Understanding Minimal and Limited Risk under the EU AI Act

A Practical Guide for DPOs and In-House Legal Teams

Artificial intelligence has quietly become part of everyday work. From productivity assistants to document summaries and email suggestions, most organisations already use AI without realising it. These technologies bring efficiency, but they also raise important questions for data protection and compliance professionals: how do you manage accountability, explainability, and transparency without overburdening your governance processes?

The EU AI Act offers a clear framework for doing exactly that. It classifies AI systems according to the level of risk they pose to people’s rights or safety. The EU AI Act distinguishes between unacceptable, high, and certain limited-risk systems. The term ‘minimal or no risk’ is commonly used to describe AI systems that fall outside the specific obligations of the Act. For most organisations, the focus will be on the last two categories. They cover the vast majority of AI systems in day-to-day use, tools that enhance productivity rather than make high-stakes decisions.

This article explains what minimal and limited risk systems are, what the EU AI Act expects of organisations that use them, and how DPOs and legal teams can embed proportionate AI governance into existing compliance frameworks.

Understanding the EU AI Act’s Risk Approach

The Act’s design is rooted in proportionality. It does not impose heavy regulation on every AI tool. Instead, it scales obligations according to the potential impact on people.

• Unacceptable risk systems are banned altogether. These include manipulative or exploitative AI such as social scoring or subliminal techniques.
• High risk systems are strictly regulated and typically found in sectors such as health, employment, education, credit scoring, or law enforcement.
• Limited risk systems require transparency measures so that users know when they are engaging with AI.
• Minimal risk systems carry no specific legal obligations beyond general laws such as the GDPR and consumer protection rules.

This tiered approach is important because it allows innovation to continue while protecting fundamental rights. For most DPOs and legal teams, the challenge is to translate those tiers into practical governance actions that fit within existing processes rather than duplicating them.

Why Minimal and Limited Risk Matter Strategically

It would be easy to treat these categories as purely technical or compliance-driven. In reality, they sit at the heart of strategic governance.

Accurately classifying AI systems defines how organisations can innovate safely. It helps determine when a full risk assessment is required, when lighter documentation will suffice, and how to prioritise oversight. More importantly, it demonstrates to regulators, partners, and customers that the organisation understands its responsibilities and has a defensible approach to accountability.

This is not simply about avoiding fines. Clear classification also builds trust and confidence among staff and clients. When people know how and why AI is being used, the organisation’s reputation for transparency and ethical practice grows stronger. That is particularly valuable in markets where trust is a differentiator, such as healthcare, finance, and technology.

Minimal Risk AI: Low Impact, High Accountability

Minimal risk AI refers to systems that present little or no potential to affect individuals’ rights or safety. They typically automate small, low-stakes tasks, often in the background, and do not involve profiling or decision-making.

Common examples include:

• Grammar and spelling assistants.
• Autocomplete and predictive text.
• Search or document retrieval tools.
• Spam filters and simple categorisation algorithms.

The EU AI Act imposes no direct obligations on these systems, but good governance remains essential. Accountability underpins both the Act and the GDPR. DPOs should ensure that minimal risk systems are visible in governance registers and can be explained if questions arise.

Practical steps for managing minimal risk AI:

• Keep a short internal note in your DPIA or processing record identifying the system, its function, and your rationale for minimal risk classification.
• Record the supplier, model version, and location of any data processed.
• Periodically review the system to ensure that its functionality has not evolved into areas such as profiling or analytics that might alter the risk level.

Minimal risk does not mean no oversight. A one-page record of your reasoning is often enough to show accountability, but it is also a valuable signal of organisational maturity.

The difference between minimal and limited risk is not about technical complexity but about human impact. Once AI begins interacting with people or generating information that could shape perceptions or decisions, transparency becomes the key dividing line.

Limited Risk AI: Where Transparency Becomes the Safeguard

Limited risk systems are those that interact with users directly or generate synthetic content but are not used in sensitive or high-risk contexts. Their primary risk lies in misunderstanding, in that people may not realise they are engaging with AI or may over-rely on outputs.

Examples include:

• Chatbots and virtual assistants.
• Tools that generate text, audio, or images.
• Meeting transcription or summarisation services.
• Productivity assistants that draft, summarise, or recommend actions.

For limited risk AI, the EU AI Act focuses on transparency obligations. Transparency obligations for certain AI systems, such as chatbots, emotion recognition, and systems generating synthetic content, are set out in Article 50 of the AI Act. Users must:

• Be clearly informed that they are interacting with AI.
• Users must be informed that content has been generated or manipulated by AI.
• Be able to identify AI-generated or synthetic content from notifications.
• Understand the capabilities and limitations of the system.

The goal is not to stop organisations using these tools, but to make sure people know when AI is at work and can interpret its outputs appropriately.

Practical steps include:

• Maintain a register of all limited risk AI systems with notes on their transparency measures.
• Confirm that user interfaces display clear AI notices or indicators.
• Keep vendor documentation that demonstrates compliance with the EU AI Act’s transparency articles.
• Incorporate transparency records into your DPIA or a dedicated AI governance appendix.

Transparency is the safeguard for limited risk AI. When users understand when AI is involved, how it works, and what it cannot do, most of the compliance risk disappears.

A Practical Example: Microsoft 365 Copilot

Microsoft 365 Copilot illustrates limited risk AI in action. Microsoft 365 Copilot would typically fall within the limited-risk category when used for general productivity tasks, but classification may change depending on context (for example, HR decision-making could raise the risk level). It operates inside familiar tools such as Word, Outlook, Excel, and Teams, using the organisation’s existing data. Copilot is not creating a new dataset, but it changes how that data is accessed and used.

DPOs can approach Copilot systematically:

1. Map the data flow. Identify what sources Copilot draws from. Most will already be governed under GDPR.
2. Determine the risk tier. Copilot’s summarisation and drafting features fall within the limited risk category.
3. Ensure transparency. Provide staff training and internal guidance making it clear that Copilot uses AI and that outputs require human review.
4. Verify supplier compliance. Keep copies of Microsoft’s documentation on Copilot’s AI model, transparency commitments, and security measures.
5. Reassess periodically. If Copilot is later used in HR or decision-making contexts, reclassify it as high risk, if applicable, and expand governance accordingly.

Copilot is a good example of how limited risk AI sits inside existing compliance frameworks. The AI layer does not replace GDPR obligations; it adds a transparency layer on top.

Managing Vendors and Third-Party AI

AI governance does not end with in-house systems. Third-party vendors and cloud providers are increasingly embedding AI functionality into standard software packages. DPOs need to know what these systems are doing and how they fit into the organisation’s risk profile.

Practical supplier governance steps include:

• Updating vendor due diligence questionnaires to include AI-specific questions.
• Requiring suppliers to disclose whether their systems use AI and, if so, how they classify it under the EU AI Act.
• Ensuring contracts contain obligations for transparency and notification of material changes in functionality.
• Reviewing third-party privacy notices to check alignment with your organisation’s transparency commitments.

This supplier awareness is critical because many limited risk systems will enter the organisation indirectly through updates or integrated features. A question as simple as “Does this system now use AI?” should become part of routine vendor management.

Combining AI Risk Assessment with DPIAs

AI risk assessments and GDPR DPIAs often apply to the same technology. Running them separately wastes time and risks inconsistency. A combined assessment provides a single, coherent record of compliance.

A practical two-in-one approach looks like this:

1. Begin with your existing DPIA template.
2. Add an AI section that determines the system’s risk tier under the EU AI Act.
3. Cross-reference overlapping controls, such as fairness, accuracy, and human oversight.
4. Record your rationale for classification and any transparency measures applied.

This combined model makes your documentation more efficient and defensible. It also shows regulators that the organisation is integrating AI governance within established privacy processes rather than treating it as a siloed exercise.

You do not need separate compliance tracks for AI and data protection. A single integrated DPIA with an AI addendum provides a clear, practical, and efficient approach to governance.

Building a Culture of Transparency and Awareness

AI compliance is not just a technical task. It depends on awareness across the organisation. Many risks arise not from deliberate misuse but from lack of understanding about where AI is operating.

DPOs can help by:

• Training staff to recognise when systems might use AI and how to disclose it.
• Including AI awareness in induction and refresher compliance training.
• Providing a clear reporting route for staff who introduce new AI tools or discover them within existing systems.
• Encouraging open discussion about AI ethics and bias without creating a culture of fear.

A culture of awareness ensures that AI deployments are surfaced early, documented properly, and reviewed for transparency obligations before they create regulatory problems.

The Case for Public AI Transparency Policies

Every organisation using AI should have a concise AI Transparency Policy available to the public. While not required by the EU AI Act, publishing a short AI transparency statement is a good practice for accountability and public trust. This policy communicates the organisation’s position, shows leadership, and demonstrates accountability in a visible way.

A strong policy should:

• Outline what types of AI systems are used and for what purpose.
• Describe how each category is governed and classified under the EU AI Act.
• Explain how transparency and fairness are maintained.
• Provide a contact route for questions or concerns.

For user-facing services, an AI indicator icon or short disclosure note linking directly to the policy can make transparency tangible. This approach mirrors cookie banners and privacy notices, ideally short, accessible, and visible.

Transparency builds confidence. A clear policy and visible AI indicator show that the organisation is proud of its responsible practices rather than hiding them in small print.

Questions to Ask in Governance and Board Meetings

Board and compliance meetings are where accountability becomes visible. Directors and senior managers do not need to be AI experts, but they should know how to ask the right questions. These conversations build oversight and reinforce the organisation’s duty to monitor risk.

Useful questions include:

• Do we have a current and published AI Transparency Policy?
• Is there an AI systems register, and who maintains it?
• What models or third-party tools are currently in use across our environment?
• Do we ask suppliers to confirm whether their products include AI components or use third-party models?
• Have our DPIA templates been updated to include AI classification and transparency checks?
• Who reviews risk classifications and re-evaluates systems as they evolve?
• How do we communicate AI use internally to staff and externally to clients or regulators?

Governance is not about knowing every detail of how AI works. It is about asking questions that reveal whether proper control and understanding are in place.

Regularly reviewing these questions in board meetings keeps AI governance aligned with other corporate risks. It also creates an audit trail showing active oversight which is a powerful indicator of accountability.

Roles and Accountability in AI Oversight

AI governance often sits across several functions. DPOs manage data protection, CISOs handle security, legal teams address contractual risk, and IT manages deployment. For many organisations, the best approach is to establish a cross-functional AI governance group.

This group should:

• Meet periodically to review the AI systems register and any new implementations.
• Ensure consistent interpretation of risk classification.
• Align AI oversight with broader risk frameworks such as ISO 27001, NIST AI RMF, or internal ethics committees.
• Report key findings to senior management and the board.

A shared model of accountability prevents gaps and ensures that AI risks are addressed from both ethical and operational perspectives.

Looking Ahead: The Future of AI Governance

The EU AI Act is the first comprehensive AI regulation, but it will not be the last. Global frameworks are converging. The NIST AI Risk Management Framework, OECD principles, and upcoming UK AI Assurance Guidance all reinforce similar ideas: risk-based classification, transparency, human oversight, and accountability.

Organisations that build governance structures now, even for minimal and limited risk AI, will be well positioned as new standards evolve. The European Commission’s AI Office, expected to oversee implementation, will likely emphasise documentation and transparency as core indicators of compliance maturity.

Future audits may ask to see your AI systems register, transparency policy, and evidence of staff awareness. Starting small, with minimal and limited risk systems, ensures that governance habits are already in place when oversight becomes more formal.

Bringing It All Together

The EU AI Act provides an opportunity, not a burden. For most organisations, compliance will not mean complex technical changes, but thoughtful governance and clear communication. The EU AI Act entered into force on 1 August 2024, with most obligations, including transparency rules for limited-risk AI, becoming applicable from 2 August 2026.

By classifying systems accurately, integrating AI risk assessment into DPIAs, maintaining a public transparency policy, managing supplier disclosures, and embedding awareness at all levels, DPOs and legal teams can meet the requirements confidently.

Minimal and limited risk AI may seem low on the regulatory ladder, but they represent the foundation of responsible AI use. Transparent documentation, consistent oversight, and honest communication will not only meet compliance expectations but also strengthen trust, with clients, employees, and regulators alike.

Compliance done the right way is not about doing everything; it is about doing the right things properly, documenting them clearly, and being open about how technology is used. That is how ethical organisations turn regulation into a mark of integrity.