Data Clean Rooms: Secure Data Sharing

This page is a translation of the original Portuguese text, provided for convenience. It deals with Brazilian law. If there is any discrepancy, the Portuguese version prevails. Read the original in Portuguese

Data clean rooms are emerging as a legal and technological solution for secure data sharing between organizations, and they call for rigorous due diligence to ensure compliance with the LGPD.

What Data Clean Rooms Are and Why They Matter for Data Protection

In recent years, the way companies share sensitive information has changed profoundly. Data clean rooms are controlled computing environments where two or more organizations can cross-reference and analyze their respective datasets and extract insights, without either party having direct access to the other’s raw information. The concept was born in digital advertising, but it has spread quickly to sectors such as health care, finance and insurance, and, naturally, to the legal practice of data due diligence.

In practical terms, a data clean room works as a neutral technical intermediary. Each participant uploads its data, in encrypted form, to this isolated environment, where pre-approved algorithms run the necessary analyses. Results are made available in aggregated or anonymized form, which prevents the re-identification of individual data subjects. This makes it possible to enable strategic collaborations between companies without violating the core principles of the Lei Geral de Proteção de Dados Pessoais (LGPD, Brazil’s General Personal Data Protection Law), such as purpose, necessity and data minimization in the processing of personal data.

For professionals who work in due diligence, understanding this technology is no longer optional. Mergers and acquisitions, commercial partnerships and customer-database sharing agreements now call for technical and legal assessments of how data moves between the parties. When we conduct a data due diligence, we must verify whether the sharing mechanisms used by the audited company meet current legal requirements, and data clean rooms increasingly form part of that infrastructure.

Legal Foundations of Data Sharing in Controlled Environments

The LGPD provides that sharing personal data between controllers must rest on specific legal bases, respect the rights of data subjects and ensure security measures proportionate to the risk involved. When you examine the use of data clean rooms from this perspective, it becomes clear that the technology alone does not replace legal compliance. It is a tool that makes it easier to comply with the law, but the contractual structure and governance policies must keep pace with the technical safeguards.

Data clean room technology does not replace legal compliance, but it does offer concrete mechanisms for data sharing that respects both business objectives and the rights of data subjects.

The purpose principle requires that shared data be used only for the purposes previously communicated to the data subject. In a well-configured data clean room, the permitted queries are defined before any processing takes place, which technically limits the possibility of misuse of the data. Even so, these query rules must be documented in contracts between the parties, with specific clauses on the types of analysis authorized, the retention periods for results and each participant’s responsibilities in the event of an incident.

The necessity principle, in turn, finds a natural ally in data clean rooms. Because only aggregated or anonymized results are extracted from the environment, we avoid excessive sharing of personal data. In practice, this feature significantly reduces the risk of improper exposure, but it is always advisable to document the proportionality analysis that justifies the volume and nature of the data loaded into the clean room.

Another key issue is the allocation of roles between controllers and processors. In many data clean room scenarios, both parties act as independent controllers of their respective datasets, while the technology provider acts as a processor. This arrangement calls for detailed Data Processing Agreements that specify each party’s obligations, incident response protocols and the audit mechanisms available.

Applicable Legal Bases

In most of the cases we review, data sharing in data clean rooms rests on the controller’s legitimate interest or on performance of a contract. When the operation involves sensitive data (such as health information or biometric data), the legal basis tends to be the data subject’s specific and prominent consent, which requires additional care in collecting and managing that consent. Regardless of the legal basis chosen, we recommend preparing a Data Protection Impact Report (Relatório de Impacto à Proteção de Dados Pessoais, or RIPD) whenever the sharing involves a significant number of data subjects or special categories of data.

Due Diligence in Data Clean Rooms: What We Check in Practice

When we conduct a due diligence focused on operations that use data clean rooms, we structure the analysis around three main areas: governance, technical security and regulatory compliance. Each area involves specific checks that let us map risks and identify gaps before they turn into real problems.

Data Governance

In the governance area, we check whether the organization has internal policies that regulate the use of data clean rooms, including criteria for approving new sharing partnerships, decision flows for which data may be loaded into the environment and processes for periodic review of the analyses performed. We also assess whether there is an effective data protection officer (DPO) and whether the records of processing operations are up to date and reflect the activities carried out in the clean room.

Another critical point is third-party management. Many data clean rooms are supplied by specialized technology companies, and the contracts signed with those providers must be evaluated. We check whether there are adequate confidentiality clauses, whether the provider is subject to independent audits and whether there are guarantees about the geographic location of the processing (especially relevant when international data transfers are involved).

Technical Security

In the security area, we analyze the technical measures implemented to protect data throughout its life cycle inside the data clean room. These include verifying encryption in transit and at rest, role-based access controls, audit logs of the operations performed, anomaly detection mechanisms and procedures for secure destruction of data once the analysis ends. We also assess whether the data clean room’s architecture uses techniques such as secure multiparty computation (MPC), differential privacy or trusted execution environments, which add extra layers of protection against re-identification attempts.

Regulatory Compliance

In the regulatory area, we map all the rules that apply to the operation. These may include the LGPD, sector-specific regulations (such as Central Bank rules for the financial sector or resolutions of the National Supplementary Health Agency for the health sector) and, in international operations, foreign laws such as the European General Data Protection Regulation (GDPR). We check whether the legal bases chosen for processing are appropriate, whether data subjects were properly informed about the sharing and whether the rights of access, correction and deletion can be effectively exercised even within the data clean room.

Legal Risks and Recommended Mitigation

Despite their clear advantages, data clean rooms do not eliminate every legal risk associated with data sharing. In practice, we see a few recurring vulnerabilities that deserve special attention during due diligence.

The first significant risk is indirect re-identification. Even when the results extracted from the data clean room are aggregated, certain combinations of variables can make it possible to identify individuals in some contexts, especially when dealing with small groups or very specific characteristics. To mitigate this risk, we recommend applying differential privacy techniques and setting minimum aggregation thresholds (for example, results involving fewer than a given number of data subjects should not be released).

The second risk is contractual inadequacy. Many companies implement data clean rooms under generic technology service contracts with no specific data protection clauses. This gap can lead to disputes over liability in the event of an incident and make it harder to demonstrate compliance to the ANPD (Brazil’s National Data Protection Authority). Mitigation involves drafting dedicated contractual instruments that address the particularities of sharing in a controlled environment.

The third risk concerns outdated impact assessments. Data clean rooms are dynamic environments, where new types of queries and new partners may be added over time. If the RIPD prepared at the start of the operation is not updated to reflect those changes, the organization may be operating outside the limits of the original risk assessment. We recommend reviewing impact reports periodically (at least every six months) and setting up automatic triggers for reassessment whenever there are significant changes in the scope of the sharing.

Trends and Outlook for the Brazilian Market

The Brazilian data clean room market is still at an early stage compared with markets such as the United States and Europe, but adoption of the technology by large companies is accelerating significantly, especially in retail, telecommunications and financial services. The consolidation of the LGPD as an effective regulatory framework, combined with the ANPD’s increasingly assertive enforcement, creates a favorable environment for solutions that allow data sharing with greater legal certainty.

On the regulatory front, we are closely following the discussions on specific rules for data sharing between processing agents, which could bring clearer guidance on the technical and organizational requirements. The expectation is that sector-specific rules will gradually incorporate references to data clean rooms as an accepted mechanism for secure sharing, which is likely to drive adoption and standardization of these solutions in Brazil.

For organizations that already use data clean rooms or plan to implement them, we recommend investing from the start in a robust governance structure that brings legal, technology and compliance teams together around clear policies and documented processes. Data due diligence is not a one-time event but an ongoing process that must keep pace with the evolution of both the technology and the regulatory environment.

This topic is directly related to big data in social security, which we address in a dedicated article.

To round out your reading, see also the firm’s article on data mapping.

As informações deste artigo são de caráter informativo e não substituem consulta jurídica individualizada. Cada caso possui particularidades que exigem análise específica.