AI Governance Explained: What Your Organisation Needs to Understand About ISO 42001 and ISO 27001

Artificial intelligence is no longer a technology organisations can simply observe from a distance and decide about later. It is already embedded into everyday business activities, from AI assistants and automated workflows to analytics platforms, customer service tools and decision-support systems.

As AI adoption increases, organisations face a new challenge: understanding how to manage the opportunities and risks that come with it.

For organisations operating ISO management systems, the question is not only “Are we using AI?” but also:

  • Do we understand where AI is being used?
  • Have we considered the risks it introduces?
  • Are responsibilities clearly defined?
  • Are we managing AI in a way that is secure, transparent and accountable?

This is where standards such as ISO 42001 and ISO 27001 become increasingly relevant.

Why AI Governance Matters

AI can deliver significant benefits, but it also introduces new types of risk.

Unlike traditional software systems, AI systems can produce outputs that are difficult to predict, change over time, and depend on complex data, models and configurations.

Poorly managed AI use can create challenges including:

  • Information security risks
  • Unintentional data disclosure
  • Inaccurate or unreliable outputs
  • Bias or unfair outcomes
  • Lack of transparency
  • Regulatory and compliance issues
  • Over-reliance on automated decision-making

These risks are not purely technical. They involve people, processes, governance and decision-making.

Effective AI governance requires organisations to understand how AI fits within their wider management system.

ISO 42001: The AI Management System Standard

ISO 42001 is the international standard for Artificial Intelligence Management Systems (AIMS).

Published in 2023, it provides organisations with a structured framework for managing AI responsibly throughout its lifecycle.

The standard addresses areas including:

  • AI governance
  • Risk management
  • Transparency
  • Accountability
  • Human oversight
  • Continual improvement

Like many modern ISO management system standards, ISO 42001 follows the Harmonised Structure. This means organisations familiar with standards such as ISO 27001, ISO 9001 or ISO 14001 will recognise many common concepts, including:

  • Context of the organisation
  • Risk assessment
  • Documented information
  • Internal audits
  • Management review
  • Continual improvement

ISO 42001 is not only relevant for organisations developing AI systems. It is also valuable for organisations using third-party AI tools and wanting a structured approach to managing their use.

Why ISO 27001 Still Matters for AI

While ISO 42001 focuses specifically on AI governance, ISO 27001 remains a critical foundation for managing the security risks associated with AI.

AI systems rely on information. This may include:

  • Training data
  • Business information
  • Customer information
  • User inputs
  • System configurations
  • Model outputs

As a result, AI introduces many information security considerations that should be managed through an organisation’s Information Security Management System (ISMS).

Organisations should consider questions such as:

Are AI systems included within information security risk assessments?

AI tools and services should be considered alongside other information assets.

Risks may include unauthorised access, data exposure, service disruption or incorrect outputs affecting business operations.

What happens if an AI system becomes unavailable?

If important business processes rely on AI, organisations need to understand the impact of outages or supplier failures.

Business continuity planning should consider:

  • Alternative processes
  • Recovery arrangements
  • Supplier dependencies
  • Acceptable downtime

How are AI systems controlled and maintained?

AI behaviour can change due to updates, new versions or changes in configuration.

Organisations should consider:

  • Version control
  • Change management
  • Testing processes
  • Monitoring arrangements

Maintaining traceability is important for understanding why an AI system produced a particular result.

How are third-party AI providers managed?

Many organisations use AI through external platforms and software providers.

Using a third-party AI service does not remove responsibility. Organisations still need to understand:

  • What data is being processed
  • How suppliers manage security
  • What contractual protections exist
  • What happens if the service changes or becomes unavailable

What Good AI Governance Looks Like

Regardless of whether an organisation pursues ISO 42001 certification, the principles behind effective AI governance provide a useful framework for managing AI responsibly.

Understanding where AI is being used

The first challenge for many organisations is visibility.

Employees may already be using AI tools for writing, analysis, research or automation without those tools being formally reviewed or recorded.

Effective governance begins with understanding what AI exists within the organisation.

Assessing AI-related risks

Not every AI system creates the same level of risk.

An AI tool used to support internal administration has very different implications from an AI system used to influence decisions affecting customers, employees or members of the public.

Risk assessments should consider:

  • The purpose of the AI system
  • The information it processes
  • The decisions it influences
  • The potential impact if something goes wrong

Maintaining transparency and accountability

Organisations should understand how AI systems are used and who remains responsible for decisions.

AI should support human decision-making, not remove accountability.

This is particularly important where AI influences areas such as:

  • Recruitment
  • Financial decisions
  • Healthcare
  • Customer services
  • Regulatory compliance

Managing the AI supply chain

AI providers should be treated as part of the organisation’s wider supplier ecosystem.

Good governance includes understanding:

  • Supplier responsibilities
  • Security controls
  • Data handling practices
  • Contractual obligations
  • Service dependencies

Who Needs to Understand AI Governance?

AI governance is not only an IT responsibility.

Because AI affects information security, risk, compliance and operational decision-making, understanding its implications is becoming increasingly important for:

  • Information Security professionals
  • Compliance teams
  • Risk managers
  • Quality managers
  • Governance professionals
  • Procurement teams
  • Internal auditors
  • Senior leadership

AI governance requires collaboration across the organisation.

Building Awareness of AI and ISO Management Systems

The adoption of AI is moving quickly, and organisations are still developing their approach to managing it effectively.

The organisations that are best positioned for the future will be those that build awareness early — understanding not only what AI can do, but also how it should be governed.

ISO 42001 provides a structured approach for responsible AI management, while ISO 27001 continues to provide a strong foundation for protecting information and managing security risks.

Together, these standards help organisations approach AI with greater confidence, control and accountability.

Final Thoughts

AI is changing how organisations operate, but successful adoption depends on more than technology.

It requires clear governance, effective risk management and people who understand their responsibilities.

ISO 42001 provides a framework for managing AI responsibly, while ISO 27001 helps organisations protect the information that AI systems depend upon.

As AI becomes increasingly embedded into business processes, developing awareness of these standards will become an important part of building resilient, responsible and future-ready organisations.