governance. Data leakage, inappropriate use, bias, and security risks exist even with third-party AI tools.
AI introduces unique risks that traditional security/privacy frameworks don't fully address: model bias and fairness, AI explainability and transparency, adversarial attacks on models, AI-specific data quality requirements, human oversight for automated decisions, AI-specific incident types (hallucinations, bias, drift). ISO 42001 complements ISO 27001 and GDPR, adding AI-specific governance.
High-risk AI: Significant impact on safety, rights, or critical decisions (e.g., hiring automation, credit scoring, medical diagnosis, critical infrastructure). Strict requirements, human oversight, conformity assessment. Limited-risk AI: Some risk, requires transparency (e.g., chatbots, AI-generated content). Must disclose AI usage to users. Minimal-risk AI: Low risk, minimal requirements (e.g., AI spam filters, recommendation engines for non-critical decisions). We classify your AI systems and apply appropriate controls to each.
Yes, actually better. Implementing governance early prevents problems later. You can: start with framework and policies, build AI inventory as systems are added, establish approval processes before extensive deployment, create foundation that scales with AI usage. Retrofitting governance after widespread AI adoption is harder and riskier.
We discover AI systems across your organization through: interviews with key stakeholders (engineering, product, ops, marketing, sales), technology stack review (applications, services, tools), procurement and vendor reviews, network and system analysis, user surveys for shadow AI discovery. Typical discovery: 3-4 weeks, covering all departments and functions.
We implement controls for SaaS AI tools: approved AI tool catalog, usage policies and guidelines, security configuration (data retention, privacy settings), data classification rules (what data can go into AI tools), monitoring for policy violations, alternative tools if needed (e.g., enterprise vs free versions). Goal: Enable AI usage safely, not block innovation.
For AI systems making automated decisions: identify protected attributes (gender, age, race, etc.), test for disparate impact across groups, validate fairness metrics appropriate to use case, diverse testing with representative data, ongoing monitoring for bias drift, remediation when bias detected. For high-risk AI, this is mandatory. For all AI, it's best practice.
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