EU AI Act compliance for enterprise AI
The EU AI Act is now enforceable, and enterprises have a limited window to put the required controls in place for high-risk AI systems. Meeting the requirements will depend on whether your architecture can govern data, control AI activity and produce the evidence regulators expect.
Download the guide to learn:
- What Articles 10–15 require from enterprise AI systems
- Which operational capabilities need to be implemented
- How those requirements translate into enterprise architecture
- Where data trust, provenance, runtime control and traceability fit
- What to prioritize before the December 2027 deadline
The implementation window is already open. The time to close the gaps in your architecture is now.
Get the Enterprise Guide to EU AI Act Compliance
A practical guide to Articles 10–15, including data governance, traceability, transparency, human oversight, accuracy and security.
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Operational controls for Articles 10–15
IndyKite connects identity, data provenance and policy context so AI interactions can be controlled, traced and reviewed. These capabilities support the operational requirements covered in the guide.
Article 10: Data governance
Articles 11–12: Records and traceability
Articles 13–14: Transparency and oversight
Article 15: Accuracy, robustness and security
Govern AI with trusted context and runtime control
IndyKite connects identities, enterprise data, provenance and policy context. Each AI request can be evaluated at runtime, enforced consistently and retained as evidence for review.
Govern the data used by AI
Apply policy at every request
Preserve evidence for audit
Support accountable human oversight
Build compliance controls in three steps
Connect identity, data and provenance
Apply policy to each AI interaction
Retain a traceable decision record
Hear from our customers
Reitan Retail
Rockwell Automation
Deutsche Telekom
PACCAR
EU AI Act compliance questions
The EU AI Act sets rules for developing, providing, deploying and using AI systems in the European Union. It can apply to organizations based inside or outside the EU when their AI systems, services or outputs are placed on the EU market or used in the EU. The obligations depend on the organization’s role and the system’s risk classification.
The Act uses a risk-based framework. Certain practices are prohibited, high-risk systems face detailed controls, and some AI systems have transparency obligations. Limited or minimal-risk systems face fewer mandatory requirements. Classification depends on the system’s intended purpose, where it is used and its potential impact on people. High-risk systems require documented risk management, appropriate data governance, technical documentation, logging, transparency, human oversight, accuracy, robustness and cybersecurity. Providers and deployers have different responsibilities, so organizations need clear ownership and evidence for the controls they operate.
IndyKite connects data with its provenance, ownership, sensitivity and permitted purpose. Policies can use that context to control which data an AI system may access and how it may be used. The resulting record supports reviews of data quality, lineage and governance controls under Article 10.
IndyKite records the identities, data, relationships, policies and runtime conditions involved in each governed interaction. Teams can reconstruct why access was allowed or denied, what information informed an action and where human intervention occurred. This evidence supports monitoring, incident investigation and audit preparation.
Start by inventorying AI systems, assigning accountable owners and classifying each use case by role and risk. Then map applicable obligations to operational controls, evidence sources and review processes. IndyKite helps implement data governance, runtime policy enforcement and decision traceability. Legal counsel should confirm how the Act applies to each organization and use case.
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