Brazil's LGPD: Anonymization, Pseudonymization, and Access Requests

Dec 13, 2023
Share this post
Sharing to FacebookSharing to LinkedInSharing to XSharing to Email

Leia em português aquiThe Lei Geral de Proteção de Dados (LGPD), Brazil's answer to data privacy, determines the rules organizations that handle personal data have to follow, unless they anonymize the data. This article delves into data anonymization, pseudonymization and what that means for data processing activities, as well as the LGPD’s stringent response time for access requests. We compare these selected aspects of the LGPD with the GDPR, and explore how technologies like those from Private AI can help organizations render data anonymized or pseudonymized efficiently, and comply with the onerous access request obligations.

Understanding Anonymization under the LGPD

  1. Definition and Scope: Anonymized data is defined as data related to a data subject who cannot be identified, considering the use of reasonable and available technical means at the time of the processing. The LGPD also defines anonymization as the use of reasonable and available technical means at the time of treatment, by which a given data loses the possibility of direct or indirect association with an individual. Lastly, the LGPD says that anonymized data shall not be considered personal data, except when the anonymization process to which it was submitted is reversed, using its own means, or when, with reasonable efforts, it may be reversed. This definition aligns somewhat with the GDPR, where anonymized data is also considered to be outside the scope of data protection laws because the data subject is not identifiable.
  2. Usage of Anonymized Data: Under the LGPD, once data is anonymized, it is no longer considered personal data and falls outside the act's scope. This means organizations can use anonymized data freely without adhering to the privacy protections and rights obligations required for personal data. It offers a pathway for analytics, research, and other data-driven activities while maintaining compliance. The language of the law is not terribly strict on this point. For example, it says that processing of personal data shall only be carried out under the following circumstances, one of which reads: for carrying out studies by research entities, ensuring, whenever possible, the anonymization of personal data. The provision for the processing of sensitive personal data reads identically in this regard.
  3. Comparative Analysis with GDPR: The GDPR and LGPD share similarities in their approach to anonymization. Both consider anonymized data as non-personal, freeing it from the respective data protection regulations. However, the GDPR is more explicit about the irreversibility of anonymization, implying a higher standard for the process. An interesting detail in the LGPD is that it explicitly excludes data from the definition of anonymized data when they are used to formulate behavioral profiles of a particular natural person, if that person is identified. It is unclear what may have prompted this exclusion, since it is rather obvious that the definition of anonymized data would not apply in this scenario. Let’s take it as a signaling provision that emphasises the sensitivity of personal profiles.

The Role of Pseudonymization

  1. LGPD’s Stance on Pseudonymization: Pseudonymization under LGPD involves processing personal data in such a way that it can no longer be attributed to a specific data subject without the use of additional information, which must be kept separately by the controller in a controlled and secure environment. This process, while a valuable security measure, does not change the data's status as personal under LGPD, unlike anonymization.
  2. Impact on Compliance: Pseudonymized data still requires adherence to the LGPD’s provisions. In fact, it is considered a recommended security measure when processing personal data for public health studies, an oddly narrow scope for this useful technique, by the way.

The Significance of Rapid Response to Access Requests

If the use case for processing the data does not allow for anonymization, data subjects have a right to access the information that is held by organizations governed under the LGPD. The law mandates a short response time of 15 days, compared to 30 under the GDPR, for data subject access requests, emphasizing the need for efficient data management systems. Organizations must be prepared to promptly identify, access, and compile personal data in response to these requests.

Private AI’s Contribution to LGPD Compliance

  1. Facilitating Efficient Data Mapping: Private AI's technology can swiftly identify and categorize over 50 entities of personal data, a necessity for complying with LGPD's access request deadlines. Particularly where unstructured data, such as free text is concerned, this can be a time-consuming process, depending on the amount of data in an organization’s system.
  2. Enhancing Anonymization Processes: By utilizing advanced algorithms, optimized for various file types, Private AI can help organizations effectively anonymize data, ensuring it falls outside of the LGPD's purview.
  3. Supporting Multilingual and Context-Sensitive Processing: Private AI’s ability to handle diverse languages and contextual nuances aligns with the LGPD’s unparalleled territorial scope, likely capturing great linguistic diversity. The LGPD applies, unlike any other privacy law, not only to processing carried out in Brazil but also to processing related to providing goods or services to individuals in Brazil. The LGPD applies where “the personal data being processed were collected in the national territory” and explains that “data collected in the national territory are considered to be those whose data subject is in the national territory at the time of collection.” In summary, if an organization processes personal data related to individuals in Brazil, the LGPD applies regardless of the origin of that data. It would come in very handy if the tool used for personal data detection and redaction supported 52 languages!

Conclusion

Brazil's LGPD places significant emphasis on the proper handling of personal data with much more obligations than covered here. This article highlighted that anonymization offers a gateway for organizations to utilize data without the constraints of the LGPD, provided the process is irreversible in light of reasonable measures taken. Additionally, the globally most stringent timeline for responding to access requests can likely not be met if attempted manually, given the vast amount of data many companies process today. In this context, Private AI's technology emerges as a critical tool, enabling organizations to navigate these complex requirements efficiently and effectively, enhancing data privacy and security in Brazil's digital ecosystem. Try it on your own data using our web demo or get a free API key here.

Data Left Behind: AI Scribes’ Promises in Healthcare

Why is linguistics essential when dealing with healthcare data?

Why Health Data Strategies Fail Before They Start

Private AI to Redefine Enterprise Data Privacy and Compliance with NVIDIA

EDPB’s Pseudonymization Guideline and the Challenge of Unstructured Data

HHS’ proposed HIPAA Amendment to Strengthen Cybersecurity in Healthcare and how Private AI can Support Compliance

Japan's Health Data Anonymization Act: Enabling Large-Scale Health Research

What the International AI Safety Report 2025 has to say about Privacy Risks from General Purpose AI

Private AI 4.0: Your Data’s Potential, Protected and Unlocked

How Private AI Facilitates GDPR Compliance for AI Models: Insights from the EDPB's Latest Opinion

Navigating the New Frontier of Data Privacy: Protecting Confidential Company Information in the Age of AI

Belgium’s Data Protection Authority on the Interplay of the EU AI Act and the GDPR

Enhancing Compliance with US Privacy Regulations for the Insurance Industry Using Private AI

Navigating Compliance with Quebec’s Act Respecting Health and Social Services Information Through Private AI’s De-identification Technology

Unlocking New Levels of Accuracy in Privacy-Preserving AI with Co-Reference Resolution

Strengthened Data Protection Enforcement on the Horizon in Japan

How Private AI Can Help to Comply with Thailand's PDPA

How Private AI Can Help Financial Institutions Comply with OSFI Guidelines

The American Privacy Rights Act – The Next Generation of Privacy Laws

How Private AI Can Help with Compliance under China’s Personal Information Protection Law (PIPL)

PII Redaction for Reviews Data: Ensuring Privacy Compliance when Using Review APIs

Independent Review Certifies Private AI’s PII Identification Model as Secure and Reliable

To Use or Not to Use AI: A Delicate Balance Between Productivity and Privacy

To Use or Not to Use AI: A Delicate Balance Between Productivity and Privacy

News from NIST: Dioptra, AI Risk Management Framework (AI RMF) Generative AI Profile, and How PII Identification and Redaction can Support Suggested Best Practices

Handling Personal Information by Financial Institutions in Japan – The Strict Requirements of the FSA Guidelines

日本における金融機関の個人情報の取り扱い - 金融庁ガイドラインの要件

Leveraging Private AI to Meet the EDPB’s AI Audit Checklist for GDPR-Compliant AI Systems

Who is Responsible for Protecting PII?

How Private AI can help the Public Sector to Comply with the Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024

A Comparison of the Approaches to Generative AI in Japan and China

Updated OECD AI Principles to keep up with novel and increased risks from general purpose and generative AI

Is Consent Required for Processing Personal Data via LLMs?

The evolving landscape of data privacy legislation in healthcare in Germany

The CIO’s and CISO’s Guide for Proactive Reporting and DLP with Private AI and Elastic

The Evolving Landscape of Health Data Protection Laws in the United States

Comparing Privacy and Safety Concerns Around Llama 2, GPT4, and Gemini

How to Safely Redact PII from Segment Events using Destination Insert Functions and Private AI API

WHO’s AI Ethics and Governance Guidance for Large Multi-Modal Models operating in the Health Sector – Data Protection Considerations

How to Protect Confidential Corporate Information in the ChatGPT Era

Unlocking the Power of Retrieval Augmented Generation with Added Privacy: A Comprehensive Guide

Leveraging ChatGPT and other AI Tools for Legal Services

Leveraging ChatGPT and other AI tools for HR

Leveraging ChatGPT in the Banking Industry

Law 25 and Data Transfers Outside of Quebec

The Colorado and Connecticut Data Privacy Acts

Unlocking Compliance with the Japanese Data Privacy Act (APPI) using Private AI

Tokenization and Its Benefits for Data Protection

Private AI Launches Cloud API to Streamline Data Privacy

Processing of Special Categories of Data in Germany

End-to-end Privacy Management

Privacy Breach Reporting Requirements under Law25

Migrating Your Privacy Workflows from Amazon Comprehend to Private AI

A Comparison of the Approaches to Generative AI in the US and EU

Benefits of AI in Healthcare and Data Sources (Part 1)

Privacy Attacks against Data and AI Models (Part 3)

Risks of Noncompliance and Challenges around Privacy-Preserving Techniques (Part 2)

Enhancing Data Lake Security: A Guide to PII Scanning in S3 buckets

The Costs of a Data Breach in the Healthcare Sector and its Privacy Compliance Implications

Navigating GDPR Compliance in the Life Cycle of LLM-Based Solutions

What’s New in Version 3.8

How to Protect Your Business from Data Leaks: Lessons from Toyota and the Department of Home Affairs

New York's Acceptable Use of AI Policy: A Focus on Privacy Obligations

Safeguarding Personal Data in Sentiment Analysis: A Guide to PII Anonymization

Changes to South Korea’s Personal Information Protection Act to Take Effect on March 15, 2024

Australia’s Plan to Regulate High-Risk AI

How Private AI can help comply with the EU AI Act

Comment la Loi 25 Impacte l'Utilisation de ChatGPT et de l'IA en Général

Endgültiger Entwurf des Gesetzes über Künstliche Intelligenz – Datenschutzpflichten der KI-Modelle mit Allgemeinem Verwendungszweck

How Law25 Impacts the Use of ChatGPT and AI in General

Is Salesforce Law25 Compliant?

Creating De-Identified Embeddings

Exciting Updates in 3.7

EU AI Act Final Draft – Obligations of General-Purpose AI Systems relating to Data Privacy

FTC Privacy Enforcement Actions Against AI Companies

The CCPA, CPRA, and California's Evolving Data Protection Landscape

HIPAA Compliance – Expert Determination Aided by Private AI

Private AI Software As a Service Agreement

EU's Review of Canada's Data Protection Adequacy: Implications for Ongoing Privacy Reform

Acceptable Use Policy

ISO/IEC 42001: A New Standard for Ethical and Responsible AI Management

Reviewing OpenAI's 31st Jan 2024 Privacy and Business Terms Updates

Comparing OpenAI vs. Azure OpenAI Services

Quebec’s Draft Regulation Respecting the Anonymization of Personal Information

Version 3.6 Release: Enhanced Streaming, Auto Model Selection, and More in Our Data Privacy Platform

Brazil's LGPD: Anonymization, Pseudonymization, and Access Requests

LGPD do Brasil: Anonimização, Pseudonimização e Solicitações de Acesso à Informação

Canada’s Principles for Responsible, Trustworthy and Privacy-Protective Generative AI Technologies and How to Comply Using Private AI

Private AI Named One of The Most Innovative RegTech Companies by RegTech100

Data Integrity, Data Security, and the New NIST Cybersecurity Framework

Safeguarding Privacy with Commercial LLMs

Cybersecurity in the Public Sector: Protecting Vital Services

Privacy Impact Assessment (PIA) Requirements under Law25

Elevate Your Experience with Version 3.5

Fine-Tuning LLMs with a Focus on Privacy

GDPR in Germany: Challenges of German Data Privacy (Part 2)

Comply with US Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence using Private AI

How to Comply with EU AI Act using PrivateGPT

Navigating the Privacy Paradox: A Guide to Ethical Fine-Tuning of Large Language Models

Adding Privacy to LangChain with Private AI