ISO/IEC 42001 AI Security Implementation Guide

Artificial intelligence (AI) is here to stay. AI tools are already at the center of customer experiences, critical business processes, and even national infrastructure. Unfortunately,  AI also introduces a new attack surface. Threats like data poisoning, prompt injection, model theft, and supply chain exposures are serious risks, leading regulators to push for enforcement.

This is where ISO/IEC 42001:2023 comes in. It’s the world’s first management system standard for AI. ISO 42001 is a structured framework handling AI security, governance, and risk management, and it’s essential for organizations seeking to deploy AI tools and systems responsibly.

Modeled on the internationally-recognized ISO 27001 standard for information security, ISO 42001 establishes an AI Management System (AIMS) that covers data sourcing, model training, evaluation, deployment, monitoring, and retirement.

For boards, CISOs, and AI leaders, this standard is more than a checklist of requirements, it’s the baseline to satisfy regulators (EU AI Act, DORA, NIS2), enterprise buyers, and risk committees. Organizations that ignore ISO 42001 risk falling behind both on compliance and customer trust and may lose out on deals as a result.

At BD Emerson, we help clients turn ISO 42001 into a competitive advantage. This guide explains the threats, controls, audits, and artifacts that matter most and shows how to get audit-ready in weeks, not years.

In This Guide 

Like our ISO 27001 Comprehensive Guide, this article provides an in-depth look into ISO 42001 requirements and implementation procedures, covering the following topics:

1. ISO 42001 overview: scope, who needs it, and how it differs from ISO 27001
2. Easy-to-follow primer on AI threats: poisoning, inference, jailbreaks, supply chain risks
3. An overview of the core principles of ISO 42001 
4. Control-by-control walkthrough of ISO 42001 clauses, mapped to evidence requirements
5. Explanation of how 42001 supports EU AI Act Article 15, DORA, NIS2, and GDPR
6. Internal audit program design (Clause 9.2) with Corrective and Preventative Action process (CAPA) 
7. List of Best Practices & Tools: open-source and commercial stacks for AI testing and guardrails
8. List of KPIs, KRIs, and templates for practical implementation
9. How BD Emerson’s consulting accelerates your path to AI assurance

After reading this guide, you will have a detailed understanding of ISO 42001 requirements combined with actionable steps for starting your journey to implementing a highly effective AI Management System.

What is ISO/IEC 42001? 

ISO/IEC 42001’s purpose is to provide a structured approach that organizations can use to govern AI risks across the lifecycle, from data sourcing and model development to deployment, monitoring, and decommissioning.

ISO 42001 mirrors the Annex SL structure used in ISO 27001, so compliance teams should be familiar with its setup. The major difference between the two is that ISO 27001 secures information systems, while ISO 42001 secures AI systems, with controls for:

• Data governance (source quality, lineage, privacy)
• Model development (secure practices, adversarial testing, explainability)
• Operations (runtime monitoring, incident response, change management)
• Governance (roles, oversight, ethics, transparency)

Who Needs ISO/IEC 42001?

ISO/IEC states that ISO 42001 is intended for “organizations of any size involved in developing, providing, or using AI-based products or services,” and is, “applicable across all industries and relevant for public sector agencies as well as companies or non-profits.” 

Here are more specific examples of the types of companies that are highly encouraged to implement the ISO 42001 framework. 

Builders: companies training or fine-tuning models (LLMs, CV, NLP) that need assurance for enterprise buyers.

Consumers: organizations deploying third-party AI that governs data, prompts, and outputs.

Vendors in finance, health, or critical infrastructure who are preparing for EU AI Act Article 15, DORA ICT risk requirements, and NIS2 audits/certification.

The bottom line is: if AI touches your business model or revenue stream, ISO 42001 security is your baseline.

Learn more: BD Emerson’s AI Security Consulting Services

Our team of security consultants will guide your team through the  implementation of ethical practices surrounding AI use and the creation of strategies for aligning critical AI safeguards with your business goals. Carefully following the ISO/IEC 42001 framework, we help you navigate the creation of an AIMS, achieve certification, and maintain compliance.

• ISO 42001 Consulting
• AI Governance Consulting
• EU AI Act Consulting

Schedule a time to talk with us!

Top AI Threat Families 

AI systems face a distinct set of threats compared to traditional IT infrastructure. The ISO 42001 security framework zeroes in on and addresses several risks, including:

• Data poisoning & contamination – Malicious or low-quality data that introduces backdoors or bias and leads the model to make incorrect predictions/false decisions.
• Adversarial evasion – Under the umbrella of Input Manipulation Attacks, these are tactics where an attacker crafts inputs (images, tokens, prompts) that purposely mislead the model and can flip model predictions.
• Model inversion & membership inference – Extracting secrets or confirming whether sensitive records are in training data through reverse engineering the model. 
• Model extraction & theft – Attacker queries the model to steal weights or replicate decision boundaries by accessing the model’s parameters.
• Prompt injection & tool abuse – Attacker forces LLMs/agents to exfiltrate secrets, execute unsafe tools, or override policies with indirect or direct prompt injections.

• Supply chain exposure – Attacker targets insecure model hubs, open-source libraries, and leaky datasets.
• Shadow AI & misconfiguration – The unapproved use of AI by employees with no organizational monitoring or governance by an IT department.

Pro Tip: Map each of these threats to a control, artifact, and KPI. For example, prompt injection maps to guardrails + red-team reports; poisoning maps to data lineage controls + quality gates.

Other Resources:

• Our NIST Compliance Consulting Services
• OWASP ML Top 10
• MITRE ATLAS

Core Principles of ISO 42001

The published ISO 42001 standard touches on several core themes regarding ethical AI usage.

• Leadership: Company executives should demonstrate leadership and commitment with regards to the AIMS and integrate policies and objectives that are consistent with the organization’s strategic roadmap..  
• Planning: Identify and evaluate the risks and opportunities associated with AI use and create a plan to address them.  
• Support: Provide your team with accessible resources and support for the AIMS, including security awareness training.
• Operation: Outline processes and procedures for the development, deployment, and maintenance of AI systems.  
• Performance evaluation: Measure and analyze the performance of AI systems and address issues when necessary.  

• Continual improvement: Continually enhance the AIMS,  ensuring that it remains relevant and effective.  

Regulatory Mapping

ISO 42001 doesn’t exist in a vacuum. Though it isn’t designed to fulfill specific legal requirements and regulations, certain ISO 42001 requirements do align with the following major regulations:

• EU AI Act Article 15: Requires accuracy, robustness, and cybersecurity controls. ISO 42001 Clauses 8–10 cover post-market monitoring, incident reporting, and system robustness.
• DORA (Digital Operational Resilience Act): Financial services ICT risk, third-party management, and testing map to ISO 42001 Clauses 4–9.
• NIS2: Cyber resilience, risk management, and incident reporting align with Clauses 5–10.
• GDPR (General Data Protection Regulation): ISO 42001 Clause 8.4 requires an AI System Impact Assessment (AISIA), parallel to DPIAs under GDPR.

How we can help: Our security experts will guide you through the adoption of a single AIMS build that satisfies ISO 42001, EU AI Act, DORA, and NIS2, and saves you from duplicating effort and investment.

Have questions? We’ll explain complex requirements in plain English. Talk to us today.

Implementation by Clause 

This section will provide a detailed breakdown of the 10 clauses of ISO 42001, explaining the requirements of each and how you should address them:

Clauses 1-3

Clauses 1-3 aren’t prescriptive, but they provide definitions and context for the rest of the ISO 42001 clauses. Clause 1 outlines the scope of ISO 42001, Clause 2 provides context and normative references, and Clause 3 provides definitions for technical terms used throughout the framework. These introductory clauses make it easier to understand the rest of the standard.

Clause 4: Context of the Organization

An effective AIMS begins with understanding an organization's external (legal, ethical, cultural and technological factors) and internal contexts (governance, contractual obligations, organizational objectives). As you begin creating an AIMS document, determine which stakeholders are impacted by your AI systems and aggregate their expectations. Define the scope of your AIMS by listing the AI systems, functions,  and processes it covers. A clear scope avoids misalignment later when performing audits or answering regulators. ​

TIPS: 

• Map relevant laws (EU AI Act, GDPR, DORA, NIS2) and industry guidelines to your scope. For example, if you operate in financial services, DORA and NIS2 will drive resilience and reporting controls; if you use personal data, GDPR and the EU AI Act will impact data governance. ​

• ​Identify both downstream (customers, users) and upstream (developers, suppliers) stakeholders. Consider fairness, human rights and environmental impacts when defining their expectations.

Clause 5: Leadership and AI Policy

Top management must demonstrate their commitment by adopting a structured AI policy and ensuring that roles and responsibilities are well-understood and assigned. ​

• ​The AI policy should align with organizational values and other management‑system policies (e.g., security, privacy, quality). ​

• Specify high‑level principles for fairness, transparency, safety, privacy, and security. ​

• Review the AI policy at planned intervals to ensure it remains effective.​

• For AI governance consulting, we recommend establishing an AI Ethics Board or steering committee that includes executives, AI practitioners, legal counsel and risk officers. ​

• This board should oversee the policy, approve high‑risk AI projects, and evaluate emerging regulations.

Clause 6: Planning Risk Management & Impact Assessment

Clause 6 requires your organization to identify risks and opportunities associated with AI. This includes distinguishing acceptable from unacceptable risks, performing AI risk assessments and planning actions. 

Clause  6.1.4 requires an AI system impact assessment, where you define a process for assessing potential consequences that AI systems may have on individuals, groups and societies. The assessment should consider the specific technical and societal context and the jurisdictions where the AI system is deployed. It’s crucial for your team to document the results and factor them into your risk treatment decisions.​

For reference: ISO/IEC 42005:2025 provides in‑depth guidance on AI system impact assessments. ​

The process steps for crafting an impact assessment include: ​

1. Document the scope​
2. Collect system information​
3. Establish thresholds for sensitive uses​
4. Assess impacts and record the results​
5. Integrate impact assessment results into your AI risk management and mitigation measures​

TIPS: 

• Develop a standard impact assessment template (inspired by ISO 42005) and require its use for every AI project. ​

• Use a compliance automation platform to route assessments to reviewers and track approvals.​

• When planning risk treatment, consider controls in Annex  A (e.g., fairness testing, privacy preservation, adversarial resilience) and map them to regulatory requirements (EU AI Act risk tiers, DORA resilience measures, NIS2 reporting obligations).

Clause 7: Support

This clause explains how to support your AIMS: ​

• Provide adequate resources​
• Ensure staff competence ​
• Raise awareness of the AI policy​
• Document procedures and maintain version control to preserve integrity (7.5). ​
• Maintain transparent internal and external communication channels for reporting AI concerns.​

For an effective AIMS, competence is critical. Make sure your organization provides training on AI ethics, security, privacy and  other relevant laws to engineers, data scientists and leadership. It is also essential to encourage cross‑disciplinary learning between legal, compliance and technical teams.

Clause 8: Operation

Operational planning and control (8.1) requires organizations to implement controls to meet AIMS requirements and implement a risk treatment plan. The risk treatment plan will explain how your organization manages design, development, procurement, deployment, operation, monitoring, changes and decommissioning of AI systems. ​

Clause  (8.4) requires organizations to perform AI system impact assessments at planned intervals or whenever  significant changes occur.​

TIPS:

• Use a life‑cycle approach: define gates (e.g., concept, design, development, validation, deployment, monitoring, retirement) and require evidence that controls have been applied at each gate.​

• Integrate change management to ensure updates do not introduce new risks. ​

• Monitor AI performance, fairness, and security in production and trigger retraining or decommissioning where necessary. Tools like model‑ops platforms can automate continuous monitoring, but still need human oversight.

Clause 9: Performance Evaluation

Clause 9 requires organizations to engage in the systematic monitoring, measurement, analysis, and evaluation of their AI systems to verify that these systems are operating within the ethical, legal, and operational parameters set forth by the organisation and relevant standards.

There are three core elements of effective AIMS performance evaluation:

• Ongoing monitoring and measurement: Which components of the AIMS need to be measured? How will the measurement methods used be established?
• Comprehensive Internal Audit: Does the AIMS conform to ISO 42001 and the organizations’ contractual obligations?
• Management Review: Have top executives and management reviewed the AIMS for relevance and effectiveness?

Clause 10: Improvement

Clause 10 outlines the requirements for continuous improvement of the AI Management System. It emphasizes the necessity for organizations to pinpoint areas and opportunities for improvement and to enact protocols that ensure the AIMS meets current and future requirement benchmarks. To properly align with Clause 10, teams must regularly evaluate the performance of the AI Management System and follow these steps: 

1. Establish a baseline of current performance
2. Establish measurable improvement objectives
3. Enact changes and improvements
4. Monitor and analyze the changes’ effects on the AI management system

ISO 42001  + Other Regulations and Laws

EU AI Act Article 15

Article 15 of the EU AI Act mandates that high-risk AI systems must be designed to be “accurate, robust, and secure,” and that they should perform consistently throughout their lifecycle. 

ISO 42001 focuses on three main themes of requirements included in the EU AI Act, including data governance and quality, transparency and human oversight, and ethical practices. 

ISO 42001 provides organizations with a structured framework for organizing and conducting the assessments required by the EU AI Act and helps teams identify potential risks then establish mitigation measures in order to address them. More specifically, The EU AI Act’s requirements including lifecycle controls, postmarket monitoring, and incident reporting align with 42001 Clauses 8–10.

DORA

The Digital Operational Resilience Act (DORA) is an EU regulation that provides a comprehensive risk and security framework to ensure that banks and other financial institutions can safely rely on digital service providers to maintain market stability. To align with DORA requirements, organizations must create and adopt  an Information and Communication Technologies (ICT) risk management framework. 

There are three ISO 42001 clauses in particular that support DORA’s operational resilience mandate:

• Clause 4.1 Understanding context: Identifies factors impacting AI systems.
• Clause 6.1 Addressing risks and opportunities: Supports AI risk management.
• Clause 8.4 AI system impact assessment: Aligns with continuous ICT risk monitoring.

In summary, DORA’s governance, ICT risk, testing, incident and third-party management requirements map to ISO 42001 Clauses 4–9.

NIS2

NIS2 refers to the European Union’s updated cybersecurity legislation that aims to strengthen cybersecurity infrastructure across the EU. Though it refers mainly to businesses within the EU, it also applies to businesses outside and not based in the EU that provide essential services to the European economy. 

The three core principles are: 

• Business continuity
• Corporate accountability
• Effective incident reporting

NIS 2’s  security requirements support these principles, and the directive outlines 10 minimum security requirements, some of which may be satisfied by compliance with ISO 42001. Additionally, if you operate within the EU or service clients in Europe and are in a NIS 2-regulated sector, frameworks like ISO 42001 and ISO 27001 can serve as key starting points for managing and establishing compliance. NIS2 requirements like risk management, reporting, secure development and operations align with 42001 Clauses 5–10.

GDPR and Beyond

Since May 25, 2018, GDPR has mandated stringent data protection measures for all entities handling the personal data of individuals within the European Union, or any organization that handles the data of EU citizens. The regulation underscores principles of transparency in data collection and usage, securing personal information, and holding organizations accountable for data privacy.

Though comprehensive, GDPR doesn’t necessarily address technical AI risks, which are regulated under the EU AI Act. ISO 42001 helps bridge the gap between the two by requiring the establishment of risk reviews, process discipline, and skill assessments for all AI systems–including those that don’t have access to personal data protected by GDPR.

The Data Protection Impact Assessment (DPIA) and AI System Impact Assessment (AISIA) (Clause 8.4) support purpose limitation, minimization, explainability, and rights – all essential to GDPR and Privacy compliance.

ISO 42001 Annex A Controls

ISO 42001 has four Annexes (A-D) that outline the objectives and principles that organizations should implement with their AIMS. We will focus on Annex A because it offers a comprehensive list of controls for responsible AI development, deployment, use, monitoring, and ongoing improvement.

Annex  A lists 42 control objectives organized into 9 topics (A.2–A.10). Below, we translate each control objective into concrete actions, grouping similar controls. Refer to ISO 42001’s Annex  B for full control implementation guidance.​

A.2 Policies Related to AI​

A.2.2 Alignment with other organizational policies: Determine how existing policies (information security, privacy, ethics) apply to AI and where new AI‑specific policies are needed. Align objectives, terminology, and oversight to prevent conflicting guidance.​

A.2.3 AI policy: Document a formal AI policy approved by top management. Include principles for responsible AI, acceptable use of AI technologies, risk tolerance and escalation procedures. Publish the policy internally and, where appropriate, externally to build trust.​

A.2.4 Review of the AI policy: Establish a review schedule (annually or after significant regulatory change) to ensure the policy remains effective. Involve legal and technical stakeholders and update the policy based on internal audit findings and technological advances.

A.3 Internal Organization

A.3.2 Reporting of concerns: Create channels for staff and external parties to report concerns about AI systems (e.g., fairness issues, safety incidents, security vulnerabilities). Provide anonymity options and protect whistle‑blowers. Integrate this process with incident management and risk treatment.​

A.3.3 AI roles and responsibilities: Define and allocate roles such as AI owner, model developer, data steward, AI ethics officer, privacy engineer, security engineer and quality assurance. Document responsibilities in job descriptions and ensure segregation of duties to avoid conflicts of interest. Provide authority and resources for these roles to perform their duties effectively.

A.4 Resources for AI Systems

Identify and document resources needed across the AI life cycle:​

A.4.2 Resource documentation: Maintain an inventory of AI system components, including hardware, software, data sets, third‑party libraries, and human resources. Track versions and dependencies.​

A.4.3 Data resources: Record provenance, quality and licensing of datasets. Ensure data meet ethical and legal requirements (consent, purpose limitation). Define retention and disposal criteria.​

A.4.4 Tooling resources: Catalogue development tools, machine‑learning frameworks, evaluation tools and monitoring platforms. Ensure tools comply with security and privacy requirements. Apply secure configuration baselines.​

A.4.5 System and computing resources: Document the infrastructure used to train and deploy AI models (on‑prem, cloud). Implement capacity management, redundancy and resilience measures in line with DORA and NIS2.​

A.4.6 Human resources: List competencies needed (data science, ethics, privacy, security, domain expertise). Provide training and ensure adequate staffing for development, monitoring and maintenance.

A.5 Assessing Impacts of AI Systems

Implement a structured AI system impact assessment process (see clause 6.1.4). The objective is to identify and evaluate potential ​consequences of AI deployment on individuals and societies. ​

1. Define scope: Describe the AI system, its purpose, data used and stakeholders impacted. Consider planned and foreseeable uses.​
2. Identify potential impacts: Evaluate impacts on privacy, safety, fairness, autonomy, human rights, environmental, and social factors. Use checklists from ISO 42005 and relevant regulatory guidance (EU AI Act high‑risk requirements).​
3. Assess likelihood and severity: Rate each impact’s severity and likelihood. Then, determine whether the risk is acceptable or requires mitigation.​
4. Determine mitigation measures: Select controls (e.g., bias mitigation, explainability techniques, privacy‑preserving methods) and assign owners.​
5. Record and report: Document the assessment, decisions and residual risks. Make the result available to relevant interested parties where appropriate.​
6. Review and update: Re‑assess when significant changes occur or at planned intervals.

A.6 Data for AI Systems

• Data acquisition and authorization: Organizations must collect and use data lawfully, obtaining necessary consents and ensuring that data subjects understand how their data will be used. Respect data minimization principles and ensure data sets are representative to reduce bias.​
• Data quality assurance: Implement processes to validate, clean, label, and annotate data. Make sure to maintain metadata documenting data provenance, version, and quality metrics. Use synthetic data cautiously and test its impact on model performance.​
• Data security and privacy: Implement encryption, access controls and anonymization/pseudonymization. Conduct privacy impact assessments and map controls to GDPR and the AI Act’s risk management requirements. When transferring data across jurisdictions, employ appropriate safeguards (e.g., SCCs).​

A.7 AI System Development

• Objectives for responsible development: Define measurable objectives (e.g., fairness thresholds, interpretability targets, acceptable error rates). Then, integrate them into development plans and track progress.​
• Design and architecture: Embed security and privacy by design and choose models appropriate for the use case, taking into account explainability requirements. Document training data, model configurations, and hyperparameters.​
• Verification and validation: Develop and execute test plans covering functionality, robustness, fairness, security and compliance requirements. Perform adversarial testing and red‑team exercises to uncover vulnerabilities. Lastly, validate that the AI system meets its intended purpose and does not result in unintended consequences.​
• Documentation: It is imperative to maintain comprehensive documentation (model cards, datasheets, risk assessments) so that auditors can understand and reproduce your organization's decisions.​

A.8 Third-Party and Customer Relationships

AI systems rarely operate in isolation. Controls in A.8 ensure responsibilities and risks are apportioned correctly:​

• A.10.2 Allocating responsibilities: When outsourcing development or using third‑party models/data, you must clearly allocate roles and responsibilities. Contracts should specify quality, security, privacy, and ethical requirements. Suppliers need to align with your AI policy and undergo due diligence.​
• A.10.3 Suppliers: Establish a process for selecting and monitoring suppliers. Evaluate their ability to meet responsible AI requirements and require evidence (e.g., certifications, impact assessments). For critical service providers, align with DORA’s third‑party risk management obligations.​
• A.10.4 Customers: Where your AI services are used by customers, ensure your approach considers their needs and expectations. Provide transparency regarding how the system works along with its limitations and offer channels for feedback and redress.​

A.9 AI System Operation

• Monitoring and measuring performance: ISO 42001 requires an evaluation of the AI system's performance and effectiveness. Define what will be monitored (accuracy, fairness, drift, resource usage) and how often. Use automated monitoring tools but supplement with human review. Then, document results and corrective actions. ​
• Change management: Establish procedures for updating AI systems (retraining models, changing data pipelines, altering logic). Assess risks before making changes and ensure updates do not violate regulatory requirements. As always, document changes and communicate them to stakeholders.​
• Incident response: Develop playbooks for responding to AI incidents (e.g., harmful outputs, bias issues, security breaches), and don't forget to integrate AI incidents into your broader incident management and disaster recovery plans.​

A.10 AI Transfer or Decommissioning

At the end of an AI system’s life cycle, make sure you have a consistent process in place for the transfer or decommissioning of the AI. 

• Identify obligations to retain or delete data, models, and documentation. 
• Ensure that knowledge is transferred to new owners or teams and that residual risks are addressed. 
• Archive or securely dispose of data and models based on contractual and legal requirements (including DORA’s resilience rules and GDPR’s erasure rights).​

Annexes B-D

Annex A details control requirements, and the other three annexes specify further guidelines, covering the following:

‍Annex B: Detailed guidance for implementing the controls in Annex A.

1. Annex C: Objectives and common risk sources of AI implementation.
2. Annex D: Standards applicable to specific sectors and industries.

ISO 42001 Internal Audit Requirement

Internal audits are critical to confirm that your AIMS conforms to your own requirements and to the ISO 42001 standard. ​

Clause 9.2.1 requires organizations to conduct internal audits at planned intervals to verify that the AI management system conforms to both organizational requirements and the requirements of ISO 42001, and that it is effectively implemented and maintained. This mirrors ISO 27001’s audit clause and should be risk‑based.

A solid internal audit program should include the following elements:

• Plan: Scope, frequency, and methods (risk-prioritize high-risk AI systems).
• Independence: Auditors trained in ML risk, not tied to system development.
• Sampling: Policies, model cards, data inventories, monitoring logs, AISIA outputs.
• CAPA loop: Classify findings, assign owners, verify closure, escalate to management review.

The internal audit of an organization’s AIMS should fulfill the following objectives:

• Ensure that the AIMS is in accord with the overall business goals and strategic vision of the organization (Clause 5.1.)
• Evaluate the performance of AI systems against the ISO 42001 framework, including the controls outlined in Annex A and ensure compliance with legal and contractual requirements as mentioned in Clause A.18.
• Identify areas for improvement and outline processes for continual AIMS enhancement in alignment with Clause 10.1. 

Clause 9.2.2 Audit Program: This clause stipulates that organizations shall plan, establish, implement and maintain audit programs including the frequency, methods, responsibilities, planning requirements and reporting. When establishing the program, you must consider the importance of processes and results of previous audits. You must define audit objectives, criteria and scope, select auditors who can ensure objectivity and impartiality, and ensure that audit results are reported to relevant managers. Documented evidence of the program and results must be retained.

Internal Audit Best Practices

Internal Audits don’t have to be a headache. Our team will help you structure your internal audit and prepare your team for success.

• Schedule Frequent Internal Audits: In order to maintain an effective and compliant AIMS, organizations are required to conduct audits at regular intervals and base these audits on the risk level and complexity of its AI systems.
• Maintain Auditor Objectivity: Select auditors who are not involved in the creation and maintenance of the AIMS, in alignment with Clause 9.2.2.
• Maintain Boundaries between Audit Team and AIMS Team: As outlined in Annex B 3.2, establish clear AI roles and responsibilities and ensure that the audit team and AIMS team have separate responsibilities.
• Establish Clear Audit Objectives and Criteria: Clause 5.16 explains the importance of establishing clear audit objectives and rules. Ideally, these objectives should provide direction throughout the audit process and ensure that all relevant elements of the AIMS are evaluated against ISO 42001’s requirements.
• Select an experienced auditor with up-to-date certifications: According to Clause 7.2, organizations should select an auditor after  reviewing the auditor’s certifications, education, and training as relating to AI Systems and ISO 42001. Auditors should be trained in ML lifecycle, privacy/security, and model risk. 

BD Emerson will guide your team through internal audits and evaluate your organization’s readiness for a certification audit. We’ll help your team stay organized and focused throughout the external audit and certification process. Once you’ve achieved ISO 42001 certification, BD Emerson can continue supporting your organization through continuous improvement.

Learn more about how BD Emerson’s experts can guide your team though an Internal Audit for ISO 42001.

AI Security Tools

There are several, effective, open-source AI security tools that identify and eliminate potential attack paths before incidents occur. Effective tools are essential when it comes to securing your AI. 

These are several examples of open-source AI security tools: 

• Adversarial Robustness Toolbox (ART) – adversarial testing
• Garak – LLM jailbreak/prompt injection testing
• Privacy Meter – membership inference risk
• Audit AI – bias testing
• Commercial platforms:
• Mindgard – AI red-teaming
• Holistic AI – governance and monitoring
• Amazon Bedrock Guardrails – runtime safety filters

Schedule a consultation to learn about AI  tooling integration.

Researchers & Communities

There are a number of AI communities that provide collaborative spaces where individuals can share information and troubleshoot problems in a group of developers, researchers, students, and professionals. For anyone interested in learning more about AI, it is essential to seek external resources and information to expand your knowledge and awareness of current threats and risks. 

Some of the top communities include MLSecOps, OWASP AI Security & Privacy, MITRE ATLAS,  and NIST AI RMF.

Generative AI KPIs & KRIs 

Like with any initiative, it is critical to measure the success and security of your AI programs against several criteria, including:

• Drift rate
• Jailbreak success rate
• PII leakage rate
• Mean time to rollback
• % of high-risk AISIAs closed

Tracking these metrics ensures your AI initiatives remain effective and secure while aligning with organizational goals

ISO 42001 Artifacts & Templates 

By partnering with Vanta, BD Emerson provides tailored documentation that your team can access in one streamlined platform so that you can prepare for audits without rifling through a mountain of paperwork. 

Audit-ready evidence includes:

• AIMS Scope, AI Inventory, Data Governance SOPs
• Model Cards, Red-Team Playbooks, Incident Runbooks
• AISIA templates (ISO/IEC 42005 aligned)
• Internal Audit Plans, CAPA logs

This cuts down exponentially on the time your team spends doing paperwork so that they can return to business-critical tasks. 

Timeline 

The path to ISO 42001 certification isn’t the same for every organization. Your starting point depends on many factors, including the maturity of your AI practices, the status of your documentation, and whether you’ve implemented other ISO standards before.

For most small and mid-sized businesses, certification is achieved within 4 to 9 months.

A typical journey looks like this:

• Discovery & Gap Assessment: 2–4 weeks to benchmark current practices against ISO 42001 requirements.
  
  
• System Design & Documentation: 1–3 months to build out or refine the AI Management System (AIMS).
  
  
• Operational Rollout & Internal Audit: 1–2 months to implement processes, train staff, and complete the internal audit.
  
  
• Certification Audit: 1–2 months, depending on auditor scheduling.
  
  
• Corrections & Adjustments (if needed): 1–4 weeks to close any findings.

Companies with prior ISO frameworks in place or streamlined AI operations often move faster, while first-time implementers may need the full timeline.

At BD Emerson, we guide clients through every step, streamlining documentation, embedding compliance into daily operations, and preparing teams for both internal and external audits, so that certification becomes a structured, predictable process.

Our AI-Related Security Services

BD Emerson’s AI security experts deliver tailored solutions and step-by-step guidance to get your AI Security program on track.

• ISO 42001 Consulting
• AI Security Consulting
• AI Governance Consulting
• EU AI Act Consulting
• Internal Audit 
• ISO 27001 integration
• Privacy-by-design & GDPR services

Conclusion

AI is no longer optional. It is embedded in business operations, customer experiences, and regulatory agendas. ISO/IEC 42001 offers the blueprint for building an AI Management System that not only meets compliance obligations but also strengthens governance, security, and trust. Organizations that act now can move faster than regulators, avoid costly missteps, and position themselves as leaders in responsible AI.

BD Emerson specializes in guiding teams through every step of ISO 42001 implementation, from scoping and control design to audit preparation and continuous improvement. 

Ready to operationalize AI security?

Book a discovery session with BD Emerson today. We’ll scope your AI portfolio, map risks, and deliver an ISO 42001 audit-ready AIMS plan that is aligned to EU AI Act, DORA, and NIS2.

Schedule a consultation