ISO 42001 Artificial Intelligence Management System

This page covers the concepts of AI governance. For the ISO 42001 certification and consulting process, please see the related page.

ISO 42001 Compliance, Gap Analysis, AI Governance, AI Risk Management and Certification Readiness

Artificial intelligence is no longer an experimental tool used only by technology teams.

Generative AI, machine learning, decision support systems, automated classification, forecasting systems, image processing, natural language processing, intelligent automation and AI-based software components have become part of organisations' real business processes.

While this transformation creates significant opportunities, it also brings new risks.

An AI system can make incorrect decisions, use biased data, produce unexpected outputs, expose confidential information, process personal data inappropriately, be manipulated, contain security vulnerabilities, or create legal and ethical risks.

It is therefore not enough for an AI system merely to "work".

Organisations need to be able to answer the following questions:

Which AI systems are we using?

Who is responsible for these systems?

Which data is being used?

How do we assess the risks?

How do we control the decisions made by AI?

How do we detect incorrect or harmful outputs?

How do we manage third-party AI services?

How do we control the lifecycle of AI models?

ISO/IEC 42001 brings a systematic management system approach to exactly these questions.

SecureSys provides end-to-end ISO 42001 consulting to help organisations establish an ISO/IEC 42001 Artificial Intelligence Management System – AIMS, assess their current position, identify their AI risks and prepare for the ISO 42001 certification process.

Our approach is not simply about preparing policies and procedures.

AI governance is assessed across the dimensions of:

people + process + technology + data + models + risk + security + ethics + regulation

The objective is not merely to obtain an ISO 42001 certificate, but to establish a trustworthy, responsible, traceable and sustainable AI management model within the organisation.

What Is ISO/IEC 42001?

ISO/IEC 42001 is the international management system standard for the governance of organisations' artificial intelligence systems.

The standard aims to help organisations that develop, supply or use AI manage the opportunities and risks arising from those systems systematically.

Under ISO 42001, organisations are expected to:

• Establish AI governance,
• Define AI policies and objectives,
• Assess AI risks,
• Define roles and responsibilities,
• Manage the AI lifecycle,
• Assess data and model risks,
• Manage third-party AI providers,
• Monitor performance,
• Carry out internal audits,
• Conduct management reviews,
• Deliver continuous improvement

ISO 42001 is therefore not simply a technical AI standard.

It is a management system standard.

What Is an Artificial Intelligence Management System?

An Artificial Intelligence Management System – AIMS is the structure that allows an organisation's AI-related policies, processes, responsibilities, controls and risk management mechanisms to be managed as a whole.

Put simply:

ISO 42001 defines how the organisation governs artificial intelligence.

An organisation may use dozens of AI tools.

ChatGPT, Microsoft Copilot, Google Gemini, GitHub Copilot or models developed in-house may all be in use.

Using these systems without a central governance structure, however, can create significant risk.

Employees may, for example, send customer data, source code, financial information or trade secrets to third-party AI systems without any control.

This is why AI governance has become critical.

Who Is ISO 42001 Suitable For?

ISO 42001 can be applied both to organisations that develop AI and to those that use it.

It is an important governance framework in particular for:

• Software companies,
• AI product developers,
• SaaS companies,
• FinTech organisations,
• Banks,
• Insurance companies,
• Public institutions,
• Defence industry firms,
• Technology companies,
• Healthcare organisations,
• Manufacturing companies,
• E-commerce firms,
• Human resources platforms,
• Data analytics companies,
• AI consultancies,
• Large corporate enterprises

Why Does ISO 42001 Matter?

The use of artificial intelligence is spreading very rapidly.

AI usage within organisations, however, is often not managed centrally.

For example:

one team may use ChatGPT.

Another may use Microsoft Copilot.

The software team may use GitHub Copilot.

The marketing team may use a different generative AI platform.

The HR team may use an automated CV screening system.

An organisation may have dozens of AI systems in use at the same time without all of them being known centrally.

This creates shadow AI risk.

ISO 42001 helps bring this fragmented picture under control and make it manageable.

What Is Shadow AI?

Shadow AI is the use by employees of AI tools that have not been formally approved by the organisation or are not centrally controlled.

Shadow AI can create significant risk.

An employee may upload the following to a generative AI tool:

• Source code,
• Customer data,
• Contracts,
• Financial reports,
• Personal data,
• Technical designs

If it is not known how that data is processed or stored, data security and privacy risks can arise.

As part of its ISO 42001 consulting, SecureSys supports organisations in establishing an AI usage policy.

What Is ISO 42001 Consulting?

ISO 42001 consulting is the process of assessing an organisation's existing AI usage and management structure, identifying the gaps, and establishing an Artificial Intelligence Management System that meets the requirements of ISO/IEC 42001.

SecureSys consulting assesses the following together:

• Organisational structure,
• AI use cases,
• AI products,
• AI models,
• Data sources,
• Third-party AI services,
• AI security risks,
• Ethical risks,
• Legal risks,
• Operational risks

ISO 42001 Gap Analysis

An ISO 42001 gap analysis identifies the differences between the organisation's existing AI management structure and the requirements of ISO/IEC 42001.

The gap analysis assesses questions such as:

• Does the organisation have an AI policy?
• Is there an inventory of the AI systems in use?
• Have AI system owners been identified?
• Is risk assessment carried out?
• Are data sources controlled?
• Is model performance monitored?
• Are AI outputs validated?
• Is bias risk assessed?
• Are third-party AI services controlled?
• Is AI security testing carried out?
• Is human oversight defined?
• Is there an incident management process?

The result is an ISO 42001 compliance roadmap tailored to the organisation.

What Is an AI Inventory?

One of the first steps in ISO 42001 work is identifying the AI systems in use across the organisation.

An AI inventory can hold information such as:

• The name of the AI system,
• Its owner,
• Its purpose,
• Data sources,
• Model type,
• Provider,
• User group,
• Type of data processed,
• Criticality,
• Risk level

This inventory forms the foundation of the AI governance structure.

Identifying AI Use Cases

Not every AI system carries the same level of risk.

An AI system that generates marketing copy, for example, cannot be assessed at the same risk level as one that influences a credit decision.

Use cases should therefore be classified on a risk basis.

In SecureSys consulting, AI use cases can be classified at levels such as:

Low Risk – Medium Risk – High Risk – Critical

AI Risk Management

One of the most important areas of ISO 42001 is the AI risk management approach.

AI systems can create risks that differ from those of classic IT systems.

For example:

• Bias,
• Hallucination,
• Model drift,
• Data poisoning,
• Prompt injection,
• Model inversion,
• Sensitive data leakage,
• Adversarial attacks,
• Insecure output handling,
• Third-party AI risk.

AI risk assessment therefore has to be approached more broadly than classic information security risk analysis.

What Are the Risks of Artificial Intelligence?

Hallucination

The AI system produces answers that are not grounded in fact but appear credible.

This can create serious risk particularly in legal, financial, healthcare and technical decision processes.

Bias

Training data or model design can produce systematically different outcomes for particular groups.

Model Drift

Model performance changes or degrades over time.

Data Leakage

Confidential or sensitive information is exposed through model inputs or outputs.

Prompt Injection

Specially crafted inputs prepared by an attacker cause the AI system to behave in unintended ways.

Data Poisoning

Model behaviour is influenced through manipulation of the training or reference data.

ISO 42001 and AI Security

AI security is one of the important parts of an ISO 42001 management system.

Where required, the SecureSys consulting approach can assess technical security tests such as:

• Prompt injection testing,
• AI application penetration testing,
• API security testing,
• LLM security assessment,
• Model access control,
• Data leakage testing,
• AI red teaming

The aim is to understand not only the managerial but also the technical risks of AI systems.

What Is AI Red Teaming?

AI red teaming is the testing of AI systems from an attacker's perspective against security, reliability and misuse scenarios.

AI red teaming can assess scenarios such as:

• Prompt injection,
• Jailbreaking,
• Sensitive information disclosure,
• Authorisation bypass,
• Unsafe output,
• Model manipulation

This approach is particularly important for critical AI systems.

AI Data Management

The quality of an AI system depends largely on the data it uses.

Data management is therefore critically important under ISO 42001.

The assessment can examine areas such as:

• Data source,
• Data quality,
• Data ownership,
• Data integrity,
• Data privacy,
• Data retention period,
• Data classification

Data Quality and Artificial Intelligence

Poor-quality data is one of the most significant causes of incorrect AI output.

Data should therefore be:

• Accurate,
• Current,
• Representative,
• Consistent,
• Traceable

Managing the data sources of AI systems directly affects model reliability.

AI Transparency

Providing an appropriate level of transparency about how AI systems are used is important.

Users or affected parties may need to understand:

• That AI is being used,
• Through which process the decision was produced,
• Whether human control is in place

The level of transparency should be determined according to the risk of the use case.

Explainability

It can be difficult to understand why some AI systems reach the decisions they do.

This can create risk particularly in finance, human resources and other critical decision processes.

Explainable AI – XAI refers to methods that make model outputs easier for people to understand.

Explainability requirements can be defined where necessary in ISO 42001 implementations.

Human Oversight

Under the ISO 42001 approach, leaving critical decisions entirely to AI systems without control can be risky.

Depending on the use case, a human-in-the-loop approach can therefore be applied.

Human oversight ensures that decisions made by the AI system are checked, or approved by a person in certain situations.

What Is Responsible AI?

Responsible AI is the approach of developing and using AI systems in a way that is safe, ethical, fair, transparent and accountable.

Responsible AI is generally based on the following principles:

• Fairness,
• Transparency,
• Accountability,
• Privacy,
• Security,
• Human oversight,
• Reliability.

ISO 42001 helps turn these principles into a management system within the organisation.

What Is AI Governance?

AI governance is the structure that determines how an organisation's AI systems are selected, used, developed and monitored.

AI governance can establish structures such as:

• AI policy,
• AI committee,
• Roles and responsibilities,
• Approval workflow,
• Risk classification,
• AI inventory,
• Monitoring,
• Incident management

The AI Committee

Establishing an AI governance committee can be useful in large organisations.

The committee can include representatives from:

• IT,
• Information security,
• Legal,
• KVKK / privacy,
• Risk,
• Human resources,
• Business units,
• Software development

The aim is to ensure that AI decisions are not made by the technology team alone.

ISO 42001 Roles and Responsibilities

Responsibilities for AI systems need to be defined clearly.

For example:

AI system owner

The person responsible for the business use of the AI system.

Model owner

The team responsible for the model lifecycle.

Data owner

The person responsible for the accuracy and use of data sources.

Information security

Assesses AI security risks.

Compliance

Assesses regulatory and standard requirements.

These roles can be customised to the organisation's structure.

The AI Lifecycle

AI systems should be managed through a lifecycle approach.

An example lifecycle:

Idea → Risk Assessment → Data → Development → Test → Approval → Go-Live → Monitoring → Change → Retirement

Different risks can emerge at each stage.

ISO 42001 makes this lifecycle systematic.

The AI Development Process

In organisations that develop their own AI models, the process has to be addressed in greater detail.

The following processes can be defined:

• Data preparation,
• Model selection,
• Training,
• Validation,
• Testing,
• Deployment,
• Monitoring,
• Retraining

Model Validation

It is not enough for a model simply to work technically.

The model has to perform correctly in the real use case.

Model validation can use metrics such as:

• Accuracy,
• Precision,
• Recall,
• Error rate,
• False positives,
• False negatives

The metric used should be selected according to the model's purpose.

Model Monitoring

An AI model must continue to be monitored after go-live.

Over time:

• Performance can decline.
• The data distribution can change.
• Bias can emerge.
• New attack methods can appear.

Continuous monitoring is therefore necessary.

Model Change Management

When a new version of an AI model is released, the change must be carried out under control.

The following process, for example, can be applied:

Change Request → Risk Assessment → Test → Approval → Deployment → Monitoring

This prevents uncontrolled model changes.

Generative AI and ISO 42001

Generative AI technologies are one of the particularly important use areas under ISO 42001.

Using systems such as ChatGPT, Copilot and Gemini within the organisation brings new risks.

The following in particular should be assessed:

• Confidential data leakage,
• Hallucination,
• Copyright,
• Sensitive input,
• Prompt injection,
• Insecure plugins

Corporate ChatGPT Usage Policy

A generative AI usage policy can be prepared for the organisation as part of ISO 42001 consulting.

The policy can define:

• Which AI tools may be used,
• Which data may not be uploaded,
• Use of personal data,
• Sharing of source code,
• Confidential information,
• Validation of AI outputs,
• Human control

AI Supplier Management

Many organisations obtain AI from external service providers rather than developing it themselves.

The following may be in use:

• Microsoft,
• OpenAI,
• Google,
• AWS,
• SaaS providers

An AI third-party risk management process should therefore be established.

Supplier assessment can examine:

• Data location,
• Data usage policy,
• Model training,
• Security controls,
• SLA,
• Incident notification,
• Sub-processors

AI Supply Chain Risk

AI systems can involve many dependencies.

A chain such as the following can form:

AI application → LLM provider → cloud provider → dataset → API → plugin

Supply chain risks therefore have to be assessed.

The Difference Between ISO 42001 and ISO 27001

ISO 42001 and ISO/IEC 27001 are not the same standard.

ISO 27001 focuses on the information security management system.

ISO 42001 focuses on the artificial intelligence management system.

ISO 27001 manages confidentiality, integrity and availability risks, whereas ISO 42001 also covers the broader governance and ethical risks of AI systems.

The two standards can be used together.

ISO 42001 and ISO 27701

ISO 27701 relates to personal data and privacy management.

Where AI systems process personal data, ISO 42001 and ISO 27701 can be assessed together.

If an AI-based recruitment system used by HR processes personal data, for example, both AI governance and privacy risks have to be addressed together.

ISO 42001 and the NIST AI RMF

The NIST AI Risk Management Framework is a strong framework for managing AI risks.

ISO 42001, meanwhile, is a certifiable management system standard.

The two structures can support one another.

The NIST AI RMF risk assessment approach can contribute to ISO 42001 implementations.

ISO 42001 and the EU AI Act

The EU AI Act is a legal framework that regulates AI systems according to their risk level.

ISO 42001 is a management system standard.

They are therefore not the same thing.

The processes established under ISO 42001, however, can support EU AI Act compliance work:

• AI inventory,
• Risk management,
• Governance,
• Documentation,
• Monitoring,
• Human oversight

ISO 42001 and GDPR / KVKK

Where AI systems use personal data, GDPR and KVKK requirements have to be assessed separately.

The following can become important within an AI project:

• The purpose of personal data processing,
• Legal basis,
• Data minimisation,
• Retention period,
• Data subject rights,
• Profiling,
• Automated decision-making

Automated Decision-Making

Some AI systems can make automated decisions about people.

Systems such as the following can fall within this scope:

• Credit scoring,
• Insurance pricing,
• Recruitment,
• Fraud detection

AI risk management has to be applied more strictly in systems of this kind.

ISO 42001 and Software Development

ISO 42001 is particularly important for software companies developing AI applications.

The following can be integrated into the development process:

• AI risk assessment,
• Secure development,
• Model testing,
• Bias testing,
• Data validation,
• AI security testing

The Responsible AI approach is then built into the software lifecycle.

ISO 42001 and DevSecOps

Security must be part of the development lifecycle in AI development projects.

MLOps and DevSecOps can be assessed together.

The following controls, for example, can be applied:

• Source code security,
• Dependency security,
• Container security,
• API security,
• Secret management,
• Model registry security

What Is MLOps?

Machine Learning Operations – MLOps is the systematic management of the development, testing, deployment and monitoring processes of machine learning models.

In an ISO 42001 implementation, MLOps processes can support AI lifecycle management.

AI Incident Management

AI systems can give rise to incident types that differ from classic cyber security incidents.

For example:

• Incorrect decisions,
• Bias incidents,
• Model compromise,
• Data leakage,
• Adversarial attacks,
• Unexpected output.

These events need to be recorded and managed.

The AI Incident Response Process

An example process:

Detection → Classification → Impact Analysis → Isolation → Remediation → User Notification → Root Cause Analysis → Improvement

This process is customised according to the organisation's risk level.

ISO 42001 Internal Audit

An internal audit should be carried out to assess whether the Artificial Intelligence Management System is being applied.

The internal audit can examine:

• Policies,
• Risk records,
• The AI inventory,
• Assessment records,
• Controls,
• Monitoring records

ISO 42001 Management Review

Senior management should assess AIMS performance regularly.

The management review can address:

• AI risks,
• Incident records,
• Audit results,
• Performance indicators,
• Changes,
• Corrective actions,
• Improvement opportunities

ISO 42001 KPI and KRI Management

The performance of the AI governance system has to be measured.

Example KPIs:

• Proportion of approved AI systems,
• Proportion of AI systems with completed risk assessment,
• Number of AI incidents,
• Model validation success,
• AI training completion rate,
• Third-party assessment rate.

Example KRIs:

• Number of critical AI risks,
• Number of uncontrolled AI systems,
• Data leakage incidents,
• Declines in model performance.

The ISO 42001 Certification Process

ISO/IEC 42001 is a certifiable management system standard.

Once the organisation has established the required AIMS structure, it can enter assessment by an independent certification body.

SecureSys consulting provides support across:

• Gap analysis,
• AIMS implementation,
• Risk assessment,
• Documentation,
• Implementation support,
• Internal audit,
• Management review,
• Certification readiness

The certification decision is the responsibility of the independent certification body.

How Does the ISO 42001 Compliance Process Work?

SecureSys ISO 42001 consulting projects generally consist of the following steps:

1. Scope Definition

The AIMS scope is defined.

2. AI Inventory

The AI systems in use and under development are identified.

3. Gap Analysis

The current state is compared against the ISO 42001 requirements.

4. AI Risk Assessment

AI risks are assessed.

5. AI Governance Model

Roles and responsibilities are established.

6. AI Policy

The AI policy is prepared.

7. AI Lifecycle Management

AI lifecycle processes are defined.

8. Data Governance

Data management processes are established.

9. Third-Party AI Management

AI suppliers are assessed.

10. AI Security

Security controls and testing requirements are defined.

11. Monitoring

Performance and risk monitoring processes are established.

12. Internal Audit

The management system is audited.

13. Management Review

Senior management assesses the system.

14. Certification Readiness

Preparation for external audit is carried out.

ISO 42001 Consulting Deliverables

Depending on project scope, SecureSys can prepare the following deliverables:

• ISO 42001 Gap Analysis Report
• Artificial Intelligence Management System Policy
• AI Governance Framework
• AI Inventory
• AI Risk Register
• AI Risk Assessment Methodology
• AI Acceptable Use Policy
• Generative AI Usage Policy
• AI Lifecycle Procedure
• AI Development Procedure
• AI Testing Procedure
• Model Validation Procedure
• AI Incident Management Procedure
• AI Supplier Risk Assessment
• AI Security Requirements
• Data Governance Requirements
• Human Oversight Procedure
• AI Impact Assessment
• AI Monitoring Plan
• AI KPI/KRI Matrix
• Internal Audit Documentation
• Management Review Documentation
• ISO 42001 Compliance Matrix
• Certification Readiness Report.

AI Impact Assessment

In critical AI projects, it is not only the technical risk that has to be assessed but also the system's effect on people and processes.

An AI impact assessment can evaluate:

• Impact on users,
• Impact on customers,
• Risk of discrimination,
• Financial impact,
• Privacy impact,
• Security impact,
• Operational impact

ISO 42001 Maturity Assessment

Alongside an ISO 42001 gap analysis, an AI governance maturity assessment can be carried out.

An example maturity model:

1 – Initial 2 – Developing 3 – Defined 4 – Managed 5 – Optimised

The organisation can then be assessed not only on conformity but also on AI management maturity.

AI Governance Dashboard

The AI management system can be reported to senior management through a simple dashboard.

For example, scores such as the following can be produced:

AI Governance – 75% AI Risk Management – 68% Data Governance – 72% AI Security – 64% Third-Party AI Risk – 58% Monitoring – 61%

A score alone, however, is not sufficient.

The dashboard should also include:

• Critical AI risks,
• Unapproved AI systems,
• Open actions,
• Incident counts,
• Risk trends

The Most Common Mistakes in ISO 42001 Consulting

Preparing Documents Only

An AI management system does not consist of procedures alone.

The processes have to be integrated into the real AI applications.

Not Building an AI Inventory

Risk management is impossible without knowing which AI systems are in use across the organisation.

Ignoring Shadow AI

Employees using uncontrolled AI tools can create significant data security risk.

Neglecting Technical Security

AI governance is not solely a matter of ethics and compliance.

The cyber security of AI systems has to be assessed separately.

Not Defining Human Oversight

Critical decision systems may need a human intervention mechanism.

Why SecureSys for ISO 42001 Consulting?

ISO 42001 consulting is not simply a matter of interpreting the clauses of the standard.

AI projects require the following areas to be addressed together:

GRC + software development + cyber security + data management + privacy + risk management

In the SecureSys approach, AI governance and technical security are assessed together.

The aim is for the AI policies produced to be consistent with the organisation's real software and technology infrastructure.

Our approach is based on the model:

Build the Inventory → Identify the Risk → Establish Governance → Apply the Controls → Test → Measure → Improve

Frequently Asked Questions

What is ISO 42001?

ISO/IEC 42001 is the Artificial Intelligence Management System standard for organisations to manage their AI systems systematically.

What is an Artificial Intelligence Management System?

It is the structure that manages an organisation's policy, risk, process, responsibility, control and monitoring mechanisms for AI systems.

What is an ISO 42001 certificate?

Organisations that establish an Artificial Intelligence Management System meeting the ISO 42001 requirements can obtain certification following audit by an independent certification body.

What is an ISO 42001 gap analysis?

It is the identification of the differences between the organisation's existing AI governance structure and the ISO 42001 requirements.

What is AI governance?

It is the governance structure that determines how AI systems are selected, developed, used and monitored, and how their risks are managed.

What is Responsible AI?

It is the approach of developing and using AI systems in a way that is safe, fair, transparent, accountable and respectful of human rights.

What is shadow AI?

These are AI systems or tools used by employees outside the organisation's formal control.

Who is ISO 42001 suitable for?

It can be applied to any organisation that develops or uses AI.

Are ISO 42001 and ISO 27001 the same?

No. ISO 27001 focuses on information security management, ISO 42001 on artificial intelligence management.

Are ISO 42001 and the EU AI Act the same?

No. The EU AI Act is a legal regulation. ISO 42001 is a management system standard. ISO 42001 implementations can, however, support EU AI Act compliance.

What is an AI risk assessment?

It is the systematic assessment of the technical, ethical, security, privacy, operational and legal risks arising from AI systems.

What is an AI impact assessment?

It is the assessment of the potential effects of an AI system on individuals, processes, customers and the organisation.

Can ISO 42001 certification be obtained?

Yes. ISO/IEC 42001 is a certifiable management system standard.

What does ISO 42001 consulting cover?

It can cover gap analysis, AI inventory, AI governance, risk assessment, policies and procedures, the AI lifecycle, third-party risks, AI security, internal audit, management review and certification readiness.

Move Artificial Intelligence From Uncontrolled Use to Corporate Governance With ISO 42001

Using artificial intelligence can now be a competitive advantage.

Uncontrolled use of AI, however, can create new corporate risks.

Do you know every AI system in your organisation?

Can you see which AI tools your employees are using?

Can you control whether customer data is being uploaded to generative AI?

Are risk assessments carried out on your AI models?

Are the critical decisions produced by AI checked by a human?

Is model performance measured regularly?

Are your AI suppliers assessed from a security perspective?

Is it clear how you would respond to an incident involving an AI system?

The answers to these questions show your organisation's real level of AI governance.

Through its ISO 42001 consulting, ISO 42001 gap analysis, Artificial Intelligence Management System, AI governance, AI risk management, Responsible AI, AI security and ISO 42001 certification readiness services, SecureSys helps organisations manage artificial intelligence safely and sustainably.

Measure Your AI Maturity Level With an ISO 42001 Gap Analysis

Banning artificial intelligence outright is not the answer.

Neither is using it without control.

The right approach is to:

Make it visible. Assess the risk. Set the rules. Control it. Test it. Improve continuously.

Request an ISO 42001 Artificial Intelligence Management System Consulting Proposal

To assess the AI systems in your organisation, identify your AI risks and build your ISO 42001 compliance roadmap, get in touch with the SecureSys team.

Don't just use artificial intelligence. Manage it safely, under control, and measurably.