Iso 42001 Complete Guide

ISO/IEC 42001 Clause 7 Support
Complete Guidance & Best Practices

ISO/IEC 42001:2023 is the international standard for AI Management Systems (AIMS), and Clause 7 (Support) ensures that your organization provides all necessary support to make the AI management system effective.

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ISO/IEC 42001

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Clause 7: Support Overview

Clause 7 covers the resources, competencies, awareness, communication, and documentation needed to sustain an AI governance program. Through addressing these five support areas, organizations can maintain a well-functioning AIMS that meets compliance requirements and fosters trustworthy AI use.

Here we break down each part of Clause 7, providing you with guidance and implementation best practices for compliance.

Clause 7.1 – Resources: Providing Sufficient AI Resources

The organization must determine and provide the resources needed to establish, implement, maintain, and continually improve the AI management system. In other words, you need to allocate adequate resources to support your AI governance efforts from day one.

What this means: “Resources” in the context of an AI management system include human resources, technological resources, and financial resources.

Your AI governance program shouldn’t just exist on paper – it needs people, tools, and budget to run effectively. Annex A.4 of ISO 42001 provides control objectives for managing AI resources (with further guidance in Clause B.4). In practice, this breaks down into several categories of resources:

  • Human resources: Ensure you have enough skilled people across all stages of the AI lifecycle – from data scientists and AI developers to risk managers and compliance officers. Consider the mix of expertise needed (technical AI knowledge as well as governance and ethics expertise) and assign clear responsibilities for AI oversight.
  • Technical resources: Provide the necessary technology infrastructure and tools. This includes computing power (e.g. cloud services or hardware for model training), development and testing environments, data storage, monitoring and logging tools, and security measures to protect AI systems. High-quality data is also a crucial resource – make sure you have processes for obtaining, managing, and validating data used by AI, since data quality directly affects AI outcomes.
  • Financial resources: Allocate a sufficient budget for AI management activities. This covers ongoing costs like employee training programs, external consulting or auditors, acquisition of new tools or platforms, and continuous improvement efforts. There should also be financial planning for maintenance of AI systems and any updates needed as AI regulations evolve.

Implementation best practices

Start by conducting a resource assessment during your AI management system planning. Identify what people and skills are needed, what tools or platforms are required, and the expected costs. Engage top management early to secure commitment for these resources – leadership support is often key to unlocking budget and staffing. It’s also wise to plan for scalability: as your AI use grows, resource needs may increase, so build flexibility into your resource plans. Document your resource provisions and link them to your AI projects and risk assessments (for example, ensure high-risk AI applications have proportionally more oversight and resources assigned). If you find gaps – e.g. not enough personnel with AI expertise – consider training existing staff or hiring new talent or partners to fill those gaps. Investing in a solid support infrastructure up front can prevent much costlier governance failures later; a few months of focused preparation now can save 18–26 months of fixing governance that doesn’t work down the line.

Clause 7.2 – Competence: Ensuring Qualified Personnel

The organization must determine the necessary competence of people doing work under its control that affects AI performance, ensure those people are competent (through education, training, or experience), and take actions to acquire any necessary competence (and evaluate the effectiveness of those actions).
You also need to maintain documented information as evidence of competence (e.g. training records or certifications).

What this means: In essence, your team needs the right skills and knowledge to manage AI responsibly.

This goes beyond just having data scientists to build models – it includes expertise in AI ethics, risk management, data privacy, security, and regulatory compliance. ISO 42001’s guidance (Annex B.4.6) emphasizes a diverse skill set: technical skills (AI/ML concepts, data science), governance skills (risk assessment, audit, quality management), and legal/ethical knowledge (e.g. understanding AI regulations, bias mitigation, privacy laws). It’s unlikely that one person will master all these areas, so you need a well-rounded team or training program. In practice, ensuring competence involves a few key steps:

  • Identify required competencies: Define the roles involved in your AI management system (such as AI engineers, model validators, compliance managers, etc.) and list the competencies each role needs. Many organizations create a competency matrix mapping roles to required knowledge and skills. An AI developer might need expertise in machine learning techniques and secure coding practices, while a compliance officer might need knowledge of AI ethics guidelines and audit processes.
  • Train and fill gaps: Compare your team’s current skills against the required competencies. Where there are gaps, take action – this could include training programs, workshops, mentoring, certification courses, or even reassigning individuals to new roles to gain experience. Data scientists may need training on risk management and documentation, whereas compliance staff might need training on basic AI concepts. If certain expertise is missing entirely, you might hire new employees or bring in consultants. (Note: if using external experts, ensure knowledge transfer and remember that your organization still holds overall accountability for competence.)
  • Evaluate effectiveness: It’s not enough just to run a training session; you should verify that the training or hiring actions actually resulted in the desired competence. You can assess this through follow-up evaluations, tests, on-the-job assessments, or performance reviews focused on AI governance tasks. For example, after training, check if staff can effectively apply the AI risk assessment process or correctly follow documentation procedures.
  • Maintain evidence of competence: Keep records to demonstrate each person’s qualifications. This can include resumes/CVs, training attendance records, certificates from courses, or internal assessment results. During an audit, you’ll need to show that people managing AI have appropriate qualifications. Keeping this information organized (for instance, in a skills tracker or HR files) will make it easier to prove compliance.

Implementation best practices

Build a cross-functional AI governance team combining technical and compliance expertise.

Don’t rely on just technical experts or just compliance officers alone – you need both working together for effective AI governance.

Consider establishing an ongoing training program on AI ethics and governance for all relevant staff, not just a one-time event.
This could involve periodic workshops on new AI regulations, refresher courses on internal AI policies, and knowledge sharing sessions between data scientists and risk managers.
Many organizations integrate AI governance training into their existing training frameworks (for example, adding an AI module to annual compliance training). If your organization is already certified to other ISO standards like ISO 27001 (information security) or ISO 9001 (quality), leverage those existing competence management processes – you might extend your ISO 27001 staff training program to cover AI, for instance.

The goal is to create a culture where the people building or overseeing AI systems truly understand both the technology and the governance expectations.

Clause 7.3 – Awareness: Building Organizational AI Awareness

Persons doing work under the organization’s control (employees, contractors, etc.) must be aware of: (a) the AI policy (the high-level commitments defined in Clause 5.2), (b) their contribution to the effectiveness of the AI management system and the benefits of improved AI performance, and (c) the implications of not conforming to the AI management system requirements. Everyone involved should know the AI governance expectations and why they matter.

What this means: “Awareness” is about ensuring that people understand the importance of their actions in the context of AI governance, even if they are not AI specialists.

This is different from competence. For example, a software developer might be competent in coding an AI model, but are they aware of how their documentation or testing practices affect compliance and trust in AI? Awareness programs aim to instill a mindset of responsible AI. Every team member should know that the organization has an AI policy (e.g. principles for ethical AI use) and recognize their role in upholding it. They should also understand what could happen if they ignore the rules – for instance, deploying an AI model without proper validation could lead to failures or regulatory penalties. Key aspects of building awareness include:

  • Communicate the AI policy and principles: Make sure the organization’s AI Policy (from Clause 5.2) is not just a document on the shelf. Hold sessions to explain the policy’s key points to all employees. Discuss your company’s AI values (like transparency, fairness, safety) so that staff can internalize them. This could be part of onboarding for new hires and regular all-hands meetings or internal newsletters for existing staff.
  • Link roles to the AIMS objectives: Help each person see how their work contributes to AI management system effectiveness. For example, a data engineer ensuring data quality is directly contributing to better AI performance and compliance. Highlight positive outcomes (e.g. “improved AI performance benefits the company and customers”) as well as the risks of negligence (“if we don’t follow our AI process, we could deploy a biased model or face legal issues”). This can motivate employees by showing the real impact of their diligence.
  • Scenario-based training: Use practical examples or case studies to reinforce awareness. For instance, walk through a scenario where lack of awareness caused an AI failure or compliance breach (perhaps a famous AI incident from industry), and discuss what could have been done differently. Similarly, show success stories where awareness and adherence to the AI management system prevented problems. Interactive workshops, simulations, or even simple quizzes can make this engaging. The goal is to move beyond theoretical knowledge and let people see how their everyday actions (like following procedures for data labeling or model testing) make a difference.
  • Continuous reinforcement: Awareness isn’t a one-time thing. Establish ongoing communication to keep AI governance top-of-mind. You might run periodic refresher trainings or share short reminders/tips. Some organizations incorporate AI ethics into their internal communications regularly, akin to safety moment messages or security tips. Recognition programs can also help – for example, acknowledge teams or individuals who exemplify good practices in responsible AI. This encourages others to stay aware and follow suit.

Implementation best practices

Treat AI governance awareness similar to how organizations handle security awareness or safety culture. Make it pervasive. Ensure that even third parties or contractors who work on your AI projects are included in awareness initiatives, since Clause 7.3 applies to all persons under your control.

One effective approach is to integrate AI policy awareness into existing training modules – if employees already take annual compliance training (e.g. for code of conduct or data privacy), add a module about AI management system requirements. Test understanding by occasionally conducting surveys or spot-checks (for instance, ask a random employee what the AI policy is or what their responsibility is in AI projects – their ability to answer indicates the level of awareness).

Your organization should strive for a culture where everyone understands their role in ensuring AI is used responsibly and effectively, not just the AI specialists.

Clause 7.4 – Communication: Establishing Effective AI Communication

The organization must determine the internal and external communications relevant to the AI management system, including: what will be communicated, when to communicate, with whom (the audience), and how to communicate. Essentially, you need a communication plan for AI governance matters.

What this means: Clear communication is vital for transparency and coordinated AI management.

Internally, different stakeholders (like developers, management, legal, and HR) need to stay informed about AI-related policies, risks, and performance. Externally, you may need to communicate with customers, regulators, or partners about your AI system in an appropriate manner. Clause 7.4 ensures you’ve thought through all these aspects. Key considerations include:

  • Identify what needs to be communicated: Typical topics include AI policy changes, AI risk assessment results, updates on AI objectives, incidents or near-misses involving AI systems, performance metrics of AI (e.g. accuracy, error rates, fairness measures), and compliance or audit results. Not everything will be communicated to everyone – decide what information is relevant to each audience.
  • Set frequency and triggers (when): Establish regular communication cycles as well as criteria for ad-hoc communications. For example, you might have weekly or bi-weekly meetings for technical teams to discuss ongoing AI development and issues. Monthly or quarterly reports could go to senior management summarizing AI system performance, risk status, and any needs for resources or decisions. A quarterly or annual summary might be prepared for external stakeholders or even a public-facing responsible AI report for transparency. Additionally, define triggers for urgent communications – e.g. if a critical AI incident occurs (like a significant error or ethical concern), who must be notified immediately and how.
  • Define audiences (with whom): Map out all the interested parties. Internally, this includes AI developers, project managers, compliance officers, executives, and possibly all staff for general awareness. Externally, relevant parties might be customers (especially if AI is embedded in products or services they use), business partners, investors, and regulators or oversight bodies. Each group will have different information needs. For instance, technical teams might need detailed model performance logs, while executives prefer high-level risk overviews. Regulators might require specific documentation or notifications (such as results of bias tests or descriptions of how you manage AI risks).
  • Choose methods and channels (how): Decide the appropriate format and channel for each communication. Internal communications can use emails, dashboards, intranet portals, team meetings, or internal reports. For example, an AI risk committee might circulate minutes and action items via email; developers might use a collaboration tool to track issues. External communications could take the form of published reports, customer briefings, press releases, or disclosures on your website. Ensure the format is “usable” for the audience – technical data should be presented in a clear way for non-technical stakeholders if needed (e.g. using visual summaries for executives). The key point is getting the right information to the right people at the right time in an understandable format.

Implementation best practices

Develop a communication plan or matrix as part of your AI management system documentation. This plan should list the communication topic or document, the intended audience, frequency, format, responsible sender, and any approval required (for external communications, you might need legal approval, for instance). Make sure this plan aligns with your organization’s overall communication policies and doesn’t conflict with confidentiality or security requirements. It can be helpful to integrate AI communications into existing meetings and reports – for example, include an “AI governance” segment in your regular IT or risk management meetings. For external stakeholder communication, consider publishing an AI transparency or responsibility statement on your website, which can fulfill part of this requirement for public communication. Many organizations preparing for AI regulations (like the EU AI Act) are starting to produce transparency reports about their AI systems; such reports can serve as external communications demonstrating compliance and building trust. Finally, review and update your communication approaches regularly. As your AI program matures or as external interest grows, you may find new communication needs (for instance, if regulators introduce new disclosure obligations, incorporate those into your plan).

Clause 7.5 – Documented Information: Managing AI Documentation

Clause 7.5 has three subparts about documented information:

  • 7.5.1 (General): The AI management system must include documented information required by the standard, and any other documented information the organization deems necessary for effectiveness. (This is basically identifying all the documents and records you need to manage your AIMS.)
  • 7.5.2 (Creating and updating): When creating or updating documents, ensure proper identification and description (e.g. title, date, author, version), use appropriate formats and media (e.g. language, file type, diagrams, whether it’s electronic or paper), and subject documents to review and approval to ensure they are suitable and adequate.
  • 7.5.3 (Control of documents): Documents required by the AI management system (and the standard) must be controlled. This means making sure documents are available where and when needed, protected from loss of confidentiality, improper use, or loss of integrity. Specific control activities include: distribution, access, retrieval and use of documents; storage and preservation (including keeping them legible); controlling changes (e.g. version control); retention and disposition (how long to keep documents and how to dispose of them). Also, any external documents that are necessary for planning and operating the AIMS (for example, external codes of practice, regulatory guidelines, or vendor manuals) should be identified and controlled as well. In short, treat your AI-related documents with a formal document management process.

What this means: Effective documentation is the backbone of any management system audit, and AI is no exception.

You need to maintain a set of documents (like policies, procedures, plans) and records (evidence of activities and results) to both guide your team and demonstrate compliance. The extent of documentation can vary depending on your organization’s size, complexity, and personnel competence – a larger or more complex AI operation will likely require more detailed documentation, whereas a smaller team with expert staff might keep things simpler (the standard acknowledges this flexibility in a note). However, even agile AI startups need certain key documents to meet Clause 7.5.

Let’s break down how to approach documented information:

Clause 7.5.1 – Required and necessary documents

First, identify all documents that ISO 42001 explicitly or implicitly requires. These typically include:

  • AI Policy (from Clause 5) – a formal document stating your organization’s AI principles and commitments.
  • Scope and context documentation (from Clause 4) – describing the boundaries of your AIMS and relevant internal/external factors.
  • Risk assessment and treatment documents (from Clause 6) – e.g. AI risk assessment reports, AI impact assessments, and records of how you decided on risk mitigations and controls (including how you addressed Annex A controls).
  • Support and competence records (from Clause 7) – training records, competency evaluations, awareness training logs, communication plans, resource allocation plans. For example, you should document that certain employees attended an “AI ethics and compliance training” on a given date (this serves as evidence for both competence and awareness).
  • Operational documents (from Clause 8) – procedures for data management, model development, validation, deployment, monitoring, incident response plans for AI, etc. These could be in the form of process documents or runbooks guiding the teams.
  • Performance evaluation records (from Clause 9) – internal audit reports of the AIMS, AI performance metrics tracking, management review meeting minutes (where leadership reviews the AI management system), and stakeholder feedback or complaints regarding AI.
  • Improvement records (from Clause 10) – records of nonconformities and corrective actions related to AI, and any continual improvement initiatives or lessons learned.
  • Annex A control documents – if you have specific controls in place (e.g. data quality checks, bias testing procedures, model documentation requirements), there should be documents or records for those. For instance, maintaining model cards, data sheets, or audit logs for AI models can be considered part of documented information. These show details about your AI systems (like their intended purpose, training data, performance, limitations) and are very useful for audits and accountability.

Additionally, you might decide to create other supporting documents that, while not explicitly demanded by the standard, help your AIMS run effectively.

Examples could be an “AI governance handbook” for staff, checklists for AI project reviews, or a register of all AI systems in the organization. Each organization can determine what is necessary.

Just ensure that for every critical aspect of your AI management, there is some documentation or record to back it up.

Clause 7.5.2 – Document creation and update

When you create or update these documents, follow a controlled process:

  • Identification and description: Give each document a clear title, a unique identifier (like a document number or version number), author name, and date. This metadata ensures anyone can recognize what the document is and whether they have the latest version.
  • Format and media: Choose appropriate formats. For most, electronic documents (Word, PDF, etc.) are practical, but ensure consistency in language (e.g. documents should be in the working language of your organization or translated as needed). If you use specialized tools (like a governance software or wiki), ensure everyone has access to view those. If any documents are physical, keep them clean and legible; if electronic, consider using common file formats that won’t become obsolete quickly. Diagrams, flowcharts, or tables can be used in documents to improve clarity (for instance, a flowchart showing the AI model development process can be part of your procedure document).
  • Review and approval: Establish a workflow where drafts of important documents are reviewed and approved by authorized personnel before being finalized. For example, an AI Policy might be prepared by the AI governance team, reviewed by the head of compliance and the CTO, and approved by the CEO. A technical procedure might be reviewed by a senior AI engineer and the risk manager. The idea is to ensure documents are accurate, adequate, and appropriate. Keep records of approvals (even if it’s just an e-signature or an email confirmation) – this shows auditors that documents were officially authorized. Every time a document is updated, the updated version should also go through necessary reviews/approvals if the changes are significant.

Clause 7.5.3 – Document control

After documents are created, you need to manage them through their life cycle:

  • Availability and access: Store documents in a location where they can be easily accessed by those who need them. Many organizations use an internal document management system or SharePoint/Drive folders with controlled access. For instance, make sure all developers can easily pull up the latest AI development guidelines, and all employees can view the AI Policy. It’s useful to maintain a master list or index of AIMS documents so you can quickly find where everything is.
  • Protection: Set permissions so that only authorized individuals can edit or delete critical documents. Protect sensitive documents (some AI documentation might contain proprietary algorithms or personal data) – this could mean access control, encryption, or anonymization where appropriate. Also guard against loss – ensure you have backups of important documentation. If using a cloud service, verify that it has version history or backup enabled.
  • Control of changes: Implement version control. Every document should have a version number or date of revision. When a change is made, update the version and keep a record of what changed (a brief change log). This could be as simple as a table at the end of the document listing revision dates and summaries, or using a version control system. Make sure people always know where to find the current version – for example, mark older copies as “obsolete” or remove access to avoid confusion. It’s also a good practice to periodically review documents (say annually) to see if they need updates due to new AI developments or regulations.
  • Storage, retention, and disposition: Decide how long each type of document and record should be kept. Some AI-related records might be needed for a certain number of years (check any legal requirements – e.g., if your AI system decisions could be questioned legally, you’d want records retained for a relevant period). Define retention periods for different document categories (policy documents might be kept for the life of the system + several years, training records maybe a few years, etc.). Ensure documents remain legible and accessible throughout their retention (migrate files to new formats if needed, replace faded printouts, etc.). When disposing of documents, do it securely – shred paper, permanently delete or archive electronic files – especially if they contain sensitive information. And document that you disposed of them as per policy.
  • External documents: Keep track of external sources that you rely on. For example, if you reference the EU AI Act guidelines or an ISO best-practice guide as part of your AIMS, treat those as controlled external documents – note the version you’re referring to and have a way to know when they get updated. This prevents a scenario where your team follows outdated guidance because an external reference changed unbeknownst to you.

Implementation best practices

Many organizations choose to adapt their existing document management practices from other compliance domains (like ISO 27001 or ISO 9001) to cover AI documentation. If you already have a document control policy, extend it to include AI-specific docs. Train your staff on the importance of documentation – sometimes technical teams underestimate this, but remind them that for complex AI (“black box”) systems, documentation is often the primary evidence to demonstrate control and accountability. To reduce burden, use templates for common documents (e.g. a standard template for reporting an AI risk assessment, or a template for model fact sheets). This ensures consistency and makes it easier to fill in required information. Also, consider using software tools or wikis to manage documentation collaboratively, which can enforce version control automatically and make retrieval easier (with search functionality). Regularly audit your documentation: check that documents are up to date, approvals are in place, and people are indeed following them. Good documentation not only helps with compliance but also improves transparency and trust in your AI systems, both internally and with external stakeholders.

Conclusion: Sustaining Your AI Governance with Clause 7 Support

Clause 7 of ISO/IEC 42001 is all about laying the supportive groundwork for successful AI governance. Through ensuring you have sufficient resources, qualified and aware people, clear communication, and robust documentation, you create the conditions for your AI management system to thrive. These support functions might not grab headlines like an advanced AI model, but they are critical to avoid pitfalls. Organizations that invest the time and effort in Clause 7’s areas often find that strong support prevents a lot of problems – it’s much easier to manage AI risks and comply with regulations when the right people, processes, and information are in place from the start. In fact, one analysis noted that a 4–7 month investment in support infrastructure can prevent 18–26 months of remediation work fixing a broken AI governance program.

As you implement Clause 7, remember that it can integrate with what you may already be doing. If you have other ISO management systems (e.g., information security or quality management), leverage those existing structures for training, awareness, and document control – extend them to encompass AI-specific needs. Encourage cross-functional collaboration: AI governance works best when tech teams and compliance teams learn from each other and work towards common objectives.

With the support framework of Clause 7 solidly in place, your organization will be well-prepared to tackle the operational controls in Clause 8 and the continuous improvement cycles in later clauses.

In summary, Clause 7 (Support) ensures your AI management system is not just a plan on paper, but a living, effective program backed by the necessary people, communication, and information. 

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