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The Do’s and Don’ts of DSARs: A Practical Guide for Responding to Data Subject Access Requests

DSAR, EU, GDPR, State Privacy Laws, United States , , , , , ,
Handling data subject access requests (DSARs) isn’t as easy as ticking a compliance checkbox. It can be a test of an entity’s data organization, internal communication, and understanding of legal requirements. Between navigating jurisdictional nuances and meeting strict deadlines, the DSAR response process can quickly unravel without a clear plan. In this guide, we suggest best practices for handling and responding to DSARs, along with tips and common pitfalls to avoid when planning effective responses.

1.    Understand the Individual’s Ask

Under international data privacy laws, including those in the US and EU, individuals may have rights over the personal data collected about them by covered entities. The way individuals generally actualize those rights are through DSARs submitted to the relevant entities. These rights can include, but are not limited to:
  • Accessing Data: Individuals may request access to all or specific categories of their personal data.
  • Ceasing Data Processing: Individuals may request the entity stop processing their personal data.
  • Data Correction or Deletion: Individuals may request rectification of inaccurate or outdated personal data or even request the deletion of their personal data.
  • Processing Information: Individuals may request what their personal data is used for and why.
  • Portability: Individuals may request to receive a copy of their personal data in a portable format.
When an individual makes a request to exercise one of these rights, the entity must then respond to the request within a set time frame determined by the applicable law. These time frames differ between applicable laws, so the first step is ensuring you know the appropriate time frame to apply. Who can submit a DSAR? DSARs may be submitted by individuals whose data is processed by entities under the scope of laws like the GDPR and US state privacy laws. Depending on the jurisdiction, DSARs may also be submitted by employees of the covered entity or by agents appointed by the individual and authorized to submit DSARs on the individual’s behalf. Why are DSARs important? DSARs allow individuals to determine what information a covered entity holds about them, how it’s being used, and why it is being processed. In short, they empower individuals to understand and exert some control over their personal data. Additionally, DSARs serve as a tool to confirm that covered entities are upholding their promises: by using these requests, individuals can check whether entities are adhering to both privacy laws and customer privacy notices. This allows individuals to better hold entities accountable for lawful data processing.

2.    Build A Response Team

Given the complexity of modern data systems, internal collaboration is essential when handling DSARs. Clear communication helps ensure DSARs are handled effectively—especially for more comprehensive requests, like deleting or accessing an individual’s data. To build your response team, start by identifying key players. Privacy officers can help oversee legal and regulatory compliance, data experts can help retrieve and process data securely, and communication teams can help draft clear responses to requests and questions. While the specific structure of each team will vary based on the covered entity’s size and complexity, every member of the team should understand the DSAR requirements and specific responsibilities, and get proper training based on their role. Do: Train Your Team       Training is critical to help every member of the team understand the importance of DSARs and their role in maintaining compliance. This isn’t about knowing the legal jargon—each team member should be able to recognize these requests (even if worded in a vague or informal way) and how to execute the steps required to meet deadlines. Since each DSAR is unique, teams should also have a clear point of contact for guidance and next steps if there is any confusion. Don’t: Delay Decisions Effective responses generally take effective planning. Because of the tight DSAR response deadlines imposed by applicable laws, covered entities should plan for these requests before they arrive. By defining clear rules, covered entities can avoid last-minute confusion and chaos when responding to DSARs.

3.    Prepare A Playbook

The regulatory landscape governing DSARs is far from uniform. Because each law may have its own requirements and response timeline, it is essential to understand jurisdiction-specific obligations. A playbook is a simple way to address these obligations in one place and guide the response team through a step-by-step process. To create a playbook, consider:
  • Legal scope: Identify applicable laws based on where the entity operates and whose personal data they process.
  • Verification requirements: Confirm the verification requirements, if any, under each law to determine what steps are needed to confirm the identity of the individual submitting the DSAR.
  • Data retrieval methods: Determine what tools and workflows are needed to locate and compile data efficiently, and how this information may be transmitted to the individual, if necessary.
  • Template responses: Draft standardized responses for anticipated outcomes, like fulfillment or denial of requests, or requests for additional information.
  • Escalation plans: Provide guidance for handling complex requests.
Playbooks should be regularly reviewed to reflect changes in regulations or operational processes. Do: Note the Nuances of Each Law Laws that provide individuals with rights over their personal data commonly include exemptions, such as data that is covered by other laws. Double-check and note these requirements for each jurisdiction and ensure that the playbook is marked in a way that users can easily understand it. Don’t: Forget to Customize Using the same strategy for every DSAR risks a misstep in responses. Privacy laws are often unique, and failing to adapt to these nuances can lead to delays, incomplete responses, or even regulatory penalties. By making your playbook specific to both your entity’s needs and the requirements of each jurisdiction, you are better preparing your team to handle DSARs.

4.    Respond Effectively

Most data privacy laws require a response within a certain time frame from when the request was received. In other words, once a DSAR is received, a clock usually starts ticking. We suggest the following steps as a starting place for a well-executed response, but your steps should be tailored to the applicable legal requirements:
  1. Acknowledge the Request: Confirm the request and provide a clear timeline for how the request will be handled.
  2. Verify the Identify (as needed): Ensure the individual’s identity is confirmed, if required by the relevant laws.
  3. Locate and Collect Data: Collaborate across departments as needed to gather the relevant information.
  4. Review Data for Exceptions: Identify data that may be exempt from disclosures or require redaction, like data that pertains to another individual.
  5. Respond Clearly: Deliver the response in a clear, accessible format with an explanation of how that response was arrived at.
  6. Record and Learn: Maintain detailed records for accountability and review the process regularly.
 Do: Build a Feedback Loop    The best way to learn is by doing. After developing your playbook, perform a trial exercise to ensure your communication is streamlined and a test request is handled as expected. Then, talk to your team to review what went well and what improvements are needed. By viewing this process as iterative, with modifications and refinements made along the way, the DSAR response team can effectively grow and shift with the volume of requests or any regulatory changes. Don’t: Overlook Redaction and Exemptions Redaction and exemptions can easily be overlooked, but neglecting these steps can lead to non-compliance, or even a breach. Always double-check any information before it is disclosed and verify that all information is accounted for and handled appropriately.   While typically seen as a compliance obligation, DSARs can also present an opportunity for entities to demonstrate data privacy and transparency. Each DSAR is a chance to refine operations, and with a capable response team and a detailed playbook, entities can approach the process with a better understanding of compliance.
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An image of the flag of Europe, which consists of twelve golden stars forming a circle on a blue field.

EDPB Opinion on AI Models and GDPR Principles: Key Takeaways

AI & Algorithms, EEA & UK, EU, GDPR , , , , ,
In December 2024, the European Data Protection Board (EDPB) issued an Opinion in response to a request from the Irish supervisory authority, focusing on the application of GDPR principles in the context of AI models. The Irish supervisory authority posed three specific questions:
  1. When and how can an AI model be considered “anonymous”?
  2. What is the appropriateness of legitimate interest as a legal basis for AI deployment and development?
  3. What are the consequences of unlawful processing of personal data on subsequent operations of the AI model?
Through its answers, the EDPB provided key guidance on how AI models interact with fundamental rights to privacy and data protection established in the GDPR.

Anonymous AI Models

According the EDPB, “[f]or a model to be anonymous, it should be very unlikely 1) to directly or indirectly identify individuals whose data was used to create the model, and 2) to extract such personal information from the model through queries.” While anonymous data can help mitigate privacy concerns, it does not automatically make the AI model completely exempt from GDPR compliance. When a model is claimed to be anonymous, supervisory authorities will evaluate the claims of anonymity on a case-by-case basis, considering “all the means likely to be used” by the controller or a user. The Opinion states that supervisory authorities should review the documentation provided by the controller when assessing if the model is truly anonymous. The EDPB outlines methods that the controller may use to demonstrate anonymity, which may include: 1) reducing the amount of personal data used during training, 2) taking steps to ensure this data cannot be identified, and 3) utilizing technical safeguards to prevent data extraction from the AI model using prompts or queries. Key Takeaway: If a business claims an AI model relies on anonymous data, the claims of anonymity should be substantiated on a case-by-case basis with sufficient evidence and documentation. To do this, businesses with allegedly anonymous AI models may need to implement technical measures to limit the collection of data, reduce the likelihood of data being identifiable, protect against that data being extracted by users during deployment, and create documentation capable of demonstrating these efforts.

Legitimate Interest as a Legal Basis

Under the GDPR, a legitimate interest may constitute a legal basis for companies to process personal data when they have a justifiable reason to do so (beyond obtaining consent). However, the legitimate interest should be balanced against the data subject’s rights and interests, which requires careful consideration and justification when processing information from data subjects. The Opinion provides a framework to assess if a legitimate interest can be a valid legal basis for processing personal data in AI development and deployment. The framework is comprised of a three-step test:
  1. Identify the legitimate interest pursued by the controller;
  2. Assess the necessity of the processing for purposes of the legitimate interest; and,
  3. Balance the legitimate interests against the rights and freedoms of the data subjects.
When conducting this test, the controller should be careful to identify an interest that is lawful, clearly articulated, and non-speculative. For example, a legitimate interest may be to develop an AI model’s conversational agent or to improve threat detection in an information system. The controller should also adhere to GDPR data minimization principles, which state that the processing activities must be proportionate and in line with only what is necessary to achieve the legitimate interest. Finally, controllers should conduct a nuanced balancing test. This test considers the unique circumstances of each case, which may include the data subject’s interest in retaining control over their data, personal benefits, or socioeconomic interest. The Opinion notes, the more precisely an interest is defined in relation to the purpose of the processing, the more precise the estimation of benefits and risks will be. By employing this framework, developers and deployers should be able to decrease the likelihood that their AI models are disproportionately infringing on individual privacy rights and better align their AI practices with GDPR requirements. Key Takeaway: The three-step analysis, according to the Opinion, is crucial to improving compliance for organizations relying on legitimate interest as a legal basis for processing in AI development or deployment. Organizations relying on legitimate interest in this AI context should review their processing activities to determine whether they are proportionate, transparent, and aligned with GDPR principles—like data minimization—to justify the reliance on legitimate interest as a legal basis for processing.

Consequences of Unlawful Processing

The Opinion notes that supervisory authorities enjoy discretionary powers to investigate and assess violations, and they can choose appropriate remedial measures based on the context of the case. However, the EDPB also provides guidance for the supervisory authorities, based on three scenarios.
  1. In the first scenario, personal data is retained in the AI model. The Opinion states that supervisory authorities will need to consider the surrounding circumstances of the AI model to determine if the development and deployment phases of the model involve different legitimate purposes for processing. If so, each should be examined separately.
  2. In the second scenario, personal data is retained in the model and is processed by another controller during deployment. In this instance, the supervisory authorities should determine if the deploying controller conducted an appropriate assessment to demonstrate accountability with Articles 5(1)(a) and 6 of the GDPR. This assessment should show that the AI model was not developed by unlawfully processing personal data.
  3. In the final scenario, a controller unlawfully processes personal data to develop the AI model, and then anonymizes the data before processing it in the context of deployment. The Opinion states that, if it can be demonstrated to the supervisory authorities that the deployment of the AI model does not entail the processing of personal data, then the GDPR does not apply. Therefore, the unlawfulness of the initial processing in development should not impact the deployment operation of the model.
While supervisory authorities do have substantial discretion in oversight of processing activities, the scenarios highlighted by the EDPB show that the development and deployment phases, while connected, may need to be evaluated independently. Key Takeaway: Organizations should proactively ensure compliance at both the development and deployment stages of an AI model. Supervisory authorities will likely use the above examples as guidance, emphasizing the important of demonstrating lawful practices through each stage of the model. The EDPB’s Opinion is an important guide for organizations navigating the intersection of AI and data privacy law. By addressing issues around anonymous AI models, legitimate interest, and lawful processing in development and deployment stages, the Opinion emphasizes responsible AI development. As AI technologies continue to advance, businesses should be aware of the ways supervisory authorities are overseeing their AI models. The insights provided by the EDPB provide a foundation to help businesses to advance and develop new AI models, while also helping to safeguard and protect the rights of individuals.
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Metaverse Law in Orange County Lawyer Magazine

AI & Algorithms, CCPA, Cybersecurity, GDPR, Health Data, State Privacy Laws , , , , , , ,
The January 2025 edition of Orange County Lawyer magazine features an article written by Metaverse Law’s Lily Li. Read “AI and Machine Learning in Drug Development and Clinical Trials” below or in Orange County Lawyer magazine.
[Originally published as a Feature Article: AI and Machine Learning in Drug Development and Clinical Trials, by Lily Li, in Orange County Lawyer Magazine, January 2025, Vol. 67 No.1, page 28.]   AI and Machine Learning in Drug Development and Clinical Trials by Lily Li   In 2013, sleep medication zolpidem (Ambien, Ambien CR, and Edluar) swept headlines. Marie Claire reported on an alarming and suspicious rise in users experiencing irrational eating, gambling, and even “sleep-driving” while in a hypnotic trance—waking with no memories of their actions.[1] In several cases, women arrested and convicted for driving under the influence contested their convictions, arguing that they were not liable for these undisclosed drug-related side effects. At the same time, several clinical studies suggested that women metabolized zolpidem differently from men. By reviewing existing literature, Japanese researchers out of Shimane University identified 40% higher concentrations of zolpidem in women than men following use, and higher rates of visual hallucinations and sensory distortions.[2] The FDA released a safety advisory, warning users of the risks of “next-morning impairment” for the use of Ambien and related drugs.[3] In addition, the FDA took the unusual step of recommending a 50% cut in the dosage for women. When asked about the change, an FDA director told ABCNews.com: “The changes are different in women and men . . .We don’t understand why yet, but women are more susceptible to next-morning impairment.”[4] Yet, a decade later, the evidence supporting different zolpidem dosages for women and men is unclear.[5] In part, this is due to the lack of research surrounding sex differences in drug impact and drug treatment, as well as substantial gaps in the inclusion of women in clinical studies. From 1977 to 1993, FDA policy recommended excluding women of childbearing potential from Phase 1 and early Phase II drug trials.[6] Even after this policy was removed in 1993, industry fears remained with respect to drug interactions with pregnancy. This episode with zolpidem raised several concerns in the drug development and clinical trial process:
  • How do we recruit representative candidates for drug trials?
  • How do we ensure the quality and availability of datasets for clinical research?
  • How do we measure potential impacts of drug dosing on different populations?
  • What are the legal implications for failing to address appropriate drug doses?
  AI and ML to the Rescue? Now that artificial intelligence is being used in research and development, one wonders: Can artificial intelligence (AI) and machine learning (ML) reduce bias and risks during drug development? Or will it create new legal risks due to bias, privacy intrusions, and lack of transparency? The FDA released a discussion paper on AI, Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products, to discuss potential regulatory frameworks to address the use of AI and ML.[7] In this discussion paper, the FDA released a set of fascinating case studies into existing research and uses of AI in the clinical trial process. Several of these case studies are discussed below, as well as an analysis of their potential impact on the zolpidem example.
  1. Recruitment. According to the FDA, “AI/ML is being used to mine vast amounts of data, such as data from clinical trial databases, trial announcements, social media, medical literature, registries, and structured and unstructured data in EHRs [electronic health records], which can be used to match individuals to trials (Harrer, 219 Shah, Antony, & Hu, 2019).” In this manner, researchers can combine huge quantities of publicly available data and individual health data from prior research to identify participants with certain medical conditions (or lack of adverse conditions) for investigational treatments. For zolpidem, the use of AI/ML may have been able to identify a much broader list of participants for initial clinical testing, making it easier to assess and identify adverse reactions.
  2. Selection and Stratification of Trial Participants. In addition to initial recruitment, AI/ ML has the capability improve intake, selection, and classification of clinical trial participants. Based on baseline characteristics selected by the researchers, such as prior clinical data, and vitals/labs taken during intake, predictive algorithms can help identify high-risk participants.[8] These groups can then be randomized and then subject to more strict monitoring protocols. In the case of zolpidem, alcohol use is associated with sometimes severe adverse effects from the drug, and so it would be beneficial to screen out candidates with a history of alcoholism or, on the flip side, assess drug interactions for this high-risk group with additional support, monitoring, or counseling.
  3. Dose/Dosing Regimen Optimization. AI/ML can be used to predict drug exposure for different populations based on factors such as weight, height, sex, and other characteristics that might impact drug metabolism. Based on prior drug exposure and response profiles for similar drugs and similar populations, AI/ML can help to narrow the dose/dosing regimen selected for a study. As noted by the FDA’s discussion paper, this can help optimize drug dosing “in special populations where there may be limited data (e.g., rare disease studies, pediatric and pregnant populations).” Based on this research, we can imagine future scenarios where AI/ML could have avoided zolpidem dosing concerns, where graduated and limited dosing was tested and applied to different sex, age, and metabolism categories to determine ideal dosing.
  4. Data Analysis. On a more intriguing level, the FDA AI discussion paper discussed the concept of creating “digital twins” of patients for clinical trials. Essentially, an AI version of the clinical participant is created, using the existing candidate’s electronic health records, vital signs, labs and other records. Researchers can assess how the digital twin would react under normal conditions using AI/ML modeling based on data gathered from similar individuals. This digital twin would then act as a substitute for a placebo candidate in a clinical trial, and act as a benchmark against the actual patient undergoing investigational treatment. For zolpidem, this could be used to assess candidates that already have underlying medical conditions such as anxiety, depression, or other confounding factors, to see whether an adverse effect from a trial is due to the investigational treatment or something that is likely to occur to the same individual from anxiety alone.
  5. Postmarketing Safety Surveillance. Finally, AI/ML can help detect and assess adverse events once the drug enters the market. This is not just limited to individual case safety reports (ICSR), required by regulators, but can include adverse events reported publicly on social media and the wider internet. This type of postmarketing safety surveillance could assist researchers and drug companies in identifying potential drug risks, prior to landing on primetime news.
  Quality and Reliability Risks While AI/ML can help to address the costs and efficiency of clinical trials, this relies substantially on the underlying data used to train AI. The quality and reliability of any AI/ML model requires similar quality controls for underlying training data. Given the safety risks of inappropriate drug dosing, or recruiting candidates with severe medical conditions, AI developers cannot rely solely on self-reported healthcare data with no external medical testing or validation. Developers should be equally wary of training on third-party data sets that do not provide documentation on the collection of data and data validation. Within an existing healthcare organization, if the organization is big enough, aggregate and de-identified data may be obtained from existing electronic health care records and prior clinical trials. Yet, even within these large datasets, errors may surface during training. Medical providers may code the same procedure, and similar symptoms, a dozen different ways. Even drug names can be misspelled and coded incorrectly within existing records. While many of these errors may end up being statistically insignificant with enough data, there is the risk of missing one or two major adverse events, or “black swan” events, that would otherwise change the entire risk profile of a drug. In addition to quality and reliability, the underlying dataset needs to be representative of the population that will be studied for the clinical trial. If the underlying dataset is only trained on a handful of individuals with a certain medical predisposition, age, sex, weight, etc., it will be difficult for the AI model to make predictions for that group. As an example, if the training data only contains the medical information for two individuals over the age of sixty, and shows no adverse effects from a particular drug dose, this information is not enough to generalize that the drug at that dosage is appropriate for all individuals over the age of sixty. For all we know, these two candidates could be a former Olympic diver and a nutrition coach, two outliers that completely skew the data. Consequently, the underlying training data for any AI model should also be assessed for bias and representativeness as it applies to the proposed clinical trial.   Data Privacy, Cybersecurity, and AI Risks The data privacy and cybersecurity risks associated with the foregoing uses of AI/ML cannot be underestimated. The quality and representativeness of any AI system in this field will rely heavily on large swathes of healthcare data, fine-tuned and, at times, personalized in the case of digital twins. This is sensitive or special category data at its finest, triggering heightened scrutiny under the EU’s data privacy law, the GDPR, and U.S. data privacy and data breach laws. To date, most healthcare organizations have sidestepped data privacy concerns by relying on HIPAA’s de-identification standard to remove personal information and other identifiers from healthcare data, making it difficult to associate with an individual. While the FDA requires Institutional Review Board (IRB) review of most biomedical research involving human subjects, this generally does not apply to de-identified personal information that cannot be linked to an individual. Simply de-identifying data and then running with it is not enough, however. Under the California Consumer Privacy Act and similar state laws, for example, recipients of de-identified data need to affirm that they will not attempt to reidentify the data (except to test their de-identification methods). The GDPR has a much higher “anonymization” standard, which looks at the re-identifiability of personal information, given all the different datasets that an organization may have access to. AI/ML itself is making the de-identification process harder. As it is capable of slicing and dicing data by age, race, sex, and medical condition, and combining multiple large datasets, it is easy to run the risk of re-identifying data. While several thousand people might have the same configuration of eye color, age, gender, and weight, only one or two may have participated in a clinical trial at a particular location, or have specific allergies or side effects to certain types of medication. As a result, in circumstances where healthcare data is not de-identified, or the risk of reidentification is heightened, then it behooves clinical organizations and their AI developers to implement written information security programs and associated privacy and security controls.   Legal Liability and Drug Dosing In several notable cases, defendants on zolpidem were able to contest or overturn DWI or even vehicular manslaughter cases. Essentially, these defendants argued that they were not aware of the potential dangers of zolpidem, and so could not be liable for their actions while “sleep driving.” This raises the question: If AI gets good enough, and can tell you exactly the right dose to take of a drug, will you (or your doctor) be liable if you deviate from the AI’s recommendations? Will the AI’s recommendations be discoverable in court (and surfaced via AI-enhanced search)? Only time will tell what this brave new world will bring.   ENDNOTES [1] Kai Falkenberg, While You Were Sleeping (September 27, 2012), Marie Claire, https://www.marieclaire.com/culture/news/a7302/while-you-were-sleeping/.   [2] Takuji Inagaki, Tsuyoshi Miyaoka, Seiichi Tsuji, Yasushi Inami, Akira Nishida, and Jun Horiguchi, Adverse Reactions to Zolpidem: Case Reports and a Review of the Literature, 12 Prim Care Companion J Clin Psychiatry 6 (2010), https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3067983/.   [3] U.S. FDA, Drug Safety Communication: FDA approves new label changes and dosing for zolpidem products and a recommendation to avoid driving the day after using Ambien CR (May 14, 2013), https://www.fda.gov/drugs/drug-safety-and-availability/fda-drug-safety-communication-fda-approves-new-label-changes-and-dosing-zolpidem-products-and.   [4] FDA: Cut Ambien Dosage for Women, ABC News (January 10, 2013, 6:03AM), https://abcnews.go.com/Health/fda-recommends-slashing-sleeping-pill-dosage-half-women/story?id=18182165.   [5] David J Greenblatt, Jerold S Harmatz, & Thomas Roth, Zolpidem and Gender: Are Women Really At Risk?, 39(3) J. Clinical Psychopharmacol. 189 (May/Jun 2019), https://pubmed.ncbi.nlm.nih.gov/30939589/.   [6] NIH Inclusion Outreach Toolkit: How to Engage, Recruit, and Retain Women in Clinical Research, last accessed September 16, 2024: https://orwh.od.nih.gov/toolkit/recruitment/history.   [7] FDA, Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products (May 10, 2023), https://www.fda.gov/media/167973/download; see also Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products; Availability, 88 FR 30313 (May 11, 2023), https://www.federalregister.gov/documents/2023/05/11/2023-09985/using-artificial-intelligence-and-machine-learning-in-the-development-of-drug-and-biological.   [8] Thi Tuyet Van Tran, Hilal Tayara, and Kil To Chong, Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future Perspectives, 15 Pharmaceutics. 1260 (Apr 17, 2023), https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143484/.   Lily Li is an AI, data privacy, and cybersecurity lawyer and founder of Metaverse Law. She is a certified information privacy professional for the United States and Europe and is a GIAC Certified Forensic Analyst for advanced incident response and computer forensics. She can be reached at info@metaverselaw.com.
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Photo of Uber sign on the windshield of a car.

Uber Fined $324 Million for Data Transfer Violations

Data Transfers, GDPR, United States , , ,

What Happened?

On Monday, the Dutch Data Protection Authority (DPA) found that Uber will be fined over $324 million for violating a European Union data privacy law.[1] The Dutch DPA stated that Uber transferred personal data about its drivers to the United States without appropriate safeguards, violating the GDPR.[2] According to the decision, transfer tools to protect this data were not used during the two years that Uber sent personal data from the EU to its US headquarters.[3]

 

Uber is expected to appeal the ruling, and Michael Valvo, an Uber spokesperson, stated that the “flawed decision and extraordinary fine are completely unjustified.”[4] In 2018, the Dutch DPA fined Uber $1.2 million for failing to report a data breach in a timely manner.[5] Earlier this year, the Dutch DPA fined Uber $11 million for infringement of privacy regulations, also concerning the personal data of drivers working for Uber.[6]

 

What Can We Learn?

Uber’s fine is among one of the largest penalties issued under the GDPR, highlighting the strict enforcement and requirements of data protection law within the EU.[7] The chairman of the Dutch DPA, Aleid Wolfsen, stated that, “the GDPR protects people’s fundamental rights by requiring companies and governments to handle personal data with care” and that Uber’s violations were “very serious.”[8]

 

Enacted in 2016, the GDPR sets forth rigorous standards for transferring and managing personal data. Significant financial penalties have been issued to multiple technology companies, including Meta’s $1.3 billion fine in 2023 for similar violations.[9]

 

The Dutch DPA alleges that Uber failed to implement adequate protections as they were not part of the Data Privacy Framework.[10] Additionally, the Dutch DPA alleged that in August of 2021, the company stopped their use of Standard Contractual Clauses (SCCs).[11] Either of these methods may have resulted in Uber avoiding regulatory scrutiny.

 

Understanding the Data Privacy Framework

There are specific rules that apply to data transfers from the EU to the US.[12] Some businesses in the US are members of the Data Privacy Framework, a set of agreements about safe personal data transfers to the US.[13] If the organization belongs to the Data Privacy Framework, they are treated as having an equivalent level of data protection to the EU.[14] This means that those businesses can transfer EU personal data to businesses consistent with EU law and without additional transfer tools.[15] However, if the business is not part of the Data Privacy Framework, the company will have to take additional protective steps when transferring data.[16]

 

Understanding Standard Contractual Clauses

If the US-based business or entity does not participate in the Data Privacy Framework and does not fall within Article 49 derogations or another exception to data transfer requirements, then two additional requirements should be met to transfer personal data outside of the EU: 1) a transfer tool, and 2) additional measures to protect data must be taken as needed. Article 46 of the GDPR provides a list of transferring tools which provide “appropriate safeguards,” including Standard Contractual Clauses (SCCs).[17]

 

SCCs are model contracts approved by the European Commission which allow controllers and processors to comply with requirements of EU data protection law.[18] SCCs have highly specific data protection safeguards, so when they are used between companies, there is a contractual obligation that personal data will be treated with a high level of protection when transferred outside the EU.[19] Because these contracts are standardized, SCC’s are a “ready-made” tool, which are relatively easy to implement.[20]

 

The investigation into Uber arose after the Schrems II ruling, which invalidated the EU-US Privacy Shield due to insufficient data protection standards in the US.[21]  Despite this ruling, Uber continued transferring personal data of their drivers from the EU to the US without implementing SCCs or other safeguards, based on the argument that Chapter V of the GDPR, which covers transfers of personal data to other countries, did not apply.[22] Uber stated that their actions were exempted under Article 3(2), which defines the territorial scope of processing activities.[23] While Uber maintains that its data protecting policies and processes, found in its privacy notice, are sufficient, this investigation and initial ruling demonstrate the heightened scrutiny that US companies face when operating in the EU.

 

Update from 9/13/2024

The European Commission has launched public consultation on the new EU SCCs. This consultation is for clauses in specific cases where a data importer is located in a third country but is directly subject to the GDPR. Adoption of these guidelines is expected in Q2 of 2025.

 

[1] https://www.reuters.com/technology/cybersecurity/dutch-privacy-watchdog-fines-uber-sending-drivers-data-us-2024-08-26/

[2] https://www.reuters.com/technology/cybersecurity/dutch-privacy-watchdog-fines-uber-sending-drivers-data-us-2024-08-26/

[3] https://www.jurist.org/news/2024/08/netherlands-data-protection-authority-fines-uber-e290m-for-violating-eu-data-regulation/

[4] https://www.nytimes.com/2024/08/26/business/uber-netherlands-fine-driver-data.html

[5] https://www.ciodive.com/news/uber-hit-with-12m-in-fines-for-2016-data-breach/543017/

[6] https://www.reuters.com/technology/cybersecurity/dutch-privacy-watchdog-fines-uber-sending-drivers-data-us-2024-08-26/

[7] https://complexdiscovery.com/uber-faces-e290-million-fine-for-gdpr-violation-in-data-transfer-to-us/

[8] https://complexdiscovery.com/uber-faces-e290-million-fine-for-gdpr-violation-in-data-transfer-to-us/

[9] https://www.metaverse.law/2023/05/22/meta-fined-for-data-transfer-violations/

[10] https://www.autoriteitpersoonsgegevens.nl/en/current/dutch-dpa-imposes-a-fine-of-290-million-euro-on-uber-because-of-transfers-of-drivers-data-to-the-us

[11] https://www.autoriteitpersoonsgegevens.nl/en/current/dutch-dpa-imposes-a-fine-of-290-million-euro-on-uber-because-of-transfers-of-drivers-data-to-the-us

[12] https://www.autoriteitpersoonsgegevens.nl/en/themes/international/transfer-within-and-outside-the-eea/personal-data-transfers-to-the-us

[13] https://www.autoriteitpersoonsgegevens.nl/en/themes/international/transfer-within-and-outside-the-eea/personal-data-transfers-to-the-us

[14] https://ec.europa.eu/commission/presscorner/detail/en/ip_23_3721

[15] https://www.dataprivacyframework.gov/Program-Overview

[16] https://www.autoriteitpersoonsgegevens.nl/en/themes/international/transfer-within-and-outside-the-eea/personal-data-transfers-to-the-us

[17] https://www.edpb.europa.eu/system/files/2021-06/edpb_recommendations_202001vo.2.0_supplementarymeasurestransferstools_en.pdf

[18] https://commission.europa.eu/law/law-topic/data-protection/international-dimension-data-protection/new-standard-contractual-clauses-questions-and-answers-overview_en

[19] https://commission.europa.eu/law/law-topic/data-protection/international-dimension-data-protection/new-standard-contractual-clauses-questions-and-answers-overview_en

[20] https://commission.europa.eu/law/law-topic/data-protection/international-dimension-data-protection/new-standard-contractual-clauses-questions-and-answers-overview_en

[21] https://www.metaverse.law/2020/11/30/eu-us-data-transfers-after-schrems-ii-european-commission-publishes-new-draft-standard-contractual-clauses/

[22] https://www.linkedin.com/posts/protectionofdata_uber-decision-dutch-dpa-activity-7234087611676463106-PWNz?utm_source=share&utm_medium=member_desktop

[23] https://www.linkedin.com/posts/protectionofdata_uber-decision-dutch-dpa-activity-7234087611676463106-PWNz?utm_source=share&utm_medium=member_desktop

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Orange County Lawyer Magazine Logo

Metaverse Law featured in OC Lawyer Magazine

AI & Algorithms, Biometrics, Data / Web Scraping, GDPR, Social Media

The Orange County Bar Association recently released the January 2024 issue of Orange County Lawyer magazine. This month, Orange County Lawyer includes an article written by Metaverse Law’s Lily Li.

Read “AI Generated Deepfakes: Potential Liability and Remedies” below or in Orange County Lawyer magazine.

 

[Originally published as a Feature Article: AI-Generated Deepfakes: Potential Liability and Remedies, by Lily Li, in Orange County Lawyer Magazine, January 2024, Vol. 66 No.1, page 26.]

AI-Generated Deepfakes: Potential Liability and Remedies

 

by Lily Li

 

Almost ten years ago, in Netflix’s hit series House of Cards, the Underwoods’ presidential bid is almost derailed by a leaked picture of an affair, nude shower scene and all. While the picture was real, the Underwoods were able to undermine the credibility of the leaked image by claiming it was fake—going so far as to recreate the image using a hired model, to show how “easy” it was to fabricate photos.

This episode, aptly named “The Road to Power,” highlights one of the greatest risks of disinformation and fake or synthetic media. It is not through the public’s gullibility to doctored images; it is the watering down of trust in online media, leading individuals to rely solely on friends, family, and other sources of information that echo their own beliefs and values.

Fast forward a decade, and synthetic media—also known as “deepfakes” –-are now pervasive. In early 2022, for example, a fake video of Ukrainian President Volodymyr Zelensky circulated on social media, calling for his soldiers to lay down their arms and surrender to Russia.[1] At the corporate level, deepfakes have been used to mimic a CEO’s voice to fraudulently transfer $243,000.[2] Just as troubling, and even more creepy, a “sophisticated hacking team” impersonated the CEO of cryptocurrency company Binance by using “video footage of his past TV appearances and digitally alter[ing] it to make an ‘AI hologram’ of him and trick people into meetings.”[3] At home, scammers can use deepfaked voices to mimic loved ones, or AI-powered chatbots to engage in romance scams via text messages and phone calls. This is just a front to ask the victim to wire money, send gift cards, or reveal personal information to engage in identity theft. The problem has become so severe that both the FTC and the FCC have released consumer alerts in early 2023 regarding these AI-generated scams.[4]

The ease in which generative AI can create realistic videos, voice, and text will only aggravate these concerns. Deepfakes have long relied on machine learning to iterate and become more realistic with training, but in the past, this type of technology required significant computing resources and time. Now, almost every tech product is incorporating generative AI or machine learning in some form, making this accessible to every novice programmer or script kiddie.

Given these growing risks, this article will focus on the potential liability that creators, platforms, and publishers face in creating and spreading deepfakes, as well as the challenges of pursuing remedies under existing laws. In addition, this article will discuss pending rulemaking governing deepfakes and potential steps forward.

 

Privacy Liability for Deepfakes

Biometrics: If deepfakes rely on scans of faceprints, facial geometry, or voiceprints to make the false video or audio, or even to train their algorithms, then biometric privacy laws may apply. The Illinois Biometric Information Privacy Act (BIPA) is one of the strictest data privacy laws in the country. It requires express written consent and meaningful disclosures prior to any use and disclosure of Illinois resident biometric data. The collection of biometric data is interpreted broadly to include faceprints and voiceprints. It provides a private right of action, up to $5,000 in statutory damages per violation, and does not require a showing of harm.[5] Earlier this year, in Cothron v. White Castle Systems, Inc.,[6] the Illinois Supreme Court went even further, confirming that each scan in violation of BIPA counts as an ongoing violation—adding further teeth to this law.

Revenge Porn Laws: To the extent the deepfakes include pornographic images, several states, like Virginia,[7] have explicitly included deepfakes within “revenge porn” laws, while other victims have pursued claims under existing revenge porn laws by claiming that the deepfakes amount to non-consensual pornography. The legal consequences vary by jurisdiction, ranging from misdemeanors to felonies with fines and jail time. New York and California also provide a private right of action for deepfake pornography.

General Data Protection Regulation (GDPR): The EU has a broad privacy law that governs use of personal data. Unlike U.S. state privacy laws, which generally allow free use of publicly available data (except for biometric processing), the EU requires all individuals, companies, and non-profits to have a lawful basis for processing any personal data—with limited exclusions for personal data “manifestly made public by the data subject.” Thus, indiscriminate scraping of social media data for deepfakes, especially where the users have limited the audience for their data, would likely violate the GDPR and be subject to fines and regulatory scrutiny.

 

IP, Torts, and other Remedies

Defamation: Traditional defamation claims are also applicable to deepfakes, if the plaintiff can show that the deepfake is communicated to third parties and makes false assertions that harms the plaintiff’s reputation. For public figures, plaintiffs must also show malice.

Rights of Publicity: Many states recognize a “right of publicity” to an individual’s voice or image. The damages or royalties from a right to publicity claim are proportionate to the value associated with licensing one’s image, so these types of claims are more appropriate for celebrities that ordinarily profit from licensing their image.

Copyright and Trademark: To the extent deepfakes use existing logos, photos, music, or even unique website designs to make them seem official or legitimate, this may support multiple claims of copyright and trademark infringement. Copyright holders may also send copyright takedown notices under the DMCA for infringing conduct.

Breach of Contract: If deepfakes rely on scraped content from existing sites or platforms, this may also support a breach of contract claim against the offending party (to the extent they’ve signed up and agreed to the platform’s rules). For example, in the widely publicized case, hiQ Labs, Inc. v. LinkedIn Corp., the Ninth Circuit found that hiQ breached LinkedIn’s User Agreement both through its own scraping of LinkedIn’s site and through its use of independent contractors to log into LinkedIn and do quality control of the data.[8] The Ninth Circuit noted, however, that LinkedIn was estopped from pursuing certain claims due to how much time had elapsed since its initial awareness of data scraping. Consequently, platforms that wish to rely on breach of contract claims to combat data scrapers, and potential misuse of their platforms for generative AI and deepfakes, must act swiftly and definitively. This is likely the impetus for X Corp’s (formerly Twitter) recent slew of crackdown on data scrapers, through a series of lawsuits filed in August.[9]

State Deepfake Laws: California, Texas, and Virginia have also enacted deepfake laws specific to political deepfakes, but these laws are limited in application and remedy. Texas SB 751, for instance, prohibits deepfake videos created “with intent to injure a candidate or influence the result of an election” and which are “published and distributed within thirty days of an election.” This law makes violations a Class A misdemeanor punishable by up to a year in jail and fines up to $4,000. More recently, Washington State passed a law requiring clear and transparent notices on any synthetic video or audio concerning candidates if it is related to an election. Senate Bill 5152 gives candidates a private right of action, including attorney’s fees for the prevailing party.

 

Limitations of Existing Remedies; Section 230 of the Communication Decency Act

There are several hurdles that would-be plaintiffs face in pursuing deepfake claims. For many torts like defamation and right of publicity, the amount of damages may be limited compared to the cost of litigation, and important First Amendment rights protect non-commercial speech that is satirical or political commentary. In addition, deepfake content can easily cross borders, so it may be difficult to find a defendant to penalize or enjoin. Consequently, instead of pursuing traditional claims, many victims rely solely on IP takedown notices, or a social media platform’s own processes to flag and remove deepfake content.

At present, Section 230 of the Communications Decency Act also shields platforms from liability for the content users upload and distribute on their platforms, as platforms generally do not constitute the “speaker” or “publisher” of such content. The line between acting as a pure platform, and contributing or generating harmful content, is increasingly blurred, however. In the recent Supreme Court case, Twitter, Inc. v. Taamneh et al,[10] plaintiffs alleged that social media platforms profited from ISIS recruitment videos and allowed ISIS to take advantage of the social media platforms’ “recommendation” algorithms that match content. While the Supreme Court declined to address the scope of 230 protections for these types of “recommendation” algorithms—the Supreme court noted that Section 230 may not protect platforms that create text, audio, or video through generative AI. In oral arguments to Google v. Gonzales, a companion case to Taamneh, Justice Gorsuch strongly implied that generative AI would fall outside of Section 230’s protections, stating: “I mean, artificial intelligence generates poetry, it generates polemics today. That—that would be content that goes beyond picking, choosing, analyzing, or digesting content. And that is not protected. Let’s—let’s assume that’s right, okay?”[11]

Going forward, we anticipate that the Illinois Biometric Information Privacy Act, and pending bills on biometric data, will likely be a more promising and lucrative way to attack platforms that explicitly use biometric data to generate or share deepfakes. In addition, as noted above, plaintiffs may have more luck pursuing claims against platforms that help create deepfake content or media using generative AI rather than solely relying on user content.

 

Do We Need Additional Laws?

As we can see from the patchwork of common law and statutory rights, the potential risks for creating and publishing deepfakes is many, but the best avenue for plaintiffs to pursue a remedy is unclear. Even some regulators are scratching their heads as to whether existing rules apply to deepfakes. For example, in July 2023, Public Citizen filed a petition with the Federal Election Commission (FEC), asking the FEC to amend its regulation on “fraudulent misrepresentation” at 11 C.F.R. § 110.16[12] to clarify that “the restrictions and penalties of the law and the Code of Regulations are applicable” should “candidates or their agents fraudulently misrepresent other candidates or political parties through deliberately false [AI]-generated content in campaign ads or other communications.”[13] In response, the FEC submitted a notice, soliciting public comment on this issue before making a decision on the merits of the petition.

The FTC has taken a firmer stance, stating that it does have authority to regulate AI generally, and deepfakes more specifically. In a March 2023 blog post titled “Chatbots, deepfakes, and voice clones: AI deception for sale,” the FTC noted that the “FTC Act’s prohibition on deceptive or unfair conduct can apply if you make, sell, or use a tool that is effectively designed to deceive—even if that’s not its intended or sole purpose.”[14]

Abroad, the European Union is taking an entirely different approach, developing a comprehensive law (the EU “AI Act”) that would govern artificial intelligence as a whole. The law, as drafted, requires all high-risk AI processing to undergo risk assessments for bias, safety, accuracy, and other risks. In addition, the AI Act would require transparency obligations for deepfakes, defined as “AI systems that generate or manipulate image, audio or video content.”[15] While the AI Act is still in draft form, it is likely to have as large and wide sweeping of an impact as the General Data Privacy Regulation, once it goes into effect.

Given the existing plethora of rights and remedies under the law, and the potential impact of the EU AI Act, this author does not believe that this is the right time to pursue a federal law specific to deepfakes—even though they present serious threats. In the current divisive political climate, it is likely that any proposed law will either get blocked, watered down, or if passed—fail to strike the right balance between free speech and misleading content. Instead, courts and regulators should strictly enforce existing laws that protect individual privacy and image rights, and the right to be free from false and deceptive practices. Attorneys should advise their tech clients on the risks of generative AI technologies and the potential gaps in Section 230 coverage. Finally, as private citizens, let’s remain diligent in what we read and share—and not be afraid to call out anyone who seeks to deceive.

 

ENDNOTES

(1) Bobby Allyn, Deepfake video of Zelenskyy could be ’tip of the iceberg’ in info war, experts warn, NPR (Mar. 16, 2022, 8:26 PM), https://www.npr.org/2022/03/16/1087062648/deepfake-video-zelenskyy-experts-war-manipulation-ukraine-russia.

(2) Catherine Stupp, Fraudsters Used AI to Mimic CEO’s Voice in Unusual Cybercrime Case, Wallstreet Journal (Aug. 30, 2019, 12:52 PM),  https://www.wsj.com/articles/fraudsters-use-ai-to-mimic-ceos-voice-in-unusual-cybercrime-case-11567157402.

(3) Luke Hurst, Binance executive says scammers created deepfake ’hologram’ of him to trick crypto developers, Euronews (Aug. 24, 2022, 2:47 PM), https://www.euronews.com/next/2022/08/24/binance-executive-says-scammers-created-deepfake-hologram-of-him-to-trick-crypto-developer.

(4) Alvaro Puig, Scammers use AI to enhance their family emergency schemes, Federal Trade Commission (Mar. 20, 2023), https://consumer.ftc.gov/consumer-alerts/2023/03/scammers-use-ai-enhance-their-family-emergency-schemes; ’Grandparent’ Scams Get More Sophisticated, Federal Communications Commission, https://www.fcc.gov/grandparent-scams-get-more-sophisticated (last visited Nov. 29, 2023).

(5) See Rosenbach v. Six Flags Entertainment Corp., 2019 IL 123186 (Jan. 25, 2019).

(6) 2023 IL 128004 (Feb. 17, 2023).

(7) Va. Code Ann. § 18.2-386.2.

(8) No. 17-3301 (N.D. Cal. Nov. 4, 2022).

(9) Blair Robinson, X Corp Lawsuits Target Data Scraping, National Law Review (Aug. 17, 2023), https://www.natlawreview.com/article/x-corp-lawsuits-target-data-scraping.

(10) 598 U.S. 471 (May 18, 2023).

(11) Transcript of Oral Argument at 49, Google v. Gonzales, 598 U.S. 617 (2023) (No. 21-1333).

(12) Available at https://www.ecfr.gov/current/title-11/section-110.16.

(13) Artificial Intelligence in Campaign Ads, 88 Fed. Reg. 55606 (proposed Aug. 16, 2023), https://www.federalregister.gov/documents/2023/08/16/2023-17547/artificial-intelligence-in-campaign-ads.

(14) Michael Atleson, Chatbots, deepfakes, and voice clones: AI deception for sale, Federal Trade Commission (Mar. 20, 2023), https://www.ftc.gov/business-guidance/blog/2023/03/chatbots-deepfakes-voice-clones-ai-deception-sale.

(15) Tambiama Madiega, Artificial intelligence act, EU Legislation in Progress, European Parliament (June 2023), https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/698792/EPRS_BRI(2021)698792_EN.pdf.

 

Lily Li is a data privacy, AI, and cybersecurity lawyer and founder of Metaverse Law. She is a certified information privacy professional for the United States and Europe and is a GIAC Certified Forensic Analyst for advanced incident response and computer forensics.

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