GDPR Article 32 establishes one of the Regulation's most critical yet flexible obligations: the security of processing. This proactive, risk-based commitment is a fundamental element of a disciplined approach to data governance. Far from a simple checklist, this article demands that businesses protect personal data, mitigating risks, protecting corporate reputation, and ultimately, avoiding the penalties imposed by supervisory authorities.



This article delves into the requirements of Article 32, providing a practical guide on the technical and organizational measures (TOMs) that businesses must implement to ensure a level of security appropriate to the risk.



For a broader overview of the relationship between the regulation and data protection, you can consult our complete guide on cybersecurity and GDPR.




Understanding Article 32: The Risk-Based Approach



Article 32 does not prescribe a one-size-fits-all solution but requires data controllers and processors to assess the inherent risks of their operations. When deciding which security measures to apply, factors to consider include:


  • The state of the art: The available technology and its market maturity.

  • The costs of implementation: The necessary investment to deploy the measures.

  • The nature, scope, and purposes of processing: The type of data processed, its volume, and the purpose of the operation.

  • The likelihood and severity of the risk: The potential for a breach of individuals' rights and freedoms.


This flexibility allows a startup with limited resources to apply proportional security measures, while a large financial corporation implements cutting-edge security protocols.




Technical and Organizational Measures (TOMs): A Pillar of Security



Article 32 explicitly mentions a series of technical and organizational measures that must be implemented. These are categorized into two main groups.



Technical Measures



These measures focus on the technological infrastructure to protect data from physical or digital threats. They include:


  • Pseudonymization and Encryption: Techniques to make personal data not directly identifiable, either by separating identifiers (pseudonymization) or using mathematical algorithms (encryption).

  • Data Masking: A specific technique that involves hiding sensitive data with false yet realistic and functional values. This allows, for example, the use of fictitious data in testing environments while maintaining referential integrity and business rules, thus complying with Article 32 requirements without exposing real information.

  • System Resilience: The ability of data processing systems to withstand incidents, such as cyberattacks or technical failures, without compromising confidentiality, integrity, and availability.

  • Data Restoration: A clear process to quickly restore access to personal data in the event of an incident. This includes business continuity plans and auditable backups.



Organizational Measures



These measures focus on the processes, policies, and people within the organization. They are essential to complement technical solutions.


  • Access Control Policies: Procedures to ensure that only authorized personnel can access data, following the "principle of least privilege."

  • Staff Training: Continuous awareness and training so employees understand their data protection responsibilities.

  • Regular Audits and Assessments: An ongoing process of reviewing, testing, and evaluating the effectiveness of security measures to detect vulnerabilities and areas for improvement.




Accountability and Penalties for Non-Compliance



Article 32 is a key basis for imposing penalties by supervisory authorities. A failure to implement adequate security measures can lead to the loss or exposure of personal data, which in turn results in fines of up to 20 million euros or 4% of a company's annual global turnover. Therefore, the proper implementation and documentation of TOMs are crucial for demonstrating compliance.



Conclusion



GDPR Article 32 is a reminder that data security is not a static goal but a dynamic and continuous process. Integrating the right measures is vital to protect the organization, its customers, and to comply with legal requirements. The complexity of managing data in development and testing environments, where control is limited, presents a recurring risk that demands a modern and proactive approach.