Regulatory Compliance for Data Platforms: DORA, NIS2, EU AI Act, and Data Act
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Key Insights
- European digital regulation is reshaping how data platforms must manage risk, security, and interoperability.
- This module covers the Digital Operational Resilience Act (DORA) for ICT risk management, the NIS2 Directive for cybersecurity, the EU AI Act's high-risk classification and conformity assessment, and the EU Data Act's data interoperability requirements.
- Practical implementation guidance covers ICT risk registers, penetration testing cadences, AI auditing frameworks, and 2025-2026 compliance deadlines including DORA enforcement from January 2025 and AI Act tiered rollout through 2026.
Overview
Building data platforms that meet regulatory compliance requirements is a complex challenge spanning multiple frameworks including GDPR, CCPA, SOX, HIPAA, and financial regulations like MiFID II and Basel III. Compliance must be engineered into the data architecture from the ground up, affecting how data is collected, stored, processed, audited, and deleted.
Key compliance requirements include data lineage for audit trails, access controls and encryption for data protection, retention and deletion capabilities for data lifecycle management, and consent tracking for personal data. Modern data platforms use column-level lineage, attribute-based access control (ABAC), and automated policy enforcement to meet these requirements at scale.
Key Concepts
- Audit Trail: Complete, immutable record of all data access and modifications for regulatory reporting and investigation.
- Attribute-Based Access Control: Fine-grained access control based on user attributes, data sensitivity labels, and contextual conditions.
- Data Residency: Requirements that data remain within specific geographic boundaries as mandated by local regulations.
- Right to Erasure: GDPR requirement enabling individuals to request deletion of their personal data from all systems.
- Data Protection Impact Assessment: A systematic process for evaluating privacy risks of new data processing activities.
Key Takeaways
- Compliance requirements must be engineered into data architecture from the beginning.
- Audit trails, access controls, and encryption are foundational compliance capabilities.
- Data residency and retention requirements vary significantly across jurisdictions.
- Automated policy enforcement scales compliance beyond manual processes.
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Data Lineage is a concept in architecture. In simple terms, Data Lineage covers architectural patterns for Data Engineering. This data engineering concept addresses key topics in the architectural patterns for data engineering domain. Also known as: data prove
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Data Governance is a concept in architecture. In simple terms, Data Governance covers architectural patterns for Data Engineering. This data engineering concept addresses key topics in the architectural patterns for data engineering domain. Also known as: data ca
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EU AI Act — High-Risk Classification is a concept in regulations. In simple terms, EU AI Act — High-Risk Classification covers regulatory frameworks in Data Engineering. This data engineering concept addresses key topics in the regulatory frameworks in data engineering domain. Also
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DORA — ICT Risk Management is a concept in regulations. In simple terms, DORA — ICT Risk Management covers regulatory frameworks in Data Engineering. This data engineering concept addresses key topics in the regulatory frameworks in data engineering domain. Also known as:
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NIS2 Directive — Cybersecurity Resilience is a concept in best practices. In simple terms, NIS2 Directive — Cybersecurity Resilience covers best practices in Data Engineering. This data engineering concept addresses key topics in the best practices in data engineering domain. Also known as:
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