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ISO 42001:2024 is an international standard that specifies the requirements for establishing, implementing, maintaining, and continually improving an AI management system (AIMS) within an organization.
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For governance and operational consistency, the organization must establish and maintain formal records detailing its data preparation protocols. This documentation must specify the rationale and criteria guiding the selection of data conditioning procedures. Furthermore, it is mandatory to document the specific methodologies and techniques that will be employed to execute these chosen activities, ensuring a transparent and auditable process.




Comprehensive documentation is required for every phase of an AI system's design and development. It is essential that these records provide a clear and auditable trail back to the organization's foundational objectives. This traceability must also extend to all formally documented requirements and the established criteria for system specifications.




To ensure the integrity of documented information, the organization is required to implement a comprehensive governance framework for its creation and revision. This framework must establish clear controls, including standardized identification and formatting protocols that specify elements such as titles, authorship, language, and media type (e.g., electronic or physical). Furthermore, all documented information is subject to a mandatory review and approval cycle to formally validate its adequacy and suitability for the intended purpose prior to its official use.




Before an artificial intelligence system is introduced into a live operational environment, the organization must conduct a pre-deployment validation to confirm that all applicable prerequisites and security criteria have been satisfied. A central element of this verification is the creation of a formalized deployment strategy. This plan must be officially documented and serve as the authoritative guide for a controlled and secure transition of the system into production use.








The organization is mandated to create and uphold a structured governance framework for the accountable application of artificial intelligence. This framework must be formally documented, detailing the established protocols and principles that guide the ethical and secure deployment of AI technologies. All related procedures shall be officially recorded and consistently maintained to provide an authoritative reference for responsible oversight and to ensure alignment with organizational policies.




The organization is required to implement and maintain strict control over all documented information supporting the AI management system. This mandate extends to any externally sourced documentation identified as critical for system planning and operation. The primary goals of this control are to ensure information is both accessible and appropriate for its intended use, while also protecting it from confidentiality breaches, improper handling, or integrity loss. Control measures must cover the entire information lifecycle, including its distribution, access, preservation, versioning, and final disposition.








The organization holds the responsibility to formally define and document a policy that specifies the exact stages within an AI system's lifecycle where event logging must be activated. As a foundational control, it is mandatory that logging mechanisms remain active throughout the system's entire operational deployment. This ensures a comprehensive record of activities is captured whenever the AI system is in an active state.




Organizations must implement a structured process for the systematic evaluation of AI-related risks, adhering to a formally defined methodology. This evaluation must occur at predetermined, regular intervals to ensure ongoing oversight. Additionally, any proposed or implemented significant changes to AI systems or their operational context must trigger an immediate reassessment to address emerging threats. A crucial component of this framework is maintaining comprehensive records of all assessment outcomes, ensuring they are preserved as documented evidence for compliance and review purposes.












The organization is obligated to codify and document its criteria for data quality. Furthermore, it must ensure that all data inputs, whether for training, testing, or operational use within an AI system, consistently conform to these established standards throughout the system's entire lifecycle.








The organization is required to implement controls ensuring that artificial intelligence systems are operated strictly within their designated scope. The application of any AI system must be confined to the intended purposes that are formally defined and documented by the provider. Any use of the system outside these specified operational parameters is prohibited and must be actively prevented through established governance measures.












To ensure the principled application of artificial intelligence, the organization shall implement a comprehensive governance framework. A primary objective of this framework must be the integration of identified customer requirements and expectations into the design, development, and operational use of all AI systems. This approach guarantees that AI solutions are not only technologically sound but also ethically and functionally aligned with end-user interests.








Senior leadership within the organization is tasked with instituting a formal AI policy that aligns with its core purpose and provides a framework for establishing AI objectives. This policy must be documented and communicated, affirming a commitment to fulfilling all applicable requirements and perpetually improving the AI management system. It shall be made accessible to relevant parties as appropriate and must reference other internal policies. Specific guidance and controls for implementation are detailed in supplementary annexes.












The organization must establish and maintain a formal framework for verifying and validating its artificial intelligence systems. This framework requires documented procedures for all assurance activities. Furthermore, the organization is obligated to specify explicit standards governing how and when these verification and validation protocols are to be implemented and executed throughout the AI system's lifecycle.




In the event of a nonconformity, the organization is required to initiate a formal response. This includes immediate measures to contain and rectify the issue while mitigating its consequences. A systematic review must be conducted to identify the root cause(s) and prevent recurrence by assessing whether similar vulnerabilities exist. Following this analysis, the organization must implement corrective actions that are proportionate to the nonconformity's effects. The effectiveness of these remedial measures must be validated, and the AI management system updated if necessary. Documented evidence of the nonconformity, the actions taken, and their outcomes is mandatory.




The organization shall ensure its executive team performs periodic evaluations of the AI management system at planned intervals. This governance activity is essential for confirming the system's sustained suitability, adequacy, and effectiveness. Such reviews must validate that the framework remains aligned with strategic objectives and continues to be fit for its intended purpose, thereby ensuring it consistently meets organizational requirements.




The organization is obligated to establish a documented methodology for maintaining data provenance. This formal process must provide a comprehensive audit trail that traces the origin, history, and transformations of all data leveraged within its artificial intelligence systems. Such traceability must be upheld consistently across the entire operational lifespan of both the data and the associated AI model, from initial creation through to final disposition.








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Sets the overall compliance standard or regulation your organization needs to follow.
Break down the framework into specific obligations that must be met.
Concrete actions and activities your team carries out to satisfy each requirement.
Documented rules and practices that are created and maintained as a result of completing tasks.
