Managing Data Compliance Without Data Duplication or Legal Risks
In today’s data enabled, AI-ready world, organisations face the difficult challenge of leveraging data effectively while staying compliant with data privacy laws and trying to do the right thing.
The key is to access critical insights without needlessly and carelessly duplicating and copying sensitive information and inadvertently violating regulations like GDPR, CCPA, HIPAA, and other relevant industry standards. This is forgetting the more practical points – like dark data and operational efficiency issues that ineffective data management can cause.
Best Practices for Data Compliance
Implement Data Virtualisation for Secure Access
Data virtualisation allows real-time access to information without physical duplication, reducing risk exposure by keeping a single source of truth and ensuring data consistency across departments.
For example, a financial firm can allow analysts to query customer transactions without duplicating raw data across various locations, ensuring compliance with financial regulations like SOX and GDPR.
Leverage Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC)
RBAC and ABAC enforce strict data access policies based on job roles and contextual conditions, preventing unauthorized personnel from accessing sensitive information and reducing the temptation to create duplicate datasets.
For example, a healthcare provider can configure ABAC to allow doctors access to patient records only for their assigned patients, ensuring compliance with HIPAA without unnecessary data replication.
Implement Secure Data Masking and Tokenisation
Data masking and tokenisation help protect sensitive information by replacing it with fictitious yet realistic data or non-sensitive equivalents.
For example, a retail company processing payment data can tokenize credit card details, ensuring compliance with PCI DSS while enabling fraud detection without handling actual card numbers.
Establish Data Lineage and Audit Trails
Data lineage and audit trails provide full visibility into who accessed what data and when, making compliance audits easier and detecting unauthorized data movement.
For example, government agency using audit trails can prove compliance with FOIA and GDPR, ensuring that citizen data is handled lawfully without replicating entire datasets for audit purposes.
Use Privacy-Preserving Computation Techniques
Privacy-enhancing technologies (PETs) allow organizations to analyse data without exposing raw information. Techniques like homomorphic encryption, federated learning, and secure multi-party computation enable AI models to train across multiple locations without transferring sensitive data.
For example, a pharmaceutical company conducting clinical trials can use federated learning to analyse patient data without ever centralizing or duplicating sensitive records.
Automate Compliance with Data Governance Frameworks
A data governance framework ensures compliance by defining clear data retention and deletion policies, automated classification of sensitive vs. non-sensitive data, and compliance-driven workflows that alert teams about potential violations.
For example, a multinational corporation can configure automated policies to delete old customer records after a set period, ensuring CCPA compliance without unnecessary copies lingering in backup systems.
Case Study: How Pivotl Implemented Data Compliance on Azure
The Pivotl team worked with a large financial services organisation managing sensitive customer and financial data, needed to comply with GDPR, CCPA, and industry-specific regulations while ensuring their data remained accessible for analytics and AI-driven insights.
Pivotl designed and implemented a compliance-driven data architecture on Azure by leveraging:
Azure Data Virtualisation and Synapse Analytics: Allowed secure access to data without physically copying it across environments.
Azure RBAC/ABAC for Data Governance: Implemented fine-grained access controls, ensuring only authorized users accessed sensitive data.
Data Masking and Encryption with Azure Key Vault: Applied dynamic data masking for PII and financial data, ensuring compliance with PCI DSS and GDPR.
Audit Logging with Azure Monitor and Sentinel: Set up continuous audit logs and data lineage tracking to ensure full visibility.
Federated Learning for AI-Driven Insights: Enabled AI-driven analytics without transferring sensitive customer data.
Key Lessons Learned from Azure Implementation
Managing Access Controls Was More Complex Than Expected: Configuring RBAC, ABAC, and Microsoft Purview across multiple data sources required fine-tuning to avoid excessive restrictions or gaps in security.
Performance Trade-offs Between Virtualisation and Compliance: Query performance was sometimes slower compared to traditional warehousing, requiring optimisation of indexing and complex caching strategies.
Encryption and Masking Can Add Latency: Applying dynamic masking and encryption at scale increased query times, solved by implementing selective masking and caching encrypted datasets.
Automating Compliance Saves Time and Effort: Automating compliance monitoring using Azure Policy & Sentinel reduced manual effort and risk.
Terraform Can Sometimes Get in The Way: When you have a lot of elements of a data platform (buckets, some servers, pipelines etc.,), if each one is designed as a stack in Bicep/CDK they can be made very configurable. At the time this was a better approach to infrastructure-as-code than Terraform, which was relatively unreliable.
Smart Data Management = Compliance + Efficiency
Managing data without unnecessary duplication is not just a compliance requirement – it’s a best practice for security, operational efficiency, and trust. By leveraging data virtualisation, role-based access, encryption, and privacy-enhancing technologies, businesses can extract valuable insights while staying compliant.
Data compliance is best achieved as part of a strategic approach to data management as compliance features can be fully integrated and enabled within the platform to minimise data movement. However, in more complex set ups, data compliance can operate as a standalone layer that governs data from where it resides without any data movement being required.
