ISO/IEC 42001:2023 Artificial Intelligence Management
Govern AI responsibly.
Build oversight that works.
Practical ISO 42001 toolkits combining editable AIMS documentation, AI governance resources, risk and impact tools, lifecycle management and evidence-focused guidance to help organisations establish, operate and continually improve an Artificial Intelligence Management System.
Understanding ISO 42001
Bring structure, accountability and oversight to organisational AI.
ISO/IEC 42001 is an international management-system standard focused specifically on artificial intelligence. It provides a structured framework for organisations to manage AI-related responsibilities, risks, opportunities and impacts while establishing appropriate governance and oversight.
An Artificial Intelligence Management System can help move an organisation from informal or fragmented AI use towards clearer ownership, repeatable governance and evidence that arrangements operate in practice.
A Working AI Management System
AI governance is more than an AI policy.
A functioning AIMS connects leadership, risk, impact assessment, data governance, lifecycle management, monitoring, assurance and continual improvement.
Governance & Accountability
Establish leadership responsibilities, ownership, approval authorities, escalation routes and oversight.
AI Risk & Opportunity
Identify, analyse, evaluate, treat and monitor AI-related risks while recognising appropriate opportunities.
AI Impact Assessment
Consider potential impacts on individuals, customers, employees, suppliers and other relevant stakeholders.
Lifecycle Management
Apply structured governance throughout relevant stages of AI selection, development, deployment, operation, change and retirement.
Data & Third Parties
Address data-governance considerations and AI-related supplier and external-provider relationships.
Performance & Improvement
Monitor effectiveness, audit the AIMS, complete management review and continually improve governance arrangements.
More Than ISO Templates
Move from documentation to demonstrated operation.
Verivexa resources are designed around implementation and evidence, not document volume alone. A management system becomes meaningful when its arrangements operate, generate evidence, are evaluated and improve over time.
Establish intended governance, responsibilities and processes.
Put those arrangements into practical operation.
Retain assessments, registers, decisions and operational records.
Monitor performance, audit the system and conduct management review.
Address findings, corrective actions and changing AI risks.
Practical AI Governance
Connect governance decisions across the AI environment.
Different organisations use AI in different ways. The management system should reflect the organisation's actual AI systems, responsibilities, risks, stakeholders and operating environment.
AI Inventory & Ownership
Maintain visibility of AI systems and establish accountable owners and responsibilities.
Risk & Impact
Establish repeatable approaches for AI risk management and AI-system impact assessment.
Responsible AI
Consider transparency, human oversight, stakeholder expectations and appropriate information provision.
Data Governance
Address relevant considerations around data ownership, quality, provenance, preparation and management.
Suppliers & Third Parties
Apply structured evaluation, responsibility and monitoring to external AI products and providers.
Performance & Oversight
Establish monitoring, management information, internal audit and leadership review of AIMS effectiveness.
AI System Lifecycle
Governance should continue throughout the life of the AI system.
The exact lifecycle varies depending on whether AI is developed internally, purchased from a third party or delivered through a combination of both approaches.
Choose Your Toolkit
Choose the level of AI management support that fits your organisation.
Start with the core AIMS foundations, build a comprehensive operating framework or add dedicated implementation and certification-readiness support.
Essentials
- Organisational context & AIMS scope
- Leadership & AI governance
- Roles & responsibilities
- AI risk & opportunity management
- AI system impact assessment
- Objectives & planning
- Competence & awareness
- Documented-information foundations
Professional
- Everything in Essentials
- AI system lifecycle management
- AI inventory & ownership
- Data-governance resources
- Transparency & responsible AI
- Supplier & third-party governance
- Performance evaluation
- Internal audit & management review
- Corrective action & continual improvement
Complete
- Everything in Professional
- Implementation tracking
- Clause & Annex A readiness tools
- Statement of Applicability support
- Evidence identification & mapping
- Implementation roadmaps & dashboards
- Stage 1 readiness support
- Stage 2 readiness support
- Audit-interview preparation
Focused ISO 42001 Resources
Strengthen a specific area of your AIMS.
Already have wider governance arrangements in place? Choose specialist resources for focused AI-management support.
AI Risk & Impact Assessment
Structure AI risk identification, assessment, treatment, ownership, opportunity management and AI-system impact assessment.
Explore Risk & Impact →AI Governance & Lifecycle
Strengthen leadership, accountability, AI ownership, governance decisions, system inventory and lifecycle management.
Explore Governance & Lifecycle →Internal Audit
Plan, conduct, evidence, report and follow up effective ISO 42001 internal audits.