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AIMS - ISO 42001:2023 Pre -Assessment

AIMS - ISO 42001:2023 Pre -Assessment

Evaluate your organization's readiness for ISO 42001 certification. This assessment helps identify gaps in your AI management system before your formal certification audit. Complete all questions to get a clear picture of where your organization stands against the standard's requirements.
34Questions
  • 1
    This pre-assessment helps determine if your organization is ready for ISO 42001 certification. ComplyAI Hub's evaluation method will identify potential nonconformities that should be addressed before a formal certification audit. The content is proprietary to ComplyAI Hub and redistribution without permission is prohibited.
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  • 2
    Have you clearly defined which AI systems and processes your AIMS covers? Focus Area: Identifying what’s included within your AI Management System. Expectations: A documented scope that outlines all AI-related processes, systems, data, and locations. Documentation Required: AIMS Scope Statement, Organizational Context Document.
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  • 3
    Can you show that leadership supports and is involved in your AIMS? Focus Area: Top management’s role in guiding and supporting AI governance. Expectations: Evidence of leadership direction, resource allocation, and policy endorsement. Documentation Required: AI Policy signed by leadership, meeting records, resource plans.
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  • 4
    Do you perform risk assessments for your AI systems? Focus Area: Identifying and evaluating risks related to AI use. Expectations: A structured process for identifying, analyzing, and mitigating AI risks. Documentation Required: Risk register, AI risk matrix, mitigation plans.
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  • 5
    Do you have a policy that defines ethical AI development and use? Focus Area: Establishing ethical principles in AI practices. Expectations: A documented policy covering fairness, non-discrimination, and accountability. Documentation Required: Ethical AI policy, principles and values statement.
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  • 6
    Is there a centralized record of all AI systems in your organization? Focus Area: Maintaining oversight of AI assets in use. Expectations: A comprehensive list of AI systems, their purpose, ownership, and use cases. Documentation Required: AI asset register, system inventory spreadsheet.
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  • 7
    Are data quality, security, and privacy actively managed in AI systems? Focus Area: How data used in AI is collected, stored, and governed. Expectations: Policies and controls for managing data lifecycle and compliance. Documentation Required: Data management policy, access control matrix.
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  • 8
    Do you test your AI systems for bias and fairness? Focus Area: Preventing discrimination and promoting equity in AI outcomes. Expectations: Regular testing and mitigation strategies for bias. Documentation Required: Bias test reports, fairness evaluation logs.
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  • 9
    Can stakeholders understand how your AI systems make decisions? Focus Area: Explainability of AI outputs to users and regulators. Expectations: Mechanisms for providing clear, understandable insights into AI behavior. Documentation Required: Explainability reports, user-facing documentation.
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  • 10
    Have stakeholders been consulted in your AI development or governance process? Focus Area: Involving those affected by AI systems in decisions. Expectations: Dialogue and feedback mechanisms for internal and external stakeholders. Documentation Required: Meeting minutes, survey results.
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  • 11
    Do you have a process to identify and respond to AI-related incidents? Focus Area: Handling failures, complaints, or unintended consequences of AI. Expectations: A structured process for logging, reporting, and addressing incidents. Documentation Required: Incident logs, response protocols.
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  • 12
    Do you assess and manage AI risks from suppliers and vendors? Focus Area: Managing risks introduced by external parties. Expectations: Due diligence, SLAs, and third-party assessments. Documentation Required: Vendor assessments, contract clauses.
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  • 13
    Are AI models tested and approved before deployment? Focus Area: Ensuring AI models meet requirements before use. Expectations: Model testing procedures with documented results and sign-off. Documentation Required: Validation reports, test results.
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  • 14
    Do you monitor AI system performance over time? Focus Area: Tracking reliability, drift, and output consistency. Expectations: Continuous or periodic monitoring and alerting mechanisms. Documentation Required: Monitoring dashboards, performance logs.
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  • 15
    Do staff receive training on responsible AI use and ethics? Focus Area: Building awareness and capability in ethical AI. Expectations: Role-appropriate training sessions with tracked completion. Documentation Required: Training materials, attendance logs.
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  • 16
    Are your AI practices aligned with relevant laws and standards? Focus Area: Legal and regulatory obligations around AI. Expectations: Compliance tracking, gap assessments, and policy alignment. Documentation Required: Legal register, compliance audit results
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  • 17
    Do you regularly review and improve your AI management practices? Focus Area: Learning from experience and updating your system. Expectations: Feedback loops, reviews, and documented changes. Documentation Required: Management review records, improvement logs.
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  • 18
    Do you consider the environmental impact of your AI operations? Focus Area: Energy use, carbon footprint, and sustainability. Expectations: Assessment processes and reduction strategies. Documentation Required: Sustainability reports, energy usage logs.
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  • 19
    Is personal data used in AI protected in line with privacy regulations? Focus Area: Protecting individual privacy and complying with laws like GDPR. Expectations: Privacy-by-design practices and consent processes. Documentation Required: Privacy impact assessments, data maps..
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  • 20
    Is each AI system properly documented from design to deployment? Focus Area: Maintaining complete records of AI development and use. Expectations: Architecture, functionality, and decision rationale. Documentation Required: Technical specifications, user guides.
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  • 21
    Do you have a process to manage changes made to AI systems? Focus Area: Controlling updates and avoiding disruption. Expectations: Approval workflows and testing before changes. Documentation Required: Change request logs, approval records.
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  • 22
    Are your AI systems protected from cyber threats and misuse? Focus Area: Securing data, models, and infrastructure. Expectations: Access control, encryption, and security monitoring. Documentation Required: Security policy, threat logs.
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  • 23
    Can humans override or monitor critical AI decisions? Focus Area: Ensuring accountability and preventing harm. Expectations: Defined roles and fallback mechanisms. Documentation Required: Oversight procedures, escalation steps.
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  • 24
    Do you manage AI systems through every phase from design to retirement? Focus Area: End-to-end management of AI solutions. Expectations: Policies covering development, deployment, and retirement. Documentation Required: Lifecycle policy, phase checklists.
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  • 25
    Can you explain how your AI model makes decisions? Focus Area: Transparency for users and auditors. Expectations: Tools or summaries that make model logic interpretable. Documentation Required: Explainability toolkit output, audit notes.
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  • 26
    Is access to AI systems restricted and monitored? Focus Area: Preventing unauthorized access or modification. Expectations: Role-based access and audit trails. Documentation Required: Access control lists, logs.
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  • 27
    Do you keep track of changes made to your AI models over time? Focus Area: Managing model versions and updates. Expectations: Version control systems and rollback capability. Documentation Required: Version history, Git logs.
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  • 28
    Do you obtain user consent when AI interacts with or uses personal data? Focus Area: Transparency and data subject rights. Expectations: Consent mechanisms and records of approvals. Documentation Required: Consent forms, logs.
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  • 29
    Do you have a formal process for retiring AI systems? Focus Area: Secure and responsible decommissioning. Expectations: Data disposal, documentation closure, and impact review. Documentation Required: Decommissioning checklist, closure reports.
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  • 30
    Are your AI practices reviewed and audited regularly? Focus Area: Independent checks for compliance and performance. Expectations: Internal or external audits and follow-up actions. Documentation Required: Audit reports, corrective actions.
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  • 31
    Do you communicate clearly with stakeholders about your AI systems? Focus Area: Sharing information and building trust. Expectations: Communication strategy and ongoing engagement. Examples of Documentation: Communication plans, stakeholder briefings.
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  • 32

    Scoring Methodology

    The readiness assessment of the current state of the AI Management System of your organization is determined based on your score.

    The option selected for each question carries a specific weight as defined in the table below:






















    Option Weightage
    Yes 100
    No 0
    Maybe 25
    Not Sure 0

    ComplyAI Hub uses its own algorithm to calculate the final score based on your responses.

    The final score reflects your organization's readiness for the ISO/IEC 42001:2023 certification audit.

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  • 33
    Fill in your details to receive pre assessment score.
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  • 34

    Thank You for Completing the ISO 42001 Pre-Assessment!

    Your answers have been successfully recorded. We will email your score and a basic overview to you shortly.

    Want to understand what these results mean for your business?

    Send us a mail to:


    • Go through your results in detail

    • Get practical recommendations for your ISO certification journey

    • Learn which gaps you need to fix before your audit

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