The EU AI Act: What it means for businesses, risk management, and data governance

Artificial intelligence is reshaping the way businesses across industries work, interact, and innovate.

From automating routine, time-consuming tasks and forecasting future trends to analyzing large volumes of data and delivering valuable insights at scale, AI has the potential to release new levels of efficiency, productivity, and innovation.

Today, the current AI environment is akin to the Wild West: A lawless time marked by the promise of unbridled opportunity and the threat of unforeseen risk. But with the introduction of the EU AI Act earlier this year, that’s all about to change.

What is the EU AI Act?

The EU AI Act, passed in March 2024, represents a landmark regulatory milestone designed to ensure the safe, transparent, and responsible use of AI systems across the European Union.

Much like the GDPR reshaped global data privacy norms, the EU AI Act establishes codified rules for both AI developers and AI deployers, including enterprises, SMEs, and public sector organizations.

The Act introduces:

• A risk-based regulatory structure
• Clear compliance timelines (6 months for unacceptable risk, 24–36 months for high-risk)
• Defined obligations for documentation, governance, transparency, and oversight
• Requirements for ensuring data quality, traceability, and security

For organizations scaling AI initiatives in 2025, the EU AI Act is more than compliance—it is a blueprint for building trustworthy, auditable, and safe AI systems.

Why do we need AI regulations?

While most artificial intelligence applications are designed to improve how we live and work, some AI apps introduce risks and biases, either intentionally or inadvertently.

Applications classified as high-risk, for example, hold the potential of putting people at risk of unfair treatment or pose safety risks if not properly governed.

These fall into categories including “critical infrastructure, education, and vocational training, employment, essential private and public services (e.g. healthcare, banking), certain systems in law enforcement, migration, and border management, justice and democratic processes (e.g. influencing elections).”

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Why AI regulation matters more than ever

As AI adoption accelerates across sectors—from healthcare to finance to manufacturing—so do risks related to:

• Bias and discrimination
• Unsafe outputs
• Opaque decision-making
• Data misuse or privacy violations
• Automated unfair outcomes in hiring, lending, or public services

Because AI learns from historical and real-time data, the quality, integrity, provenance, and governance of that data directly determine system behavior. Poor quality data leads to poor decisions—and high-risk models amplify the consequences.

The Act makes one thing clear: Responsible AI starts with responsible data.

Risk categories under the EU AI Act

A core feature of the Act is its four-tier risk classification framework, which determines what obligations apply to each AI system.

1. Unacceptable risk — Fully banned

These AI systems violate EU values or fundamental rights and are prohibited outright.

Examples include:

• Subliminal or behavioral manipulation
• Exploitation of vulnerabilities, including age or disability
• Biometric categorization based on sensitive traits (e.g., ethnicity, political beliefs)
• General-purpose social scoring
• Real-time remote biometric identification in public spaces
• Emotion recognition in workplaces or schools (except for specific safety use cases)
• Predictive policing based on personal traits
• Untargeted scraping of facial images

These systems pose inherent harms and are incompatible with responsible AI principles.

2. High risk — Strict governance required

High-risk AI systems significantly impact people’s safety, rights, access to services, or life opportunities.

Examples include AI used in:

• Critical infrastructure (transport networks)
• Education & vocational training (exam scoring)
• Medical devices / safety-critical systems
• Employment & HR (résumé screening, worker management tools)
• Essential private/public services (credit scoring)
• Law enforcement (evidence analysis)
• Migration, asylum, border management
• Judicial decision support

Obligations for high-risk systems include:

• Documented risk management
• High-quality, unbiased training datasets
• Traceability and logging
• Detailed technical documentation
• Human oversight mechanisms
• Demonstrable accuracy, robustness, and cybersecurity standards

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3. Limited risk — Transparency obligations

Systems in this category primarily require that humans are aware they are interacting with or being influenced by AI.

Examples:

• Chatbots and virtual assistants
• AI-generated content
• Synthetic media and deepfakes

Mandatory requirements:

• Clear disclosure (“This content was generated by AI”)
• Labeling of deepfakes and public-facing synthetic media

4. Minimal or no risk — Freely allowed

Most consumer AI systems fall here.

Examples:

• AI-powered video games
• Spam filters
• Product recommendation engines
• Basic analytics tools

These have no additional regulatory obligations.

The new imperative: Data governance for EU AI Act compliance

To comply with the AI Act—especially for high-risk systems—organizations must demonstrate robust data governance across their entire data ecosystem.

A modern data governance framework provides:

1. Clear data policies & controls

Defines how data is collected, used, shared, retained, secured, and audited.

2. Defined roles & responsibilities

Including Data Owners, Data Stewards, AI Product Owners, and Governance Committees.

3. Continuous data quality monitoring

Ensures datasets used for training, validation, and monitoring are:

• Accurate
• Complete
• Consistent
• Up-to-date
• Free from discriminatory bias

4. Data security & access control

Regulates who can access sensitive data and how that access is logged and verified.

5. Data lineage & traceability

Allows teams to:

• Track where data originates
• Understand how it transforms
• Prove how it feeds AI systems

6. Organization-wide data literacy

Teams must understand:

• What the AI Act requires
• How to responsibly use data
• How to identify risks early

This governance foundation ensures organizations can prove to regulators that their AI systems are safe, auditable, compliant, and trustworthy.

How DataGalaxy helps organizations comply with the EU AI Act

Implementing data governance helps to ensure that an organization’s data is accurate, accessible, and secure. And doing so requires the right combination of people, processes, and technology.

Defining the right roles and responsibilities (people) and developing the right data governance framework (process) are good first steps. However, having the right tools (technology) is essential for data governance to succeed.

DataGalaxy’s Data & AI Product Governance Platform is purpose-built for organizations adopting AI at scale under new regulatory standards.

With DataGalaxy, organizations can:

• Map and understand their entire data landscape using a centralized data catalog
• Document data definitions, classifications, lineage, and business context
• Assign clear data owners and stewards for accountability
• Automate regulatory reporting for AI training datasets and model documentation
• Track data provenance to demonstrate compliance
• Monitor data quality and historical changes
• Flag and manage sensitive or high-risk data elements
• Enable self-service discovery, reducing reliance on tribal knowledge

Trace issues down to the column level

Visualize how data quality issues impact tables, queries, and downstream assets.
 With lineage, diagrams, and column-level visibility, teams can assess risk and resolve problems faster.

This unified approach allows enterprises to build responsible AI pipelines that meet EU AI Act requirements while accelerating innovation—not slowing it down.

Data catalogs enable organizations to classify and flag sensitive data that should be used with caution, helping them to avoid risks and unintended biases that could result from the use of sensitive or restricted information when training AI models.

Conclusion

The EU AI Act represents the first of what will likely be numerous regulations aimed at ensuring the safe and responsible use of AI.

For organizations that need to demonstrate compliance, implementing data governance and a data knowledge catalog is a good first step toward meeting regulatory obligations.

Done right, data governance and a data knowledge catalog will ensure the data powering their AI apps remains clean, protected, and secure, which, in turn, will help aid compliance efforts.

About the author

Jessica Sandifer

With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.