The National Security Framework (ENS), under RD 311/2022, translates the legal requirements of the GDPR into enforceable technical controls.

For Public Administrations and their providers, data anonymization is the strategic measure that guarantees data irreversibility, ensuring that information processed in non-production environments remains outside the scope of sanctions for personal data processing.



Key ENS Measures Covered by Anonymization



1. Environment Separation and Secure Provisioning (Measure [op.pl.4])



The ENS prohibits the use of real information in development, testing, or training environments. To comply with this strict separation, it is necessary to implement data transformation workflows that intercept information before it persists in the target environment.



By provisioning anonymized data to Staging or QA, technical teams are guaranteed to work with datasets that maintain referential integrity. This allows applications to function correctly (preserving relationships between tables) without leaving any trace of Personally Identifiable Information (PII) in secondary systems.



2. Guarantee of Irreversibility (Measure [mp.si.1])



Data protection under the ENS requires a robust anonymization process. The AEPD (Spanish Data Protection Agency) warns that superficial field deletion or simple masking is insufficient if a risk of re-identification exists.



The use of advanced perturbation algorithms and dictionary substitution ensures the result is resistant to inference attacks. By delivering pre-transformed data, the Privacy by Design principle is met, drastically mitigating the impact of any potential security incident in non-production environments.




Technical Solvency and Audit Evidence



Passing a Medium or High-level ENS audit requires demonstrating that controls are automated, repeatable, and auditable.


  • Sovereignty and Automation: In hybrid cloud infrastructures, anonymizing data at the source ensures sovereignty and security in transit, preventing sensitive data from reaching the destination environment unprotected.

  • Compliance Traceability: Unlike manual processes, a specialized platform generates detailed logs. This evidence allows the Information Security Officer (RSI) to demonstrate effective compliance to the National Cryptologic Center (CCN) auditor.




Data Anonymization Use Cases in Public Administration



Implementing anonymization processes allows ENS compliance to translate into real operational improvements across various Public Sector scenarios:


  • Third-Party Collaboration: Delivering anonymized datasets to external consultants for development or support, eliminating the risk of access to real citizen data.

  • Transparency and Open Data: Publishing statistical datasets that comply with transparency laws without violating the anonymity of individuals.

  • Government Big Data: Analyzing trends in healthcare or social services using high-fidelity data that has been decoupled from its personal origin.




Innovation under the ENS Framework



ENS compliance should not be perceived as an obstacle to innovation. Tools like Gigantics enable public entities and their technological partners to automate data anonymization in an agile and auditable manner.


Delegating this technical complexity to a specialized platform ensures system integrity. If you are evaluating options, you can consult our comparison of the best data anonymization tools on the market.