Shadow AI: How Can Companies Protect Against Unknown Uses?

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Employees don’t always wait for their employers to approve new technology. This could be using a personal chatbot account to summarize a document, installing an AI browser extension, uploading information into an AI analysis tool or using an AI feature built into software without their employer knowing about it. This practice is often referred to as “shadow AI.”

Shadow AI is typically not malicious. Often, it’s the result of employees wanting to work more efficiently, or as a result of unclear AI use policies. However, shadow AI can create security and confidentiality issues. If employees send personal or company information into an AI system that has not been reviewed, the business may not have an accurate picture of where its information is going, how it’s being used, or for how long it is being stored. 

What does shadow AI look like?

Shadow AI could include any number of unapproved AI tools used for work purposes. For example, an employee may paste customer information into an LLM to create a summary, upload internal presentation information for editing or use an AI tool to analyze a spreadsheet. 

The problem is that using shadow AI can result in company information being sent to a third-party provider that the original company may not know about. By hiding in the “shadows,” employees who use AI tools in this way can create governance gaps. Without proper review of the AI tools being used by its employees, a company may not know what information is being provided, how long it will be retained for, who can access it or whether the vendor can use it for other purposes, like training its own systems. 

How can companies address employee AI usage?

Company AI policies can bring clarity to issues surrounding shadow AI. 

By addressing what kinds of technologies may be used, these policies can explain which AI tools are approved, what information employees may enter into those tools, and when a new AI tool or use requires review. In general, companies drafting these policies may want to specifically and clearly state what information may and may not be used. For example, a general policy telling employees to use AI responsibly may not provide enough guidance when someone is deciding whether to upload a confidential document or customer data into a new tool. 

These policies should also be communicated clearly. Without understanding the policies that a company has in place, employees may inadvertently engage in shadow AI use. With clarity on approved tools, uses, and inputs, an effective employee AI use policy could lower these risks. Within this policy, a company may may consider creating a process to request new AI tools. This way, relevant business teams can review any AI tools and uses before company information is provided.  

What should businesses take away?

Businesses do not necessarily need to prohibit AI use. However, companies may consider reviewing which tools employees are using and what information is being provided to them. Some steps companies may consider in this review process include, but are not limited to: 

  • Identifying AI tools employees are actually using, including personal accounts, browser extensions and AI features within existing software.
  • Explaining which tools are approved, restricted or prohibited and what information employees may enter.
  • Considering privacy, confidentiality, retention, deletion and AI-training terms with vendors.
  • Determining whether AI uses involve processing that requires a CCPA risk assessment or updates to privacy documentation.
  • Establishing rules preventing employees from providing sensitive company information to unapproved AI tools.
  • Giving employees a clear way to request new AI tools before using them for company work.

While AI in the workplace may boost efficiency, it may also create risk if the company is not aware of it. By providing employees with guidance on AI, businesses may be able to reduce the risks of shadow AI. 

AI Notetakers: Key Takeaways for Recording Calls

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Use of AI notetakers is quickly becoming routine. These tools can automatically join video calls, listen to conversations, generate transcripts, create summaries, and identify key points in a meeting. 

While these tools have an argument for efficiency, they also create legal, privacy, and confidentiality risks. 

This issue is a lot more complicated than simply asking participants of a meeting for their consent to be “recorded.”  This is because recording, transcription, and AI processing are separate activities – and each may have its own notice and consent requirements.

As a result, businesses using these tools need to understand both what is happening during the meeting and what happens to the information after the meeting has ended.

What are AI notetakers?

An AI notetaker is a tool that captures meeting content and uses artificial intelligence to create transcriptions, summaries, action items, and log other important meeting records. Most operate as a bot that joins a video conference meeting as an additional participant. 

Many AI notetakers process information from meetings through cloud-based services rather than keeping the conversation only on an employee’s local computer. Depending on the provider, the service may receive audio transcripts and other meeting information and then use an AI system to analyze and summarize the dialogue. This means meeting content may be transferred outside of the company’s own system and processed or stored by a third-party provider, which may have privacy and data-sharing implications. 

Why do recording and consent laws matter?

Recording laws differ across the United States. Some states generally follow a one-party consent approach, meaning the consent of one participant is sufficient to record a conversation.  However, other states follow an all-party consent approach, sometimes referred to as “two-party consent” which generally requires the consent of everyone involved in a conversation for recording. The specific details and exceptions vary by jurisdiction. 

What actually counts as consent?

Sometimes consent can be more complicated than just simply displaying a recording symbol. Video-conference platforms may use different methods to alert participants when recording a meeting begins: a pop-up requiring participants to acknowledge the recording, an audible announcement, a banner or an icon that’s displayed during the meeting, some hosts may also require verbal consent.

But notice and consent are not always the same thing and what constitutes legally sufficient consent can depend on the applicable law and the circumstances at hand.

What happens to meeting information after the call?

Once an AI notetaker has captured a meeting, businesses should understand what rights the provider has over that information. Vendor terms can address data ownership, licensing, data retention, and whether information may be used to develop or improve the provider’s service. 

This becomes especially important as ordinary workplace conversations frequently include information that employees may not intentionally send to a third party: customer information, personnel issues, financial projections, internal strategy, product development and many other confidential materials may all be discussed while the AI notetaker captures the conversation. 

The transcription or summary can also create additional security concerns once the meeting ends. For example, automatically generated notes may be distributed via email, downloaded, forwarded and stored in employee accounts long after an original conversation has occurred. Meeting summaries create additional opportunities for sensitive information to spread or remain stored indefinitely. 

Businesses should therefore review not only what the tool captures but also who receives the resulting transcript, where it is stored, how long it is retained and who has the ability to delete it.

What about attorney-client privilege?

Adding an AI notetaker to a legal discussion can create questions about whether confidentiality has been maintained, and depending on the circumstances, whether privilege could be challenged or waived. You can read more updates from Federal District Courts on the issue here. 

This does not mean that all use of technology during a legal meeting automatically destroys attorney-client privilege. Whether privilege is affected may depend on the circumstances such as how the AI provider handles information and why the tool is being used. Therefore, businesses should be more cautious about allowing AI notetakers into meetings that involve legal advice or other professionally protected information. 

The same concern applies to trade secrets and other confidential business information. Meetings involving sensitive product plans or internal strategies and other proprietary information may not be appropriate for AI transcription unless the tool and its data practices have been reviewed carefully. 

What should businesses take away?

AI notetakers can be useful workplace tools, but businesses should have clear policies in place before employees begin using them regularly. Below are some high-level tips that businesses may want to consider when onboarding a new AI notetaker: 

  • Approve specific tools: Employees should know which AI notetakers are permitted and how those tools record, transcribe, store and process meeting information.
  • Create clear notice and consent procedures: Businesses may consider the laws that may apply to meeting participants and make sure notices accurately reflect how AI is being used.
  • Limit use in sensitive meetings: Legal, HR, disciplinary investigation and other confidential discussions may require additional approval or no AI notetaker at all.
  • Review vendor data practices: Companies should understand how meeting information is used, stored, retained and deleted, including whether it may be used to train or improve AI systems.
  • Set rules for transcripts and summaries: Businesses may want to determine who can access or share AI-generated notes, how long they are kept and whether they are treated as official company records.

Healthcare AI data breaches: Why companies need to protect more than patient records

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Healthcare and life science companies are not only protecting traditional patient records – things like names, diagnoses, treatment notes, lab results, and insurance information. They may also be protecting clinical trial data, research data, vendor systems, proprietary models, and other information used to support patients and drug development. When these companies and systems are involved in data breaches, the legal risks can become broader than a standard data breach. 

A useful example is the recent Novo Nordisk incident. Novo Nordisk is the pharmaceutical company that makes drugs such as Ozempic and Wegovy and recently disclosed a cyber incident involving patient data from clinical trials. It has also been reported that a hacking group claimed to have stolen over a terabyte of data and attempted to extort the company, though Novo Nordisk has not confirmed the entire scope of those claims. 

The Novo Nordisk incident shows that healthcare breaches can involve more than names and medical records. Companies also need to consider whether a cyber incident involves clinical trial information, healthcare provider data, research files, AI model information, or other confidential business materials. When this type of data is involved, companies may need to take precautions to protect the data, document their response, and comply with applicable privacy and cybersecurity obligations.

Recent health-related breaches also highlight how these incidents create major legal and business consequences. The Change Healthcare cyberattack that occurred in 2024 shows the scale of healthcare cybersecurity risks with reports that 197.2 million individuals were impacted by this breach. For scale, 197.2 million people would be more than half of Americans. The 23andMe breach also highlights the litigation risks tied to sensitive genetic information, with a bankruptcy judge approving a $46.75 million settlement for victims in the breach. While the facts of these examples differ, they all highlight why companies handling health-related data need to treat cybersecurity, AI governance and breach response as connected compliance and a priority in business practices. 

Why are AI-related healthcare breaches unique?

A standard healthcare breach analysis often focuses on whether names, medical records, insurance information, or other identifiable patient information was exposed. These concerns are highly important and sensitive. But as artificial intelligence becomes more integrated into healthcare, it creates additional risks because AI tools rely on large volumes of sensitive data that may also be tied to valuable research and business information.

For example, an AI model used in drug development may involve clinical trial data, molecular research, imaging data, prompts, outputs and other model training information. If these systems are accessed by an unauthorized actor, the incident may raise privacy concerns and may also create IP and competitive risks. 

This means healthcare AI security should not only focus on protecting patient records. Companies should also consider whether their AI models, training datasets, research systems, and vendor environments are adequately secured.

Why does de-identification matter?

De-identification can be key when healthcare data is used with AI. In simple terms, de-identification means removing information that could identify a person up to a certain legal standard. This matters because companies may want to use patient data or clinical data to train, test or improve their models. 

If health data is properly de-identified before being used with AI, the company may reduce privacy risks. However, if the data is not properly de-identified, several questions remain. The company may need to ask whether its privacy notices clearly explained that health data could be used for AI training or analysis. State privacy laws may also require clear disclosures about how personal information is collected and used. For example, California’s CCPA requires covered businesses to provide notice about the categories of personal information collected and the purposes for which that information is collected or used. This is especially important when health-related data is later used in a way that patients or consumers may not have expected. 

The company may also need to consider whether protected health information was shared with an outside AI vendor, whether a business associate agreement was required, and whether patient authorization was needed.

What happens if PHI may have been compromised? 

Breach response is also an important issue. If a healthcare company experiences a ransomware attack or an unauthorized access incident, they may need to assess whether protected health information (PHI) was compromised, This may include reviewing what information was involved, whether it could identify an individual, who accessed it, whether it was viewed or acquired and if the risk was mitigated or not.

The Novo Nordisk incident highlights why this analysis matters. Novo Nordisk stated that the clinical trial information involved was not directly linked to patient names or other direct identifiers. However, the incident still raised questions about patient-related data, de-identification, and whether any protected health information may have been compromised. This is why companies should be able to document how health-related data was stored, protected, and separated from information that could identify individuals.

What should companies take away? 

For healthcare and life science-related companies, the key takeaway is that AI governance should include privacy and cybersecurity from the start. Before using health-related data with AI, companies should ask:

  • What data goes into the AI system?
  • Is the data identifiable or properly de-identified?
  • Who can access the data, prompts, outputs, or model?
  • Are outside vendors involved?
  • Can the vendor use the data to train or improve its own models?
  • What security controls apply if the system is breached?

Companies should also treat AI models, training datasets, prompts, outputs, and research tools as sensitive assets as well since they can carry PHI and may lead to inadvertent disclosures. Vendor contracts should address confidentiality, data retention, security controls and model training. 

AI may help healthcare and life science companies innovate and operate faster, but innovation cannot replace privacy and regulatory accountability. Companies using AI with health-related data need to confirm de-identification practices, strengthen vendor contracts, and properly prepare for responses before incidents occur.

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California: New AI laws in California – roundup of the 2025 legislative session

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This article was originally published by OneTrust DataGuidance on November 24, 2025 and can be found on the DataGuidance website here.

California introduces comprehensive AI laws focusing on transparency, children’s safety, healthcare, antitrust, and law enforcement.

California has taken an aggressive stance towards artificial intelligence (AI) legislation and will likely set the standard for other US states. Back in 2024, Governor Newsom vetoed comprehensive AI safety legislation under bill SB 1047 and advised caution on regulations for this nascent and important technology. This year, Governor Newsom pressed ahead with a full slate of new AI laws. The reasons for this change in approach are many, including but not limited to the lack of federal AI legislation, the growing concern over children’s interactions with AI, especially sexualized content, and harmonization with more stringent requirements in the EU and elsewhere.

This year’s legislative session set records for the number and scope of new AI laws. For the roundup this year, Lily Li, of Metaverse Law Corporation, breaks down the new AI laws by scope and sector, noting where this may add on to existing California legislation and rulemaking from 2024-2025.

General AI safety, transparency, and risk assessments

  • SB 53: Transparency in Frontier Artificial Intelligence Act (Wiener) – Starting in January 2026, California will require large frontier AI developers to publish a framework detailing how they incorporate safety, security, and testing standards into their AI models. SB 53 also creates a mechanism for AI developers and the public to report critical safety incidents, and protects internal whistleblowers who report risks posed by frontier AI models. The law establishes significant penalties for companies that fail to comply, with fines of up to $1 million per violation.
  • AB 316: Artificial Intelligence defenses (Krell) – This amends California’s Civil Code. If a party to a lawsuit develops, modifies, or uses AI, this law prohibits them from asserting as a defense that the AI autonomously caused the harm.
  • AB 853: California AI Transparency Act (Wicks) – This bill expands the existing AI Transparency Act and modifies the effective date from January 1, 2026, to August 2, 2026. The California AI Transparency Act requires covered generative AI developers to provide an AI-detection tool to assess whether image, video, or audio content is created or altered by generative AI. This bill adds to the existing law by requiring large online platforms to embed provenance data into generated content. Starting January 1, 2028, users will also have the option to include latent disclosures on ‘capture devices’ such as cameras, video recorders, and other recorders.

This new California approach to AI transparency and safety legislation needs to be read in conjunction with the following existing laws.

  • California Privacy Protection Agency’s (CPPA’s) recently approved Cyber, Risk, ADMT, and Insurance Regulations – The CPPA’s most recently updated 127-page regulation package contains requirements governing cybersecurity audits, risk assessments, and automated decision-making technology. AI developers and systems that process personal information and meet certain California privacy thresholds will now face new cybersecurity audit and risk assessment requirements. In addition, automated and significant decisions concerning the provision or denial of financial or lending services, housing, education enrollment or opportunities, employment or independent contracting opportunities or compensation, or healthcare services will trigger significant notice, opt-out, and risk assessment requirements.
  • AB 2013: AI Training Data Transparency Act (Irwin-2024) – Passed last year, this law will require covered generative AI developers to publish online a high-level summary of the datasets used in the development of the generative AI system or service, including but not limited to whether personal information or copyrighted information is included in the training data. The law is scheduled to go into effect on January 1, 2026.

Children’s safety, age verifications, and companion chatbots

  • SB243: Companion Chatbots (Padilla) – This law applies to chatbots that provide human-like interactions and are capable of sustaining relationships across multiple interactions. Beginning July 1, 2027, developers of these ‘companion chatbots’ will need to develop and report protocols addressing suicidal ideation and self-harm to regulators and the public. The law requires AI disclosures, referrals to suicide hotlines or crisis text lines, and break reminders. SB 243 further requires developers to institute reasonable measures to prevent the chatbot from producing visual material of sexually explicit conduct or directly stating that the minor should engage in sexually explicit conduct. The legislation includes a private right of action to individuals who suffer ‘an injury in fact’ with statutory damages of $1,000 per violation, or actual damages if greater.
  • AB 1043 – Digital Age Assurance Act (Wicks) – Starting January 1, 2027, operating systems and covered application stores will be required to obtain age data from users and pass on age bracket data to developers when users download and launch an application.
  • AB 56: Social Media Warning Law (Bauer-Kahan) – Starting January 1, 2027, covered social media platforms will need to display a warning label to minors the first time a user accesses the platform each day, after three hours of active use, as well as once per hour of cumulative active use after that. The warning label must say ‘The Surgeon General has warned that while social media may have benefits for some young users, social media is associated with significant mental health harms and has not been proven safe for young users.’
  • AB 621: Deepfake pornography (Bauer-Kahan) – This amends California’s Civil Code and expands protections against deepfake pornography. The law explicitly provides a cause of action against individuals who create or disclose deepfake pornography if they know, or reasonably should know, that the depicted individual was a minor and also provides a cause of action against individuals who knowingly facilitate or recklessly aid or abet the creation or disclosure of such nonconsensual deepfake pornography. The bill confirms that a minor cannot consent to the creation or distribution of deepfake pornography.

California’s approach to AI and children has a long and complicated history, and these new laws should be read in conjunction with the following laws on the books.

  • California Age Appropriate Design Code (Wicks) – This law was signed on September 15, 2022, and was scheduled to go into effect on July 1, 2024. Modeled after the UK Age Appropriate Design Code, this law requires businesses to conduct impact assessments, provide Privacy by Default, estimate the age of all users, and restrict dark patterns. The law was enjoined in March 2025, but is being appealed by the California Attorney General.
  • Protecting Our Kids from Social Media Addiction Act (Skinner-2024) – This law is scheduled to go into effect on January 1, 2027, and prohibits covered social media platforms from providing addictive feeds to minors without verifiable parental consent. The law has so far escaped a constitutional challenge, but may face other court challenges prior to the effective date.

Healthcare AI and chatbots

  • AB 489: Health care professions: deceptive terms or letters: artificial intelligence (Bonta) – This law prohibits AI systems from falsely indicating or implying possession of a medical license or certificate through advertising, marketing, or other functionality. AB 489 also makes AI developers directly subject to the healthcare professional licensing board or enforcement agency if they develop such a system. Each use of a prohibited term, letter, or phrase shall constitute a separate violation.

California’s approach to AI in healthcare also needs to be read in conjunction with the following laws and guidance.

  • Legal Advisory on the Application of Existing California Law to Artificial Intelligence in Healthcare – In January 2025, California Attorney General Rob Bonta issued this advisory, setting forth California’s existing consumer protection, civil rights, competition, and data privacy laws governing healthcare AI.
  • SB 1120: Physicians Make Decisions Act (Becker-2024) – This law prohibits covered healthcare service plans from denying, delaying, or changing healthcare services based, in whole or in part, on medical necessity using AI, algorithms, or other software tools. Such determinations shall require a physician or licensed healthcare professional and review of individual circumstances. This law also requires written policies and procedures governing such determinations.
  • AB 3030: Artificial Intelligence in Health Care Services (Calderon – 2024) – This law applies to health facilities, clinics, physicians’ offices, or other health group practices that use generative AI for communications about patient clinical information. Under this bill, generative AI, which pertains to clinical information, must include:
    • a disclaimer that indicates the communication was generated by AI at the beginning of the interaction; and
    • clear instructions on how the patient can contact the appropriate person.

Antitrust and pricing discrimination

  • AB 325: Cartwright Act violations (Aguiar-Curry) – This amends California’s existing antitrust law, the Cartwright Act, to explicitly cover ‘common pricing algorithms.’ The law prohibits:
    • the use or distribution of a ‘common pricing algorithm’ as part of a contract, combination in the form of a trust, or conspiracy to restrain trade or commerce; or
    • coercion to set or adopt a recommended price or term, recommended by the common pricing algorithm for the same or similar products or services.

Complaints shall not be required to allege facts tending to exclude the possibility of independent action.

Law enforcement use of AI

  • SB 524 Law Enforcement Agencies (Arreguín) – SB 524 requires law enforcement to disclose if an official report was written either fully or in part using AI, as well as retain the first draft created by AI and an associated audit trail that, at minimum, identifies both the officer who used AI to create a report and the video and audio footage used to create a report, if any. SB 524 also prohibits AI vendors from sharing, selling, or otherwise using information, except as provided in the bill (e.g., troubleshooting, bias mitigation, quality control, legal purposes, etc.).

Employment and bias

While Governor Newsom vetoed SB 7, the No Robo Bosses Act, the Governor’s veto letter pointed to the CPPA’s ADMT regulations as addressing some of the bill’s requirements. Per Governor Newsom, SB 7 is ‘partially covered’ by these regulations, as they ‘allow employees and independent contractors to better understand how their personal data is used by automated decision technology.’ In addition, the California Civil Rights Council’s recently promulgated regulations state that California’s antidiscrimination laws apply to AI workplace tools. These regulations address another concern raised in SB 7, which sought to prohibit ADS systems from inferring a worker’s protected status.

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EDPB Opinion on AI Models and GDPR Principles: Key Takeaways

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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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