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ISO/IEC 42001:2023 AI Management System Checklist

Assess your organization's AI management system against the ISO/IEC 42001:2023 standard. 22 questions across 5 categories.

AI Policy & Leadership Commitment

Establishing the AI governance framework and ensuring top management commitment.

Do you have a documented AI management system policy approved by top management?

ISO 42001 requires establishing a formal AI management system policy.

Yes, documented and approved by top management A draft exists but is not yet approved No AI policy exists

Have you defined measurable, AI-specific objectives and KPIs?

Clearly defined AI objectives and KPIs are essential for measuring performance and the effectiveness of the management system.

Yes, objectives are defined with regular measurement Objectives exist but are not measured regularly No AI-specific objectives have been defined

Does top management actively support and provide adequate resources for the AI management system?

Top management commitment is critical for successful AI governance.

Active support with adequate resources provided Support is limited or provided on an ad-hoc basis No dedicated support or resources are provided

Are AI-related roles and responsibilities clearly defined and documented?

Clear responsibilities ensure accountability.

Yes, all roles and responsibilities are documented Only key roles are defined No roles have been formally defined

Do you have a competency framework for personnel involved with AI?

A competency framework ensures the team possesses the necessary skills and knowledge for their roles.

A comprehensive competency framework and training plan is in place Only basic competency requirements exist No formal competency framework exists

AI Lifecycle Management

Managing the entire AI system lifecycle, from design to decommissioning.

Do you document the complete AI system lifecycle, from design to decommissioning?

Full lifecycle documentation is a core requirement for ISO 42001 compliance.

Yes, the full lifecycle is documented Documentation is partial or incomplete No lifecycle documentation exists

Do you maintain an up-to-date inventory of all AI systems within the organization?

An inventory of AI systems helps to maintain oversight and track all active systems.

Yes, a complete and updated inventory is maintained An inventory exists but it is not regularly updated No AI system inventory is maintained

Is there a formal change management process for modifications to AI systems?

A formal change management process ensures all modifications are controlled, tested, and documented.

Yes, a formal, documented process is followed An informal process exists No change management process is in place

Do you have a defined process for secure AI system retirement and the handling of associated data?

A system retirement process ensures that data is handled securely at the end of the system's lifecycle.

Yes, a defined retirement process exists Retirement is handled on an ad-hoc basis No retirement process is defined

Do you use version control and ensure full traceability for AI models and data?

Version control and traceability are critical for reproducibility, auditability, and debugging.

Yes, full version control and traceability are implemented Partial version control or traceability is in place No version control or traceability measures are used

Risk Management & Impact Assessment

AI-specific risk management and conducting impact assessments.

Do you have an AI risk assessment methodology aligned with ISO 42001?

A structured risk assessment methodology is necessary to systematically identify and evaluate risks associated with AI systems.

Yes, we have a methodology aligned with ISO 42001 We use a generic risk assessment, not specific to AI No AI risk assessment methodology exists

Do you conduct AI Impact Assessments (AIA) for new or significantly modified AI systems?

An AI Impact Assessment (AIA) helps to identify and understand the potential societal, ethical, and individual impacts of a system.

Yes, AIA is mandatory for all relevant systems Only for systems classified as high-risk Occasionally, on a case-by-case basis We do not conduct AIAs

Are there documented risk treatment plans for identified AI risks?

Risk treatment plans define the specific controls and actions to mitigate identified risks.

Yes, treatment plans exist for all identified risks Plans exist only for major or high-priority risks No documented treatment plans exist

Do you maintain an AI risk register and regularly monitor the risks?

A risk register helps to track all identified risks and their corresponding treatment plans.

Yes, a register is maintained and regularly monitored A register exists but is not regularly monitored No AI risk register is maintained

Data & Model Quality

Ensuring the quality of data and models that underpin the AI systems.

Do you have a data quality management process for AI training and inference data?

Data quality is critical for the performance and reliability of AI models.

Yes, a systematic data quality management process is in place Only basic data quality checks are performed No data quality management process exists

Do you have a formal model validation and testing process before deploying to production?

Model validation ensures the AI system performs as intended and meets its requirements.

Yes, we have a comprehensive validation and testing process Only basic or informal testing is conducted No formal validation process is in place

Do you continuously monitor the performance of AI models in production?

Continuous monitoring helps to detect performance degradation and model drift in a timely manner.

Yes, continuous and automated monitoring is in place Only periodic or manual checks are performed No performance monitoring is conducted

Do you have automated detection for model drift and data drift?

Drift detection helps identify when model performance degrades due to changes in the input data distribution.

Yes, automated drift detection is implemented Drift is checked manually and periodically No drift detection measures are in place

Compliance & Continuous Improvement

Ensuring compliance with legal and other requirements, and fostering continuous improvement.

Do you conduct regular internal audits of the AI management system?

Internal audits help assess the effectiveness of the system and identify areas for improvement.

Yes, regular, scheduled audits are conducted Audits are conducted occasionally or on an ad-hoc basis No internal audits are conducted

Does top management regularly review the performance of the AI management system?

Management reviews ensure ongoing oversight and alignment with strategic objectives.

Yes, regular management reviews are held Reviews are conducted annually No formal management reviews take place

Is there a process for managing non-conformities, implementing corrective actions, and ensuring continuous improvement?

A formal process for corrective actions ensures that problems are systematically resolved and lessons are learned.

Yes, a systematic process for corrective actions is in place Issues are handled informally on a case-by-case basis No formal process for corrective actions exists

Do you track and ensure compliance with applicable AI regulations (e.g., EU AI Act)?

Tracking and complying with relevant regulations is essential to avoid legal and financial risks.

Yes, we actively track regulations and ensure compliance We are aware of regulations but have no formal compliance program We do not actively track regulatory changes

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