Course description

This course provides comprehensive guidance on implementing ISO 30201:2019, the international standard that outlines the core principles and framework for a Records Management System (RMS). ISO 30201 is part of the ISO 30300 family and supports organizations in ensuring the integrity, reliability, usability, and preservation of records over time. This training equips professionals with the tools to establish a records management framework aligned with organizational objectives, regulatory requirements, and digital transformation initiatives.

What You’ll Learn

By the end of the course, you will be able to:

• Understand the purpose, scope, and terminology of ISO 30201:2019.

• Identify the structure and components of a records management system (RMS).

• Align RMS policies and procedures with ISO 30301 and ISO 15489 standards.

• Define roles and responsibilities for records governance and lifecycle management.

• Establish processes for creating, capturing, classifying, and securing records.

• Apply risk-based approaches to retention, access, and disposal of records.

• Manage digital records, metadata, and system interoperability.

• Ensure compliance with legal, regulatory, and ethical obligations.

• Support audits, investigations, and business continuity through reliable records.

• Monitor and improve the RMS based on performance indicators and feedback.

Who Should Attend

• Records and Information Managers

• Document Controllers and Archivists

• Compliance and Governance Officers

• Risk and Legal Affairs Professionals

• IT Professionals managing document systems

• Consultants implementing RMS or ISO 30301 systems

Recommendations

• Prior experience with document control or compliance is helpful

• No prior ISO 30201 experience required

• Suitable for public, private, and nonprofit sectors managing sensitive or critical records

Skills

• ISO 30201 clause interpretation

• Records lifecycle governance

• Risk-based records retention planning

• Digital records and metadata management

• Records access, privacy, and security controls

• Compliance with ISO 30301, ISO 15489, and local laws

• RMS audit readiness and documentation

• Integration with quality or information management systems

• Continuous improvement of records processes

Course description

This practitioner course turns ISO/IEC 42001 into a practical blueprint for establishing, implementing, maintaining, and improving an Artificial Intelligence Management System (AIMS). We link strategy and policy to clear governance, roles, and accountability for AI products and services. You’ll learn to scope your AIMS, identify stakeholders, and run risk/impact assessments that address safety, bias/fairness, privacy, security, and explainability. We translate lifecycle controls into day-to-day practices: data management, model design and testing, validation, deployment, monitoring, incident/change management, and decommissioning. The course covers documentation and traceability, transparency measures, human oversight, supplier/third-party model management, and metrics for performance and harm reduction. You’ll prepare audit-ready evidence, internal audit plans, and management review inputs that drive continual improvement and align with broader IMS or ESG commitments. Jurisdictional notes highlight privacy, accessibility, and sector-specific regulations relevant to your context. Designed for organizations that develop or use AI, from startups to the enterprise and public sector.

What you’ll learn

• Translate ISO/IEC 42001 into an AIMS roadmap

• Define scope, policy, roles, and governance structure

• Run AI risk/impact assessments (bias, safety, privacy, security)

• Implement lifecycle controls: data, design, testing, deployment, change

• Establish monitoring, incident response, and model drift management

• Manage third-party models, data providers, and contracts

• Design transparency, human oversight, and recordkeeping

• Prepare for internal audits, management reviews, and improvement

Who should attend

• AI/ML leaders, product owners, and data science managers

• Risk, compliance, privacy, and information security teams

• Quality/IMS managers integrating AI governance

• Engineering and platform (MLOps) leaders

• Executives accountable for AI strategy and ethics

Recommended

• Basic ISO/PDCA or governance/risk knowledge

• Familiarity with AI/ML concepts and data pipelines

• Awareness of privacy/security frameworks (helpful)

• Experience with software lifecycle or vendor management (helpful)