Red Lines under the EU AI Act: Understanding ‘Prohibited AI Practices’ and their Interplay with the GDPR, DSA

Blog 1 | Red Lines under the EU AI Act Series  

This blog is the first of a series that explores prohibited AI practices under the EU AI Act and their interplay with existing EU law. You can find the whole series here.

The EU AI Act prohibits certain AI practices in the European Union (hereinafter also “the Union”or “the EU”), at the top of the pyramid of its layered approach: harmful manipulation and deception, social scoring, individual risk assessment, untargeted scraping of facial images, emotion recognition, biometric categorization, and real-time remote biometric identification for law enforcement purposes. These are the “red lines” that the EU has drawn through the AI Act. “Red lines” in AI governance have been generally described as meaning “specific boundaries that AI systems must not cross”, and, in more detail, as “specific, non-negotiable prohibitions on certain AI behaviors or AI uses that are deemed too dangerous, high-risk, or unethical to permit”. Most “red lines” emerge from soft law or self-regulation, with the AI Act being the first law globally drawing such lines, exemplifying the strict AI regulatory approach that the EU is pursuing. 

Prohibited AI practices are regulated by Article 5 of the AI Act, which already became applicable in February 2025 (see a full timeline of when chapters of the AI Act become applicable). Starting on 2 August 2025 this provision also became enforceable by the designated authorities at Member State level, or the European Data Protection Supervisor – the supervisory authority for EU institutions, as the case may be. Non-compliance with it triggers administrative fines of up to 35 million euros or up to 7% of the total worldwide annual turnover for the preceding financial year, whichever is higher. However, the supervision and enforcement landscape is highly fragmented and decentralized. 

This blog is the first of a series which will explore each prohibited AI practice and its interplay with existing EU law, such as the General Data Protection Regulation (GDPR) and the Digital Services Act (DSA), starting from the Guidelines on Prohibited Artificial Intelligence Practices under the AI Act (hereinafter ‘the Guidelines’), published by the European Commission on 4 February 2025. The aim is to understand what AI systems and practices are within the scope of Article 5 of the AI Act, and to highlight potential areas of legislative overlap or lack of clarity. This is increasingly important, at a time where the European Commission has prioritized addressing the interplay of the digital regulation acquis with a view to amending parts of the AI Act and the GDPR through the Digital Omnibus initiative. While the initial proposal for the Digital Omnibus on AI does not seek to amend the AI Act’s prohibited practices requirements, multiple political groups of the European Parliament and several governments of member states are proposing amendments to enhance the list of prohibited practices particularly with regard to intimate deep fakes and Child Sexual Abuse Material. 

This blog continues with an Introduction into the significance of the Guidelines and the place of the prohibited practices into the broader layered architecture of the AI Act, tailored to severity of risks (1), details about the definitions and scope of the prohibited practices (2), and an analysis of the interplay of the prohibited AI practices with the GDPR and DSA (3), before Conclusions (4) highlight takeaways: 

  1. Entry into force of prohibited AI practices under the AI Act: A year on 

Prohibited practices under Article 5 of the AI Act entered into force on 2 February 2025 and became enforceable on 2 August 2025. However, so far, no enforcement or otherwise regulatory action in relation to prohibited AI practices has been announced. 

About a year ago, on 4 February 2025, the European Commission released Guidelines on Prohibited Artificial Intelligence Practices under the AI Act. The AI Act regulates the placing on the market, putting into service, and use of AI systems across the Union on the basis of harmonized rules and a tiered approach based on the severity of the risks posed by some AI systems. While there are four risk categories in the AI Act, the Guidelines provide legal explanations and practical examples on AI practices that are deemed unacceptable due to their potential risks to fundamental rights and freedoms, and are therefore prohibited. 

While the Guidelines are non-binding, they offer the Commission’s first interpretation of the Article 5 prohibitions as well as crucial insights into its own analysis on the interplay between core requirements of the AI Act and other EU law, including (but not limited to) the GDPR and the DSA. In publishing the Guidelines, the Commission explicitly acknowledged that any authoritative interpretations of the AI Act ultimately reside with the Court of Justice of the European Union (CJEU), and notes that these may be reviewed or amended in light of relevant future case law or enforcement actions by market surveillance authorities. However, while enforcement actions under the AI Act are yet to emerge, analysis can be made with regard to the interplay between the Commission’s Guidelines and existing CJEU case law, as well as decisions by Data Protection Authorities (DPAs) under the GDPR. 

This first blog in our series on ‘Red Lines under the EU AI Act’ highlights how the Commission’s Guidelines take a scaled approach to delineating the practices which fall within and outside of the scope of prohibited practices. The Guidelines highlight the close interplay between Articles 5 (on prohibited AI practices) and 6 (on high-risk AI systems) of the AI Act, and note that where an AI system does not fulfil the requirements for prohibition under the AI Act, it may still be unlawful or prohibited under other laws such as the GDPR. 

  1. From emotion recognition, to social scoring via AI systems: Overview of prohibitions under Article 5 of the AI Act

The tiered regulatory approach of the AI Act takes into account four risk categories of AI systems on the basis of which scaled obligations are proposed: unacceptable risk, high risk, transparency risk, and minimal to no risk. This analysis zooms in especially on unacceptable risk, as found in Article 5 AI Act, which prohibits the placing on the EU market, putting into service or use of AI systems for manipulative, exploitative, social control or surveillance practices. Of note, Article 5 is framed as such that technology or AI systems themselves are not prohibited, but “practices” involving specific AI systems that pose unacceptable risks are. This framing is different from the one in Chapter III of the AI Act, which classifies and regulates systems themselves as “high-risk AI systems.”   

The prohibited practices are, by their inherent nature, deemed to be especially harmful and abusive due to their contravention of fundamental rights as enshrined in the EU Charter of Fundamental Rights. The Guidelines issued by the European Commission highlight Recital 28 of the AI Act by reiterating that the impacts of prohibited AI practices are not limited to the right to personal data protection (Article 8 EU Charter) and the right to a private life (Article 7), but they also pose an unacceptable risk to the rights to non-discrimination (Article 21), equality (Article 20), and the rights of the child (Article 24). 

Prohibited AI practices under the AI Act include:

2.1. The Guidelines extend the scope of prohibited AI practices to include those related to general-purpose AI systems 

In defining the material scope of Article 5 AI Act, the Guidelines expand upon the definitions of “placing on the market, putting into service or use” of an AI system. This is important, because all prohibited practices under Article 5(1) AI Act, from letters (a) to (g), refer to “the placing on the market, the putting into service or the use of an AI system that (…)” engages in a specific practice defined under each of the letters of the provision. Therefore, understanding the definitions of these terms is essential for the application of the “prohibitions”.

“Placing on the market” is the first making available of an AI system on the Union market, for distribution or use in the course of a commercial activity, either for a fee or free of charge (see Articles 3(9) and 3(10) AI Act for full definitions). Placing an AI system on the Union market is considered as such regardless of the means of supply, whether through an API, direct downloads, via cloud or physical copies. 

“Putting into service” refers to the supply of an AI system for first use to the deployer or for own use in the Union for its intended purpose (Article 3(11)), and covers both the “supply for first use” to third parties and “in-house development or deployment”1. The inclusion of in-house development to the scope of Article 3(11) is a significant extension introduced by the Guidelines, considering the definition of “putting into service” in the AI Act only refers to “the supply of an AI system for first use directly to the deployer or for own use in the Union.” This interpretation might need further clarification, especially as Article 2(8) AI Act excludes “any research, testing or development activity regarding AI systems or AI models prior to their being placed on the market or put into service” from its scope of application.

Regarding the “use” of an AI system, which is not directly defined by the AI Act, the Guidelines specify that it should be similarly broadly understood to cover the use and deployment of AI systems at any point in their lifecycle, after having been put into service or placed on the market. Importantly, the Guidelines specify that “use” also includes any “misuse” that may amount to a prohibited practice, making deployers responsible for reasonably foreseeable harms that may arise. 

Given the scope of the prohibited practices, the Guidelines focus on both providers and deployers of AI systems and highlight that continuous compliance with the AI Act is required during all phases of the AI lifecycle. For each of the prohibitions, the roles and responsibilities of providers and deployers should be construed in a proportionate manner, “taking into account who in the value chain is best placed” to adopt a mitigating or preventive measure.

The Guidelines acknowledge that while harms may often arise from the ways AI systems are used in practice by deployers, providers also have a responsibility not to place on the market or put into service AI and GPAI systems that are “reasonably likely” to behave or be used in a manner prohibited by Article 5 AI Act. It is important to highlight that the Guidelines extend the scope of Article 5 to general-purpose AI systems as well, even though they are not specifically called out by the provision (see para. 40 of the Guidelines). 

As highlighted above, the provision is drafted as such to target “practices” of AI, which opens the possibility that not only GPAI systems are covered, but also practices of agentic AI or any new shape or form of AI systems that result in a practice described by Article 5 AI Act. Indeed, the Guidelines specifically mention that the “prohibitions apply to any AI system, whether with an ‘intended purpose’ or ‘general purpose.’” It is worth noting, however, that the Guidelines address prohibitions in relation to general-purpose AI systems rather than models, recalling that such systems are indeed based on general-purpose AI models but “have the capability to serve a variety of purposes, both for direct use as well as for integration in other AI systems” (Article 3(66) AI Act). 

2.2. Purposes that do not fall within the scope of the AI Act, and practices that do

The Guidelines note that the AI Act expressly excludes from its scope AI systems used for national security, defence, and military purposes (Article 2(3)). For this exclusion to apply, the AI system must be placed on the market, put into service or used exclusively for such purposes. This means that so-called “dual use” AI systems, such as those for civilian or law enforcement purposes, do fall within the scope of the law. A direct example from the Guidelines notes that: “if a company offers a RBI (remote biometric identification – n.). system for various purposes, including law enforcement and national security, that company is the provider of that dual use system and must ensure its compliance” with the AI Act (emphasis added). 

In addition to judicial and law enforcement cooperation with third countries, research and development activities also fall outside the scope of the AI Act. Indeed, as also recalled above, the AI Act does not apply to “any research, testing or development activity regarding AI systems or AI models prior to their being placed on the market or put into service” (Article 2(8)). The Guidelines view this exemption as a natural continuation of the AI Act’s market-based logic, which applies to AI systems once they are placed on the market. However, this raises consistency issues with how the same Guidelines include “in-house development or deployment” of AI systems in the scope of “putting into service” (see also Section 2.1. above).

It is worth noting that the Guidelines are explicit in their reminder of the fact that the research and development exclusion does not apply to testing in real-world conditions, and in cases where those experimental systems are eventually placed on the Union market. The testing of AI systems in real-world conditions may only be carried out in AI regulatory sandboxes, and in full compliance with other Union law, including the GDPR insofar as personal data processing is concerned. 

The Guidelines also note that purely personal, non-professional activities similarly fall outside of the AI Act’s scope (Article 2(10)). This includes, for example, an individual using a facial recognition system at home. However, they are careful in noting that the facial recognition system as such does remain within the scope of the AI Act as regards the obligations of providers of such systems in ensuring compliance, even in full knowledge that the system is intended to be used by natural persons for purely non-professional purposes or activities. 

The Guidelines take an overall cautious approach in delineating the purposes and practices which fall outside the scope of the AI Act through consistent reference to Recitals 22 to 25. The Recitals recall and make clear that providers and deployers of AI systems which fall outside the scope of the AI Act may nevertheless have to comply with other Union laws that continue to apply. 

  1. Interplay of the AI Act’s Prohibitions with the High-Risk Designation and other Union Law 

3.1. A scaled approach to the interplay between high-risk AI systems and prohibited AI practices  

The Guidelines highlight key areas of interplay between the different risk categories, showing a scaled approach in the AI Act’s risk designation. Importantly, the Guidelines note the close relationship between Article 5 on prohibited practices, and Article 6 on high-risk AI systems. They note that “the use of AI systems classified as high-risk may in some cases qualify as prohibited practices in specific circumstances” and, conversely, most AI systems that fall under an exception from a prohibition listed in Article 5 will qualify as high-risk. This approach clarifies yet again that Article 5 is not meant to prohibit a specific technology, but practices or uses of technology.

An example where Articles 5 and 6 of the AI Act should be considered in relation to each other is in the case of AI-based scoring systems, such as credit scoring, which will be considered high-risk if they do not fulfil the conditions for the credit scoring prohibition as outlined in Article 5(1)(c). While not specifically mentioned by the Guidelines in this context, it is worth noting that Courts and DPAs across the EU have been active in cases involving automated credit scoring practices under Article 22 GDPR on automated-decision making (ADM), as well as in cases that may amount to “profiling”. The notion of “profiling” under the GDPR is particularly relevant in the context of understanding Article 5(1)(d) AI Act. As such, in addition to taking into full account the risk designations under Articles 5 and 6 AI Act, it is also crucial to note the ADM prohibition under Article 22 GDPR, as compliance with one law may not automatically equal to compliance with the other. 

3.2. Interplay between the prohibited AI practices under the AI Act with the GDPR and DSA 

The Guidelines acknowledge the dichotomy between the AI Act and other Union law by recalling that, as a horizontal law applying across all sectors, the Act is without prejudice to legislation on the protection of fundamental rights, consumer protection, employment, the protection of workers and product safety. They also frame the goal of the AI Act and its preventive logic in the sense that it provides additional protection by addressing potential harms arising from AI practices which may not be covered by other laws, including by addressing the earlier stages of an AI system’s lifecycle. 

The Guidelines expressly highlight that where an AI system may not be prohibited under the AI Act, it may still be prohibited or unlawful under other laws because of, for example, “the failure to respect fundamental rights in a given case, such as the lack of a legal basis for the processing of personal data required under data protection law”, where, for instance, the GDPR is applicable, including extra-territorially. 

Crucially, the Guidelines acknowledge that in the context of prohibitions, the interplay between the AI Act and data protection law is particularly relevant, since AI systems often process personal data. They specify that laws including the GDPR, the Law Enforcement Directive, and the EU Data Protection Regulation applying to EU institutions (EUDPR), “remain unaffected and continue to apply alongside the AI Act”, noting the complementarity of the Act with the EU data protection acquis. 

This statement in the Guidelines about this relationship seems to be weaker than the provision in the AI Act, which states that the AI Act “shall not affect” the GDPR, the EUDPR, the ePrivacy Directive or the Law Enforcement Directive (Article 2(7) AI Act). This technically means that the AI Act is without prejudice to the GDPR and any of the other EU data protection aquis. This fact might create some complex compliance situations in practice, and will require a broad and comprehensive understanding of the EU digital rulebook as a whole, noting that its component parts cannot be read in isolation. For instance, what law prevails if a prohibited AI practice under the AI Act that overlaps with a solely automated decision-making practice involving personal data and legally or significantly affecting an individual, lawfully meets the exceptions under Article 22 GDPR? The AI Act is not designated as lex specialis, based on Article 2(7).

In addition to data protection law, the Digital Services Act (DSA) is similarly deemed relevant in the context of the AI Act’s prohibitions. The Guidelines highlight that these apply in conjunction with the relevant obligations on the providers of intermediary services (defined by Article 3(g) DSA) when AI systems or models are embedded in such services. Further, the AI Act and its prohibitions do not affect the application of the DSA’s provisions on the liability of such providers, as set out in Chapter II DSA, or existing or future liability legislation at Union or national levels. In the context of liability legislation, the Guidelines refer to Directive (EU) 2024/2853 on liability for defective products, and the now withdrawn AI Liability Directive. 

3.3. Notes on Enforcement of the AI Act’s Prohibitions and Penalties: Fragmentation and Decentralization 

The Guidelines recall that market surveillance authorities (MSAs), as designated by EU Member States, are responsible for enforcing the AI Act and its prohibitions. Member States had until 2 August 2025 to designate one or multiple MSAs, with some countries having already assigned the role to their national DPA with regard to certain parts of the AI Act (e.g., high-risk AI systems). Competent authorities can take enforcement actions in relation to the prohibitions on their own initiative or following a complaint by any affected person or other natural or legal person. The staggered timeline between the date of applicability of the AI Act’s provisions on prohibited uses and the deadline for designating the responsible authorities to enforce them has been causing some legal uncertainty.   

A review of Member States that have already appointed MSAs at the time of writing show, for the most part, a decentralized approach to enforcing the AI Act’s prohibited practices. Such an approach, which assigns supervision and enforcement roles to a variety of authorities depending on the sector they regulate and their area of expertise, is typical for EU product safety legislation. 

For example, on 4 February this year, Ireland published its Regulation of Artificial Intelligence Act 2026, the national law that, once adopted, will implement the AI Act’s provisions. On this basis, the enforcement approach proposed by the Act is to establish the AI Office of Ireland, either on or before 2 August 2026, which will act as the central coordinator and Single Point of Contact (Article 70 AI Act). Under this umbrella, the Act also proposes to assign monitoring and enforcement powers to different existing authorities for different prohibited practices: the Central Bank of Ireland will enforce prohibited practices in respect of financial services regulated by it; the Workplace Relations Commission will enforce prohibited practices used in employment (Article 5(1)(f) AI Act); the Coimisiún na Meán will be responsible for “certain” prohibited practices in respect of online platforms (as defined by the DSA); and the Irish Data Protection Commission (DPC) will also be responsible for “certain parts” of the prohibited practices. While the Act does not yet specify which “certain parts” the Irish DPC will be responsible for monitoring, the draft already gives an indication of the decentralized approach to enforcing the rules on prohibited practices at national level, with responsibility assigned to a variety of authorities. 

In France, the CNIL is responsible for monitoring compliance of the prohibited practices for predictive policing, the untargeted scraping to develop facial recognition databases, emotion recognition in the workplace and education institutions, biometric categorization, and real-time remote biometric identification (Articles 5(1)(d) – (h)). Responsibility for monitoring compliance with Articles 5(1)(a) and (b) lies with the Audiovisual and Digital Communication Regulatory Authority and the Directorate General for Competition, Consumer Affairs and Fraud Control. Here we can also see responsibility for monitoring prohibited practices being assigned to more than one regulator, depending on their existing area(s) of regulatory focus. 

Finally, the Guidelines state that non-compliance with the AI Act’s prohibitions constitute the “most severe infringement” of the law, and is therefore subject to the highest fines. Providers and deployers engaging in prohibited AI practices can be fined up to EUR 35 000 000 or 7% of total worldwide annual turnover, whichever is the highest. 

  1. Closing reflections and key takeaways

The AI Act doesn’t prohibit technology, but uses or practices of technology that pose unacceptable risk

Article 5 of the AI Act is broadly framed as such that technologies or AI systems themselves are not directly prohibited, but “practices” involving specific AI systems that pose unacceptable risk are. Such systems are, in turn, tied to certain actions, specifically to “placing on the market, putting into service or use” of an AI system. These actions are also interpreted broadly such that, for example, the “use” of an AI system also includes its intended use and potential misuse. The broad framing ensures that both providers and deployers of AI systems consider all phases of the AI lifecycle and approach compliance in a proportionate manner, taking into account “who in the value chain is best placed to adopt a mitigating or preventive measure.”

Practices of “General Purpose AI Systems” may also fall under the “prohibitions” of the EU AI Act

Equally of note is that the Guidelines extend the Article 5 prohibitions to practices related to any AI system, including general-purpose AI systems (rather than models themselves), even though such systems are not expressly mentioned in the AI Act provision. The Guidelines acknowledge that while harm often arises from the way specific AI systems are used in practice, both deployers and providers have a responsibility not to place on the market or put into service AI systems, including general-purpose AI systems, that are “reasonably likely” to behave in ways prohibited by Article 5 of the AI Act. 

“In-house development” is at the same time excluded from the application of the AI Act and included in the “putting into service” definition in the Guidelines, needing further clarification

As shown above, the Guidelines provide clarifications about what “placing on the market”, “putting into service” and “use” of an AI system mean, which reveal a broad interpretation of the legal definitions enshrined in the AI Act. Notably, “putting into service” is expanded to mean not only “supply for first use”, but also “in-house development or deployment” (see Section 2.1 above). At the same time, Article 2(8) of the AI Act excludes from the scope of application of the regulation any “testing or development activity” regarding AI systems and models “prior to their being placed on the market or put into service”. Further clarification from the European Commission about this part of the Guidelines is needed for legal certainty.

The interplay of the prohibitions under the AI Act and the GDPR needs legal certainty

The Commission’s Guidelines on the AI Act’s prohibitions adopt a scaled approach to delineating, based on the level of risk, which AI practices or uses may be outright prohibited and which may instead fall under the Article 6 high-risk designation. The logic of the scaled approach also extends beyond the AI Act, as the Guidelines caution that while an AI practice may not fall under the Article 5 prohibitions, it may still be unlawful under other Union laws, such as the GDPR and DSA. What is not as clear, though, is what would happen if an AI practice potentially prohibited under the AI Act would otherwise be allowed by other legislation designated as prevailing over the AI Act, and particularly the GDPR. For example, Data Protection Authorities have allowed, in the past, some facial recognition systems to be used, and have found fixable infractions related to the use of emotion recognition systems, showing that such systems could be lawful under the GDPR if all conditions highlighted in the decision would be met. The European Data Protection Board could support consistency of interpretation and application of the two legal regimes with dedicated guidelines.

The enforcement architecture of prohibited AI practices exhibits significant decentralization and fragmentation, including at national level

There are two layers of decentralization of the enforcement architecture for the prohibited AI practices: first, they are primarily left to national competent authorities as opposed to a centralized authority at EU level; second, at national level, multiple authorities have often been designated within one jurisdiction, as the cases of Ireland and France described above show. This level of decentralization is expected to lead to fragmentation of how the relevant provisions of the AI Act are applied. This landscape is further complicated by the interplay of the prohibitions under the AI Act and the GDPR, through the role of supervisory authorities over processing of personal data and their independence as guaranteed by Article 16(2) of the Treaty on the Functioning of the European Union and Article 8(3) of the EU Charter of Fundamental Rights. 

Finally, besides the close interaction between the various provisions of the AI Act themselves, the Guidelines also highlight the significant interplay between the Act and other Union laws. The ways in which these interactions may play out in the context of the several prohibited practices, such as emotion recognition and real-time biometric surveillance, will be explored in more detail in future blog posts in this series. Meanwhile, a deep dive into the broad framing of the AI Act’s prohibited practices reveals that a similarly broad understanding of the data protection acquis and EU digital rulebook is required in order to fully make sense of, and comply with, key obligations for the development and deployment of AI systems across the Union. 

  1.  See para. 13 of the Guidelines, p. 4. ↩︎

Paradigm Shift in the Palmetto State: A New Approach to Online Protection-by-Design

South Carolina Governor McMaster signed HB 3431, an Age-Appropriate Design Code (AADC) -style law, on February 5, adding to the growing list of new, bipartisan state frameworks fortifying online protections for minors. Although HB 3431 is dubbed an AADC, its divergence from past models and unique blend of requirements that draw upon a variety of other state laws may signal that youth privacy- and safety-by-design frameworks are undergoing a paradigm shift away from “AADCs” and into a new model for online protections entirely. South Carolina’s novel approach evolves the online design code schema from approaches seen in other jurisdictions through its focus on both privacy and safety risks, the way covered services must address those risks, the kinds of safeguards online services should provide to users and minors, enforcement priorities, and navigating constitutional pitfalls. 

For compliance teams, the need to unpack the law’s unique provisions is urgent since the law took effect upon approval by the Governor, meaning these requirements are now in effect. Further complicating the timing of compliance considerations, NetChoice filed a lawsuit on February 9 challenging the constitutionality of the Act on First Amendment and Commerce Clause grounds. NetChoice has requested a preliminary injunction to block enforcement of the law as litigation progresses. However, with an unclear litigation timeline, several newly effective legal obligations, and significant enforcement provisions carrying personal liability for employees, compliance teams may be stuck between two high-stakes options: (1) a risk of insufficient action and consequential liability if entities are slower to come into compliance while monitoring litigation outcomes; or, (2) a risk of sunken compliance costs that could have been invested in other important compliance and trust and safety operations if they invest heavily into compliance now and the law is later overturned.

This blog post covers a few key takeaways, including:

Please see our comparison chart for a full side-by-side analysis of how South Carolina’s approach compares against other state law protections for minors online.

Download the Chart here

Scope

South Carolina’s Act applies to any legal entity that owns, operates, controls, or provides an online service reasonably likely to be accessed by minors. Whereas prior comparable state laws typically limited the scope to for-profit entities, South Carolina seemingly extends application to non-profit and other non-commercial entities. This approach mirrors the legal entity framing adopted in Vermont’s and Nebraska’s AADCs, though those laws include narrower applicability thresholds. With respect to applicability threshold criteria, South Carolina aligns with the model set out in Maryland’s AADC, applying to entities that meet any one of the following: (1) $25 million or more in gross annual revenue; (2) the buying, selling, receiving, or sharing of personal data of more than 50,000 individuals; or (3) deriving more than 50 percent of annual revenue from the sale or sharing of personal data. 

An Evolving Approach to Design Protections & Enforcement

Duty of Care

Similar to Vermont’s AADC and state comprehensive privacy laws that incorporate heightened protections for minors, such as Connecticut and Colorado, South Carolina imposes a duty of care on covered online services. Significantly, South Carolina’s duty requires entities to exercise reasonable care to prevent heightened risks of harm to minors, including compulsive use, identity theft, discrimination, and severe psychological harm, among others. The obligation to “prevent” harms to minors diverges sharply from comparable duties of care which only require entities to “mitigate” risks–seemingly placing a higher bar on entities’ compliance efforts compared to other online protection frameworks. Moreover, South Carolina includes two disclaimers regarding the application of the duty of care, including: (1) clarifying that “harm” is limited to circumstances not precluded by Section 230; and, (2) clarifying that entities are not required to prevent minors from intentionally “searching for content related to the mitigation of the described harms.” 

Mandatory Tools & Default Settings

South Carolina takes a Nebraska AADC-style approach to requiring comprehensive tools and protective default settings for minors–but with a twist. Notably, South Carolina requires covered services to provide extensive tools to all users of an online service, such as tools for disabling unnecessary design features, opting-out of personalized recommendation systems (except for tailoring based on explicit preferences), and limiting the amount of time spent on a service or platform. For minors, the Act requires covered services to enable all tools by default, functionally achieving the same goals as high default settings requirements in other frameworks, like Vermont’s and Maryland’s AADCs. Additionally, South Carolina includes prescriptive requirements for the kinds of parental tools businesses must build and provide for parents to monitor and further limit minors’ use of online services–seemingly inspired by the parental tools obligations proposed by the KOSA. Importantly, businesses in scope of several minor online protection frameworks should pay close attention to South Carolina’s expansive mandatory tools and default settings requirements–and the range of users for which these tools must be available–when assessing compliance impacts. 

Processing Restrictions

South Carolina’s new law includes a common component of other minor online protection frameworks: normative processing restrictions limiting the way covered online services can collect and use minors’ data, including restrictions on profiling and geolocation data tracking and a prohibition on targeted advertising. Notably, similar to Nebraska’s AADC, South Carolina also broadly prohibits covered entities’ use of dark patterns on a service. This goes far beyond many other privacy laws that instead prohibit dark patterns only insofar as they are used in obtaining consent or collecting personal data. Although the law as a whole is subject to Attorney General enforcement, South Carolina’s Act singles out the dark patterns prohibition as a violation of the South Carolina Unfair Trade Practices Act, which includes a private right of action.

Third Party Audits

One of the key issues hampering states’ implementation of AADC frameworks has been legal challenges to requirements for service providers to perform data protection impact assessments (DPIAs). The DPIA rules typically require covered online services to assess the likelihood of harm to children. For example, California’s AADC has been subject to litigation because, among other things, it included a requirement for businesses to assess and limit the exposure of children to “potentially” harmful content. The Ninth Circuit held that assessments that require a company to opine on content-based harms are constitutionally problematic, but it did not hold that DPIAs are entirely unconstitutional–yet the litigation caused some proponents of AADC-style laws to explore alternatives to DPIAs

Within this dynamic constitutional landscape, South Carolina shifts away from requiring covered entities to internally assess harms through DPIAs and instead requires covered entities to undergo annual third-party audits and publicly disclose the reports. Those audits must include detailed information on various aspects of the online service as it pertains to minors, including the purpose of the online service, for what purpose the online service uses minors’ personal and sensitive data, whether the service uses “covered design features” (e.g., infinite scroll, autoplay, notifications/alerts, appearance-altering filters, etc.), and a description of algorithms (an undefined term) used by the covered online service. This shift towards public disclosure of service assessment information may cause notable compliance difficulties and raise trade secret questions for covered online services, although it is unclear whether this unique ‘third-party audits’ approach addresses the underlying constitutional concerns highlighted in state AADC litigation. 

Enforcement

South Carolina authorizes the Attorney General to enforce the Act, allowing for treble financial damages for violations. Most significantly, South Carolina also authorizes the Attorney General to hold officers and employees personally liable for “willful and wanton” violations–a novel and severe enforcement mechanism not employed in comparable frameworks. However, personal liability for employees and officers is not entirely unheard of in the broader consumer protection and digital services enforcement context. For example, in an aggressive enforcement approach advanced by the Federal Trade Commission (FTC) under Chair Lina Khan, the agency pursued personal liability against senior executives at a public company for violations of the FTC Act. In a more recent example, the Kentucky Attorney General filed a consumer protection lawsuit against Character.AI and its founders alleging the company knowingly harmed minors in the operation of its companion chatbot product, exposing minors to “sexual conduct, exploitation, and substance abuse.” 

Conclusion

By adopting its novel approach, South Carolina adds to a growing state-level experiment that seeks to establish obligations to address and disclose risks of harm in online services and afford greater protections for minors with constitutional constraints. South Carolina’s novel blend of different state-level models, unique take on service assessments, and unusual enforcement approach may signal a broader fragmentation of online youth protection frameworks into three increasingly defined models: (1) data management-oriented heightened protections for minors embedded in state privacy laws; (2) age appropriate design codes that impose a fiduciary duty to act in children’s best interests, require age-appropriate design, and mandate DPIAs to assess foreseeable harms; and, (3) a “protective design” model exemplified by South Carolina, that synthesizes elements observed in first two while uniquely integrating privacy and safety obligations. It remains to be seen how the emerging protective design model may influence ongoing state legislative efforts, impact business compliance efforts, and measure-up against potential constitutional scrutiny.

From Chatbot to Checkout: Who Pays When Transactional Agents Play?

Disclaimer: Please note that nothing below should be construed as legal advice. 

If 2025 was the year of agentic systems, 2026 may be the year these technologies reshape e-commerce. Agentic AI systems are defined by the ability to complete more complex, multi-step tasks, and exhibit greater autonomy over how to achieve user goals. As these systems have advanced, technology providers have been exploring the nexus between AI technologies and online commerce, with many launching purchase features and partnering with established retailers to offer shopping experiences within generative AI platforms. In doing so, these companies have also relied on developments in foundational protocols (e.g., Google’s Agent Payment Protocol) that seek to enable agentic systems to make purchases on a person’s behalf (“transactional agents”). But LLM-based systems like transactional agents can make mistakes, which raises questions about what laws apply to transactional agents and who is responsible when these systems make errors. 

This blog post examines the emerging ecosystem of transactional agents, including examples of companies that have introduced these technologies and the protocols underpinning them. Existing US laws governing online transactions, such as the Uniform Electronic Transactions Act (UETA), apply to agentic commerce, including in situations where these systems make errors. Transactional agent providers are complying with these laws and otherwise managing risks through various means, including contractual terms, error prevention features, and action logs. 

How is the Transactional Agent Ecosystem Evolving? 

Several AI and technology companies have unveiled transactional agents over the past year that enable consumers to purchase goods within their interfaces rather than having to visit individual merchants’ websites. For example, OpenAI added native checkout features into its LLM-based chatbot that hundreds of millions of consumers already use, and Perplexity introduced similar features for paid users that can find products and store payment information to enable purchases. Amazon has also released a “Buy For Me” feature, which involves an agentic system that sends payment and shipping address information to third party merchants so that Amazon’s users can buy these merchants’ goods on Amazon’s website. 

Many of these same companies are developing frameworks and protocols (e.g., A2A, AP2, UCP, ACP, and MCP) that can combine to facilitate transactional agents across e-commerce. At the same time, merchants are modifying their experiences to ensure their goods can reach transactional agent users.  

Application of Existing Laws (such as the Uniform Electronic Transactions Act)

As consumer-facing tools for agentic commerce develop, questions will arise about who is responsible when transactional agents inevitably make mistakes. Are users responsible for erroneous purchases that a transactional agent may make on their behalf? In these cases, long-standing statutes governing electronic transactions apply. The Uniform Electronic Transactions Act (UETA), a model law adopted by 49 out of 50 U.S. states, sets forth rules governing the validity of contracts undertaken by electronic means, and suggests that consumer transactions conducted by an agentic system can be considered valid transactions.

First, the UETA has provisions that apply to “electronic agents,” which are defined as “a computer program or an electronic or other automated means used independently to initiate an action or respond to electronic records or performances in whole or in part, without review or action by an individual.” This is a broad, technology-neutral definition that is not reserved for AI. It encompasses a range of machine-to-machine and human-to-machine technologies, such as automated supply chain procurement and signing up for subscriptions online. The latest transactional agents can take an increasing set of actions on a user’s behalf without oversight, such as finding and executing purchases, so these technologies could potentially qualify as transactional agents. 

This means that transactional agents can probably enter into binding transactions on a person’s behalf. Section 14 of the UETA indicates that this can occur even without human review when two entities use agentic systems to transact on their behalf (e.g., an individual user of a system that buys goods on their behalf and that of an e-commerce platform that can negotiate order quantity and price). At a time where agentic systems representing distinct parties interacting with each other are edging closer to reality, these systems could bind the user to contracts undertaken on their behalf despite the lack of human oversight. However, a significant caveat is that the UETA also says that individuals may avoid transactions entered into by transactional agents if they were not given “an opportunity for the prevention or correction of [an] error . . . .” This is true even if the user made the error. 

Finally, even if an agentic transaction is deemed valid and a mistake is not made, other legal protections may apply in the event of consumer harm. For example, a transactional agent provider that requires third parties to pay for their goods to be listed by the agent, or gives preference to its own goods, may violate antitrust and consumer protection law. There is also a growing debate over the application of other longstanding common law protections, such as fiduciary duties and “agency law.”

What Risk Management Steps are Transactional Agent Providers Taking to Manage Responsibility?

Managing responsibility for transactional agents can take varied forms, including contractual disclaimers and limitations, protocols that signal to third parties an agentic system’s authorization to act on a user’s behalf, as well as design decisions that reduce the likelihood of transactions being voided when errors occur (e.g., confirmation prompts that require users to authorize purchases):

Conclusion

Organizations are increasingly rolling out features that enable agentic systems to buy goods and services. These current and near-future technologies introduce uncertainty about who is responsible for agentic system transactions, including when mistakes are made, which is leading providers to integrate error prevention features, contractual disclaimers, and other legal and technical measures to manage and allocate risks. 

Looking ahead, there will be many more privacy, data governance, and risk management challenges to address. The uptake of transactional agents raises data governance considerations. As these technologies become more autonomous, organizations must decide to what extent transactional agents proactively infer consumer preferences and adapt actions based on their impact on a user’s financial wellbeing. Publishers and retailers also face the challenge of how to let transactional agents interact with their websites. This particular issue has fed tensions over who owns the direct consumer relationship in an agentic world (e.g., is it online marketplaces and information aggregators or the agentic system’s provider?). Even with applicable laws for transactional agents, the evolution of these technologies (e.g., less human oversight) and increased investment in these technologies will create new legal challenges for practitioners to address. 

FPF Retrospective: U.S. Privacy Enforcement in 2025

The U.S. privacy law landscape continues to mature as new laws go into effect, cure periods expire, and regulators interpret the law through enforcement actions and guidance. State attorneys general and the Federal Trade Commission act as the country’s de facto privacy regulators, regularly bringing enforcement actions under legal authorities both old and new. For privacy compliance programs, this steady stream of regulatory activity both clarifies existing responsibilities and raises new questions and obligations. FPF’s U.S. Policy team has compiled a retrospective looking back at enforcement activity in 2025 and outlining key trends and insights.

Read the retrospective here

Looking at both substantive areas of focus in enforcement actions and the level of activity by different enforcers, the retrospective identified four notable trends in 2025: 

  1. California and Texas Lead Growing Public Enforcement of Comprehensive Privacy Laws: Comprehensive privacy laws may finally be moving from a period of legislative activity into a new era where enforcement is shaping the laws’ meaning, as 2025 saw a significant increase in the number of public enforcement actions.
  2. States Demonstrate Increasing Concern for Kids’ and Teens’ Online Privacy and Safety: As legislators continue to consider broad youth privacy and online safety legal frameworks, enforcers too are looking at how to protect the youth online. Bringing claims under existing state laws, including privacy and UDAP, regulators are paying close attention to opt-in consent requirements, protections for teenagers in addition to children under 13, and the online safety practices of social media and gaming services. 
  3. U.S. Regulators Go Full Speed Ahead on Location and Driving Data Enforcement: Building on recent enforcement actions concerning data brokerage and location privacy, federal and state enforcers have expanded their consumer protection enforcement strategy to focus also on first-party data collectors and the collection of “driving data.”
  4. FTC Prioritizes Enforcement on Harms to Kids and Teens, and Deceptive AI Marketing, Under New Administration: The FTC transitioned leadership in 2025, moving into a new era under Chair Andrew Ferguson that included a shift toward targeted enforcement activity focused  on ensuring children’s and teens’ privacy and safety, and  “promoting innovation” by addressing deceptive claims about the capabilities of AI-enabled products and services.

There are several practical takeaways that compliance teams can draw from these trends: obtaining required consent prior to processing sensitive data, including through oversight of vendors’ consent practices, identification of known children, and awareness of laws with broader consent requirements; ensuring that consumer controls and rights mechanisms are operational; avoiding design choices that could mislead consumers; considering if and when to deploy age assurance technologies and how to do so in an effective and privacy-protective manner; and avoiding making deceptive claims about AI products.

2026: A Year at the Crossroads for Global Data Protection and Privacy

There are three forces twirling and swirling to create a perfect storm for global data protection and privacy this year: the surprise reopening of the General Data Protection Regulation (GDPR) which will largely play out in Brussels over the following months, the complexity and velocity of AI developments, and the push and pull over the field by increasingly substantial adjacent digital and tech regulations. 

All of this will play out with geopolitics taking center stage. At the confluence of some of these developments, the protection of children online and cross-border data transfers – with their other side of the coin, data localization in the broader context of digital sovereignty, will be two major areas of focus.

1. The GDPR reform, with an eye on global ripple effects

The gradual reopening of the GDPR last year came as a surprise, without much debate or public consultation, if any. It passed its periodic evaluation in the summer of 2024 with a recommendation for more guidance and better implementation to suit SMEs and harmonization across the EU, as opposed to re-opening or amending it. Moreover, exactly one year ago, in January 2025, at CPDP-Data Protection Day Conference in Brussels, not one, but two representatives of the European Commission, in two different panels (one of which I moderated) were very clear that the Commission had no intention to re-open the GDPR. 

Despite this, a minor intervention was first proposed in May to tweak the size of entities under the obligation to keep a register of processing activities through one of the simplification Omnibus packages of the Commission. But this proved to just crack the door open for more significant amendments to the GDPR proposed later on, under the broad umbrella of competitiveness and regulatory simplification the Commission started to pursue emphatically. Towards the end of the year, in November 2025, major interventions were introduced within another simplification Omnibus dedicated to digital regulation. 

There are two significant policy shifts the GDPR Omnibus proposes that should be expected to reverberate in data protection laws around the world in the next few years. First, it entertains the end of technology-neutral data protection law. AI – the technology, is imprinted all over the proposed amendments, from the inconspicuous ones, like the new definition proposed for “scientific research”, to the express mentioning of “AI systems” in new rules created to facilitate their “training and operations” – including in relation to allowing the use of sensitive data and to recognizing a specific legitimate interest for processing personal data for this purpose. 

The second policy shift – and perhaps the most consequential for the rest of the data protection world, is the narrowing down of what constitutes “personal data”, by adding several sentences to the existing definition to transpose what resembles the relative approach to de-identification which was confirmed by the Court of Justice of the EU (CJEU) in the SRB case this September. To a certain degree, the proposed changes bring the definition to pre-GDPR days, when some data protection authorities were indeed applying a relative approach in their regulatory activity. 

The new definition technically adds that the holder of key-coded data or other information about an identifiable person, which does not have means reasonably likely to be used to identify that person, does not process personal data even if “potential subsequent recipients” can identify the person to whom the data relates. Processing of this data, including publishing it or sharing it with such recipients, would thus be outside of the scope of the GDPR and any accountability obligations that follow from it.

If the language proposed will end up in the GDPR, this would likely mark a narrowing of the scope of application of the law, leaving little room for supervisory authorities to apply the relative approach on a case-by-case basis following the test that the CJEU proposed in SRB. This is particularly notable, considering that the GDPR has successfully exported the current philosophy and much of the wording of the broad definition of personal data (particularly its “identifiability” component) to most data protection laws adopted or updated around the world since 2016, from California, to Brazil, to China, to India.

The ripple effects around the world of such significant modifications of the GDPR would not be felt immediately, but in the years to come. Hence, the legislative process unfolding this year in Brussels on the GDPR Omnibus should be followed closely. 

2. The Complexity and Velocity of AI developments: Shifting from regulating data to regulating models?

There is a lot to unpack here, almost too much. And this is at the core of why AI developments have an outsized impact on data protection. There is a lot of complexity related to understanding the data flows and processes underpinning the lifecycle of the various AI technologies, making it very difficult to untangle the ways in which data protection is applicable to them. On top of it, the speed with which AI evolves is staggering. This being said, there are a couple of particularly interesting issues at the intersection of AI and data protection to be necessarily followed this year, with an eye towards the following years too.  

One of them is the intriguing question of whether AI models are the new “data” in data protection. Some of you certainly remember the big debate of 2024: do Large Language Models (LLMs) process personal data within the model? While it was largely accepted that personal data is processed during training of LLMs and may be processed as output of queries done within LLMs, it was not at all clear that any of the informational elements related to AI models post-training, like tokens, vectors, embeddings or weights, can amount by themselves or in some combination to personal data (or not). The question was supposed to be solved by an Opinion of the European Data Protection Board (EDPB) solicited by the Irish Data Protection Commission, which was published in December 2024.

Instead, the Opinion painted a convoluted regulatory answer by offering that “AI models trained on personal data cannot, in all cases, be considered anonymous”. The EDPB then dedicated most of the Opinion on laying out criteria that can help assess whether AI models are anonymous or not. While most, if not all of the commentary around the Opinion usually focuses on the merits of these criteria, one should perhaps stop and first reflect on the framework of the analysis – namely assessing the nature of the model itself rather than the nature of the bits and pieces of information within the model. 

The EDPB did not offer any exploration of what non-anonymous (so, then, personal?) AI models might mean for the broader application of data protection law, such as data subject rights. But with it, the EDPB may have – intentionally or not, started a paradigm shift for data protection in the context of AI, signaling a possible move from the regulation of personal data items to the regulation of “personal” AI models. However, the Opinion was ostensibly shelved throughout last year as it did not seem to appear in any regulatory action yet. I would have forgotten about it myself if not for a judgment of a Court in Munich in November 2025, in an IP case related to LLMs.  

The German Court found that song lyrics in a training dataset for an LLM were “reproducibly contained and fixed in the model weights”, with the judgment specifically referring to how models themselves are “copies” of those lyrics within the meaning of the relevant copyright law. This is because of the “memorization” of the lyrics in the training data by the model, where weights and vectors are “physical fixations” of the lyrics. This judgment is not final, with a pending appeal. But it will be interesting to see whether this perspective of focusing on the models themselves as opposed to bits of data within them will find more ground this year and immediately following ones, pushing for legal reform, or will fizzle out due to over-complexity of making it fit within current legal frameworks. 

Key AI developments which might push the limits of existing data protection and privacy frameworks to a breaking point, as they descend from research to market, will be: 

3. A concert of laws adjacent to data protection and privacy steadily becoming the digital regulation establishment 

A third force pressing onto data protection for the foreseeable future are all the novel data-and-digital adjacent regulatory efforts solidifying into a new establishment of digital regulation, with their own bureaucracies, vocabulary and compliance infrastructure: online safety laws – including their branch of children’s online safety laws, digital markets laws, data laws focusing on data sharing or data strategies including personal and non-personal data, and the proliferation of AI laws, from baseline acts to sectoral or issue-specific laws (focusing on single issues, like transparency). 

It may have started in the EU five years ago, but this is now a global phenomenon. Look, for instance, at Japan’s Mobile Software Competition Act, a law regulating competition in  digital markets focusing on mobile environments which became effective in December 2025 and draws strong comparisons with the EU Digital Markets Act. Or at Vietnam’s Data Law which became effective in July 2025 and is a comprehensive framework for the governance of digital data, both personal and non-personal, applying in parallel to its new Data Protection Law.

Children’s online safety is taking increasingly more space in the world of digital regulatory frameworks, and its overlap and interaction with data protection law could not be clearer than in Brazil. A comprehensive law for children’s online safety, the Digital ECA, was passed at the end of last year and it is slated to be enforced by the Brazilian Data Protection Authority starting this spring. 

It brings interesting innovations, like a novel standard for such laws to be triggered – “likelihood of access” of a technology service or product by minors, or “age rating” for digital services, requiring providers to maintain age rating policies and continuously assess their content based on it. It also provides for “online safety by design and by default” as an obligation for digital services providers. From state level legislation in the US on “age appropriate design”, to an executive decree in UAE on “child digital safety” – the pace of adopting online safety laws for children is ramping up. What makes these laws more impactful is also the fact that age limits of minors falling under these rules are growing to capture teenagers up until 16 and even 18 year-olds in some places, bringing vastly more service providers in scope than first generation children online safety regulations.  

The overlap, intersection and even tensions of all these laws with data protection become increasingly visible. See, for instance, the recent Russmedia judgment of the CJEU, which established that an online marketplace is a joint-controller under the GDPR and it has obligations in relation to sensitive personal data published by a user, with consequences for intermediary liability that are expected to reverberate at the intersection of the GDPR and Digital Services Act in practice. 

The compliance infrastructure of this new generation of digital laws and its need for resources (human resources, budget) break their way into an already stretched field of “privacy programs”, “privacy professionals”, and regulators, with the visible risks of moving attention from, and diluting meaningful measures and controls stemming from privacy and data protection laws. 

4. Breaking the fourth wall: Geopolitics

While all these developments play out, it is particularly important to be aware that they unfold on a geopolitical stage that is unpredictable and constantly shifting, resulting in various notions of “digital sovereignty” taking root from Europe, to Africa, to elsewhere around the world. From a data protection perspective, and in the absence of a comprehensive understanding of what “digital sovereignty” might mean, this could translate into a realignment of international data transfers rules through more data localization measures, more data transfers arrangements following trade agreements, or more regional free data flows arrangements among aligned countries. 

Ten years after the GDPR was adopted as a modern upgrade of 1980s-style data protection laws for the online era, successfully promoting fair information practice principles, data subject rights and the “privacy profession” around the world, data protection and privacy are at an inflection point: either hold the line and evolve to meet these challenges, or melt away in a sea of new digital laws and technological developments.

6 Privacy Tips for the Generative AI Era

Data Privacy Day, or Data Protection Day in Europe, is recognized annually on January 28 to mark the anniversary of Convention 108, the first binding international treaty to protect personal data. The Council of Europe initiated the day in 2006, with the first official celebration held on January 28, 2007, marking this year as the 19th anniversary of celebration. Companies and organizations around the world often devote time for internal privacy training during this week, working to improve awareness of key data protections issues for their staff.

It’s also a good time for all of us to think about our own sharing of personal data. Nowadays, one of the most important decisions we need to make about our data is when and how we use AI-powered services. To raise awareness, we’ve partnered with Snap to create a Data Privacy Day Snapchat Lens. Check it out by scanning the Snapchat code and learn more below about privacy tips for generative AI! 

snap lens 2026

  1. Know When You’re Using Generative AI

As a first step, it’s important to know what generative AI is and when you’re using it. Generative AI is a type of artificial intelligence that creates original text, images, audio, and code in response to input. In addition to visiting dedicated generative AI platforms (such as ChatGPT), you may find that many companies’ existing functionality now also includes generative AI capabilities. For example, a search in Google now provides answers powered by Google’s generative AI, Gemini. Other examples include Snap’s AI Lenses and AI Snaps in creative tools, and Adobe’s Acrobat and Express are now powered with Firefly, Adobe’s generative AI. X’s Grok now assists users and answers questions. 

One of the best ways to identify when you’re using generative AI is to look for a symbol or disclaimer. Many organizations provide clues like symbols, and a range of companies like Snap, Github, and many others often use either a sparkle or star icon to denote generative AI features. You might also notice labels like “AI-generated” or “Experimental” alongside results from some companies, including Meta. 

  1. Think Carefully Before You Share Sensitive or Private Information

While this is a general rule of thumb for interacting with any product, it’s especially important when using generative AI because most generative AI systems use data that users provide (such as conversation text or images) to allow their models to continuously learn and improve. While your prompts, generated images, and other pieces of data can improve the technology for all users, it also means that if you share sensitive or private information, it could potentially be shared or surfaced in connection with training and developing the algorithm. 

Be especially careful when uploading files, images, or screenshots to generative AI tools. Documents, photos, or screenshots can include more information than you realize, such as metadata, background details, or information about third parties. Before uploading, consider redacting, cropping, or otherwise limiting files to include only the information necessary for your task.

Some companies promise to not use your data for training, often if you are using the paid version of their service. Others provide an option to opt-out of use of your data for training or versions that have special protections. For example, ChatGPT’s new health service supports the upload of health records with additional privacy and security commitments, but you need to be sure to be using the specific Health tab that is being rolled out to users.

  1. Manage Your AI’s Memory

Many generative AI tools now feature a memory function that allows them to remember details about you over time, providing more tailored responses. While this can be helpful for maintaining context in long-term projects, such as remembering your writing style, professional background, or specific project goals, it also creates a digital record of your preferences and behaviors. A recent FPF report explores these different kinds of personalization. 

Fortunately, you typically have the power to control what Generative AI platforms remember. Most have settings to view, edit, or delete specific memories or to turn the feature off entirely. For instance, in ChatGPT, you can manage these details under Settings > Personalization, and Gemini allows you to toggle off “Your past chats” within its activity settings to prevent long-term tracking. Meta also provides options for deleting all chats and images from the Meta AI app. Another option is to use “Temporary” or “Incognito” modes, so you can enjoy a personalized experience without generative AI compiling data attributed to your profile. 

In addition to managing memory features, it’s also helpful to understand how long Generative AI services keep your data. Some platforms store conversations, images, or files for only a short time, while others may keep them longer unless you choose to delete them. Taking a moment to review retention timelines can give you a clearer picture of how long your information sticks around, and help you decide what you’re comfortable sharing.

  1. Define Boundaries for Agentic AI 

Agentic AI, a form of generative AI that can complete tasks for users with greater autonomy, is becoming increasingly popular. For example, companies like Perplexity, OpenAI, and Amazon have unveiled agentic systems that can make purchases for consumers. While these systems can take on more tasks, they still require users to review purchases before they are final. As a best practice, you should look over the purchase to check that it aligns with your expectations (e.g., ordering 1 pair of socks and not 10). It is also important to keep in mind that since agentic systems can pull information from third party sources, there is a risk that the system will rely on inaccurate information about a product during purchases (e.g., that an item is in stock).

As agentic systems become more embedded in our lives, you should also be mindful about how much information you share with them. Consumers are already disclosing sensitive details about themselves to more basic chatbots, which businesses, the government, and other third parties may want to access. When interacting with agentic systems, keep this in mind and pay attention to what you disclose about yourself and others. You may similarly want to consider what type of access to provide to the agentic AI product, and rely on the principle of least privilege–only providing the minimum access needed for your use. For example, if an agentic system is going to manage your calendar, think through options for narrowing the access so your entire calendar is not shared, and that other apps connected to your calendar, like your email, are not shared unless necessary.

  1. Review How Generative AI Products Handle Privacy and Safety

It’s important to regularly review the privacy and security practices of any company with which you share information, and this applies similarly to companies offering generative AI products. This can include checking what data is collected and how, as well as how that information is used and stored. 

Snap has a Snapchat Privacy Center where you can review your settings. You can find those choices here.

ChatGPT’s privacy controls are available in the ChatGPT display, and OpenAI has a Data Controls FAQ that outlines where to find the settings and what options are available.

Gemini has the Gemini Privacy Hub, as well as an area to read about and configure your settings for Gemini Apps, which includes options for turning your Gemini history off. 

Claude has a Privacy Settings & Controls page that outlines how long they store your data, how you can delete it, and more. 

Co-Pilot provides an array of options for reviewing and updating your privacy settings, including how to delete specific memories and how your data is used. These settings are available on Microsoft’s website, here. Microsoft also provides a detailed Privacy FAQ page as well. 

Keep in mind that Generative AI products change quickly, and new features may introduce new data uses, defaults, or controls. Periodically revisiting privacy and safety settings can help ensure your preferences continue to reflect how the product works today, rather than how it worked when you first configured it.

  1. Explore and Have Fun!

LLMs can often provide useful data protection advice, so ask them questions about AI and privacy. Just be sure to double-check sources and accuracy, especially for important topics!

Data Privacy Day is a reminder that privacy is a shared responsibility. By bringing together FPF’s expertise in privacy research and policy with Snap’s commitment to building products with privacy and safety in mind, this collaboration aims to help people better understand how AI works and how to use it thoughtfully.

FPF Releases Updated Infographic on Age Assurance Technologies, Emerging Standards, and Risk Management

The Future of Privacy Forum is releasing an updated version of its Age Assurance: Technologies and Tradeoffs infographic, reflecting how rapidly the technical and policy landscape has evolved over the past year. As lawmakers, platforms, and regulators increasingly converge on age assurance as a governance tool, the updated infographic sharpens the focus on proportionality, privacy risk, and real-world deployment challenges.

What’s New

The updated infographic introduces several key changes that reflect the current state of age assurance technology and policy:

A Fourth Category: Inference. The original infographic outlined three approaches to age assurance: declaration, estimation, and verification. This update adds a fourth category—inference—which draws reasonable conclusions about a user’s age range based on behavioral signals, account characteristics, or financial transactions. For example, an email address linked to workplace applications, a mortgage lender, or a 401(k) provider, combined with login patterns during business hours, may infer that a user is an adult.

Relatedly, the updated version intentionally downplays age declaration as a standalone solution. While declaration remains useful for low-risk contexts and as an entry point in layered systems, experience and enforcement history continue to show that it is easily bypassed and insufficient where legal or safety obligations attach to age thresholds. The infographic now situates declaration primarily as an initial step within a waterfall or layered approach, rather than as a meaningful assurance mechanism on its own.

The update also highlights several new and emerging potential risks associated with modern age assurance systems. If not addressed properly, these could include loss of anonymity through linkage, increased breach impact from improper secured retained assurance data, secondary data use of assurance data, and circumvention risks such as presentation attacks or shared-device misuse.

In parallel, the infographic expands its coverage of risk management tools that can mitigate these concerns when age assurance is warranted. These include tokenization and zero-knowledge proofs to limit data disclosure, on-device processing and immediate deletion of source data, separation of processing across third parties, user-binding through passkeys or liveness detection, and emerging standards such as ISO/IEC 27566 and IEEE 2089.1. The emphasis is not on eliminating risk—which is rarely possible—but on aligning technical controls with the specific harms a service is attempting to address.

As with prior versions, the updated infographic reinforces a core message: there is no one-size-fits-all age assurance solution. Effective approaches are risk-based, use-case-specific, and privacy-preserving by design, balancing assurance goals against the rights and expectations of users. By clarifying the role of inference, contextualizing declaration, and surfacing both new risks and mitigation strategies, this update aims to support more informed decision-making across policy, product, and engineering teams.

Emerging Age Assurance Concepts. The field has advanced considerably, and the updated infographic now includes a dedicated section on emerging technologies that address Age Signals and Age Tokens, User-Binding, Zero Knowledge Proofs (ZKP), Double-Blind Models and One-Time vs. Reusable Credential.

Updated Risks and Risk Management Approaches. The infographic now presents a more comprehensive view of the risks and challenges associated with age assurance—including excessive data collection and retention, secondary data use, lack of interoperability, false positives and negatives, data breaches, and user acceptance challenges. Correspondingly, the risk management section highlights both established and emerging mitigations: on-device processing, tokenization and zero knowledge proofs, anti-circumvention measures (such as Presentation Attack Detection), standards (ISO/IEC 27566-1, IEEE 2089.1), and certification and auditing.

Practical Example: The updated infographic includes a detailed use case following “Miles,” a 16-year-old accessing an online gaming service. The scenario illustrates how multiple age assurance methods can work together in a layered “waterfall” approach—starting with low-assurance age declaration for basic access, escalating to facial age estimation for age-restricted features, and offering authoritative inference or parental consent as inclusive fallbacks when estimation results are inconclusive and formal id is not available . The example also demonstrates token binding with passkeys, ensuring that even if Miles shares his phone with a younger friend, the age credential cannot be accessed without the correct PIN, pattern, or biometric.

The full updated infographic is available here.

Future of Privacy Forum to Honor Top Scholarship at Annual Privacy Papers for Policymakers Event

Washington D.C. — (January 26th, 2026) — Today, the Future of Privacy Forum (FPF) — a global non-profit that advances principled and pragmatic data protection, AI, and digital governance practices — announced the winners of its 16th annual Privacy Papers for Policymakers (PPPM) Awards.

The PPPM Awards recognize leading research and analytical scholarship in privacy relevant to policymakers in the U.S. and internationally. The award highlights important work that analyzes current and emerging privacy and AI issues and proposes achievable short-term solutions or means of analysis that have the potential to lead to real-world policy solutions. Seven winning papers, two honorable mentions, and one student submission were selected by a select group of FPF staff members and advisors based on originality, applicability to policymaking, and overall quality of writing.

Winning authors will have the opportunity to present their work at virtual webinars scheduled for March 4, 2026, and March 11, 2026.

“As artificial intelligence and data protection increasingly shape global policy discussions, high-quality academic research is more important than ever,” says FPF CEO Jules Polonetsky. “This year’s award recipients offer the kind of careful analysis and independent thinking policymakers rely on to address complex issues in the digital environment. We are pleased to recognize scholars whose work helps ensure that technological innovation develops in ways that remain grounded in privacy and responsible data governance.”

FPF’s 2026 Privacy Papers for Policymakers Award winners are:

In addition to the winning papers, FPF awarded two papers as Honorable Mentions: Brokering Safety by Chinmayi Sharma, Fordham University School of Law; Thomas Kadri, University of Georgia School of Law; and Sam Adler, Fordham University, School of Law; and Focusing Privacy Law by Paul Ohm, Georgetown University Law Center. 

FPF also selected a paper for the Student Paper Award: Decoding Consent Managers under the Digital Personal Data Protection Act, 2023: Empowerment Architecture, Business Models and Incentive Alignment by Aditya Sushant Jain of O.P. Jindal Global University – Jindal Global Law School. 

In reviewing the submissions, winning papers were awarded based on the strength of their research and proposed policy solutions for policymakers and regulators in the U.S. and abroad.

The 2026 Privacy Papers for Policymakers Awards will take place over two virtual events on March 4 and 11. Attendance is free, and registration is open to the public. Find more information to register for the March 4 webinar here, and the March 11 webinar here. 

FPF Releases an Updated Issue Brief on Vietnam’s Law on Protection of Personal Data and the Law on Data

This Issue Brief has been updated to reflect the latest changes introduced by Decree 356/2025, the implementing decree to Vietnam’s Personal Data Protection Law, which was enacted on 31 December 2025.

Vietnam is undergoing a sweeping transformation of its data protection and governance framework. Over the past two years, the country has accelerated its efforts to modernize its regulatory architecture for data, culminating in the passage of two landmark pieces of legislation in 2025: the Law on Personal Data Protection (Law No. 91/2025/QH15) (PDP Law), which elevates the Vietnamese data protection framework from an executive act to a legislative act, while preserving many of the existing provisions, and the Law on Data (Law No. 60/2025/QH15) (Data Law). Notably, the PDP Law is expected to come into effect on January 1st, 2026.

The Data Law is Vietnam’s first comprehensive framework for the governance of digital data (both personal and non-personal), and applies to all Vietnamese agencies, organizations and individuals, as well as foreign agencies, organizations and individuals either in Vietnam or directly participating or are related to digital data activities in Vietnam. The data law became effective in July 2025. Together, these two laws mark a significant legislative shift in how Vietnam approaches data regulation, addressing overlapping domains of data protection, data governance, and emerging technologies. 

Read the Issue Brief here

This Issue Brief analyzes the two laws, which together define a new, comprehensive regime, for data protection and data governance in Vietnam. The key takeaways from this joint analysis show that:

This Issue Brief has three objectives. First, it summarizes key changes between the PDP Law and Vietnam’s existing data protection regime, and draws a comparison between the PDP Law and the EU’s General Data Protection Regulation (GDPR) (Section 1). Second, it analyzes the interplay between the Data Law and the PDP Law (Section 2). We then provide key takeaways for organizations as they navigate the implementation of these laws (Section 3). 

You can also view the previous version of the Issue Brief here.

Innovation and Data Privacy Are Not Natural Enemies: Insights from Korea’s Experience 

The following is a guest post to the FPF blog authored by Dr. Haksoo Ko, Professor at Seoul National University School of Law, FPF Senior Fellow and former Chairperson of South Korea’s Personal Information Protection Commission. The guest post reflects the opinion of the author only and does not necessarily reflect the position or views of FPF and our stakeholder communities. FPF provides this platform to foster diverse perspectives and informed discussion.

1. Introduction: From “trade-off” rhetoric to mechanism design

I served as Chairman of South Korea’s Personal Information Protection Commission (PIPC) between 2022 and 2025. Nearly every day I felt that I was at the intersection of privacy enforcement, artificial intelligence policy, and innovation strategy. I was asked, repeatedly, whether I was a genuine data protectionist or whether I was fully supportive of unhindered data use for innovation. The question reflects a familiar assumption: that there is a dichotomy between robust privacy protection on one hand and rapid AI/data innovation on the other, and that a country must choose between the two.

This analysis draws on the policy-and-practice vantage point that I gained to argue that innovation and privacy are compatible when institutions establish suitable mechanisms that reduce legal uncertainty, while maintaining constructive engagement and dialogue. 

Korea’s recent experience suggests that the “innovation vs. privacy” framing is analytically under-specified. The binding constraint is often not privacy protection as such, but uncertainty as to whether lawful pathways exist for novel data uses. In AI systems, this uncertainty is heightened by the intricate nature of their pipelines. Factors such as large-scale data processing, extensive use of unstructured data, composite modeling approaches, and subsequent fine-tuning or other modifications all contribute to this complexity. The main practical issue is less about choosing among lofty values; it is more about operationalizing workable mechanisms and managing risks under circumstances of rapid technological transformation.

Since 2023, Korea’s trajectory can be read as a pragmatic move toward mechanisms of compatibility—institutional levers that lower the transaction costs of innovative undertakings while preserving proper privacy guardrails. These levers include structured pre-deployment engagement, controlled experimentation environments, risk assessment frameworks that can be translated into repeatable workflows, and a maturing approach to privacy-enhancing technologies (PETs) governance.

Conceptually, the approach aligns with the idea of cooperative regulation: regulators offer clearer pathways and procedural predictability for innovative undertakings, while also deepening their understanding of the technological underpinnings of these new undertakings. 

This article distills the mechanisms Korea has attempted in an effort to operationalize compatibility of privacy protection with the AI-and-data economy. The emphasis is pragmatic: to identify which institutional levers reduce legal and regulatory uncertainty without eroding accountability, and how those levers map to the AI lifecycle.

2. Korea’s baseline architecture of privacy protection

2.1 General statutory backbone and regulatory capacity

Korea maintains an extensive legal framework for data privacy, primarily governed by the Personal Information Protection Act (PIPA), and further reinforced through specific guidance and strong institutional capacity of the PIPC. The PIPA supplies durable principles and enforceable obligations, while guidance and engagement tools translate those principles and statutory obligations into implementable controls in emerging contexts such as generative AI.

The PIPA embeds familiar principles into statutory obligations: purpose limitation, data minimization, transparency, and various data subject rights. In AI settings, the central challenge has been their application: how to interpret these obligations in the context of, e.g., model training and fine-tuning, RAG (retrieval augmented generation), automated decision-making, and AI’s extension into physical AI and various other domains.

2.2 Principle-based approach combined with risk-based operationalization

Korea’s move is not “light-touch privacy,” but a principle-based approach combined with risk-based operationalization. The PIPC concluded that, given the uncertain and hard-to-predict nature of technological developments surrounding AI, adopting a principle-based approach was inevitable: an alternative like a rule-based approach would result in undue rigidity and stifle innovative energy in this fledgling field. At the same time, the PIPC recognized that a major drawback of a principle-based approach could be the lack of specificity and that it was imperative to issue sufficient guidance to show how principles are interpreted and applied in practice. Accordingly, the PIPC embarked on a journey of publishing a series of guidelines on AI.

In formulating and issuing these guidelines, an emphasis was consistently placed on the significance of implementing and operationalizing risk-based approaches. Emphasizing risk-based operationalization has several noteworthy implications. First, risk is a constant feature of new technologies, and pursuing zero risk is not realistic. As such, the focus was directed towards minimizing relevant risks, instead of seeking their complete elimination. Second, as technologies evolve, the resulting risk profile would also change continuously. Thus, putting in place procedures for periodic risk assessment would be crucial so that a proper mechanism for risk management could be at play. Third, a ‘one-size-fits-all’ approach would rarely be suitable, and multiple tailored solutions often need to be applied simultaneously. Furthermore, it is advisable to consider the overall risk profile of an AI system rather than concentrating on a few salient individual risks. This is akin to the Swiss cheese approach in cybersecurity: deploying multiple independent security measures at multiple layers on the assumption that every layer may have unknown vulnerabilities. 

Through a series of guidelines, the PIPC indicated that compliance can be achieved by implementing appropriate safety measures and guardrails that are proportionate to the risks at issue. Some of the guidelines that the PIPC issued include guidelines on pseudonymizing unstructured data, guidelines on utilizing synthetic data, guidelines on data-subject rights in automated decision-making, and guidelines on processing data captured by mobile hardware devices (such as automobiles and delivery robots).

3. Mechanisms of compatibility: What Korea has deployed

The PIPC devised and deployed multiple mechanisms to convert the “innovation vs. privacy” framework into a tractable governance program. They function as a portfolio: some instruments reduce uncertainty through ex ante engagement, while others enable innovative experimentation under structured constraints. Still, others turn principles into repeatable compliance workflows. The PIPC aimed to offer organizations a set of options, acknowledging that, depending on the type of data and the purposes for which the data would be used, different data processing needs would arise. The PIPC recognized that tailored mechanisms would be necessary to address these diverse requirements effectively.

3.1 Case-by-case assessments to reduce uncertainty

AI services could reach the market before regulators can fully resolve novel interpretive questions. In some cases, regulators may commence investigations after new AI services have been launched. As such, businesses may have to accept that they may face regulatory scrutiny ex post. The uncertainty resulting from this unpredictability could make innovators hesitant to launch new services. Accordingly, the PIPC has implemented targeted engagement mechanisms designed to deliver timely and effective responses on an individual basis. For organizations, this would provide predictability, in an expedited manner. The PIPC, on the other hand, through these mechanisms, would gain in-depth information about the intricate details and inner workings of new AI systems. By adopting this approach, the PIPC could develop the necessary expertise to make well-informed decisions that are consistent with current technological realities. The following provides an overview of several mechanisms that have been implemented.

(1) “Prior adequacy review”: Structured pre-deployment engagement

A “prior adequacy review” refers to a structured pre-deployment engagement pathway. The participating business would, on a voluntary basis, propose a data processing design and safeguard package in consideration of the risks involved; the PIPC would then evaluate the adequacy of the proposal against the identified risks; and, if deemed adequate, the PIPC would provide ex ante comfort that the proposed package aligns with the PIPC’s interpretation of the law.

The discipline is the trade: reduced uncertainty in exchange for concrete safeguards and future audits. Safeguard packages could include structured data sourcing and documentation, minimization and de-identification of data where feasible, strict access control, privacy testing and red-teaming for model outputs, input and output filtering for data privacy, and/or structured handling of data-subjects’ requests.

More than a dozen businesses have used this mechanism as they prepared to launch new services. One example is Meta’s launch of a service in Korea for screening and identifying fraudulent advertisements using celebrities’ images without their authorization. While there was a concern about the legality of processing someone’s images without his/her consent, the issue was resolved, in part, by considering the technological aspect that can be called the “temporary embedding” of images. 

(2) “No action letters” and conditional regulatory signaling

A “no action letter” is another form of regulatory signaling: under specified facts and conditions, the PIPC clarifies that it will not initiate an enforcement action. The overall process for a “no action letter” is much simpler than for a prior adequacy review. Its development was informed by the “no action letter” framework, which is widely used in the financial sector.

Where used, its value is to reduce uncertainty significantly to an articulated set of commitments. Although preparatory work had taken place earlier, the mechanism was officially implemented in November 2025. The first no action letter was issued in December 2025 for an international research project that used pseudonymized health data of deceased patients. 

(3) “Preliminary fact-finding review” 

A “preliminary fact-finding review” serves as an expedited evaluative process particularly suited to rapidly evolving sectors. Its primary objective is to develop a comprehensive understanding of the operational dynamics within an emerging service category and to identify pertinent privacy concerns. Although this review may result in the issuance of a corrective recommendation, which is a form of an administrative sanction, issuing such a corrective recommendation is typically not a principal motivation for conducting a preliminary fact-finding review.

For organizations, the value of this review process lies in gaining directional clarity without having to worry about the possibility of immediate escalation into a formal investigative proceeding. For the PIPC, the value is an enlightened understanding of market practices, which in turn serves to inform guidance and targeted supervision. 

In early 2024, the PIPC conducted a comprehensive review of several prominent large language models, including those developed or deployed by OpenAI, Microsoft, Google, Meta, and Naver. The assessment focused on data processing practices across pre-training, training, and post-deployment phases. The PIPC issued several minor corrective recommendations. As a result of this review, the businesses obtained legal and regulatory clarity regarding their data processing practices associated with their large language models. 

3.2 Controlled experimentation environments: Providing “playgrounds” for R&D

A second group of mechanisms centers on establishing controlled experimental environments. For instance, in situations requiring direct access to raw data for research and development, policy priorities shift towards enabling experimentation while simultaneously reinforcing safeguards that address the corresponding heightened risks. The following is an overview of several specific mechanisms that were implemented in this regard.

(1) “Personal Data Innovation Zones” 

“Personal Data Innovation Zones” provide secure environments where vetted researchers and firms can work with high-quality data in a relatively flexible manner. The underlying idea is an appropriate risk-utility calculus. That is, once a secure data environment—an environment that is more secure than usual with strict technical and procedural controls—is established, research within such a secure environment can be conducted with more room for flexibility than usual. 

Within a Personal Data Innovation Zone, for instance, data can be used for a long period of time (up to five years with a renewal possibility), data can be retrieved and reused rather than being disposed of after one-time use, and adequacy review of pseudonymization can be conducted using sampled data, instead of reviewing the entire dataset. So far, seven organizations, such as Statistics Korea and Korea National Cancer Center, have been designated as having satisfied the conditions for establishing secure data environments. 

(2) Regulatory sandboxes for personal data

Regulatory sandboxes for personal data permit time-limited experiments under specific conditions designed by regulators. Through this mechanism, approval may be granted to organizations that have implemented suitable safeguard measures. One example of this mechanism that has supported new technological developments is a case involving the use of unobfuscated original video data to develop algorithms for autonomous systems such as self-driving cars and delivery robots. Developing algorithms for self-driving cars and delivery robots would almost inevitably require permitting the use of unobfuscated data since, otherwise, it would be exceedingly cumbersome to obfuscate or otherwise de-identify personal data that can be found in all of the video data to be used. In the review process, certain conditions would be imposed in order to safeguard the data properly, often emphasizing strict access control and the management of data provenance. 

(3) Pseudonymized data and synthetic data: From encouragement to proceduralization

The PIPC has also moved from generic endorsement of privacy-enhancing technologies (PETs) to procedural guidance. Pseudonymized data and synthetic data are the clearest examples. A phased process was developed—preparation, generation, safety/utility testing, expert or committee assessment, and controlled utilization—with an emphasis on risk evaluation.

Some organizations, in particular certain research hospitals, established data review boards (DRBs), although doing so was not a statutory requirement. A DRB’s role would include, among others, evaluating the suitability of using pseudonymized data, assessing the identifiability of personal data from a dataset that is derived from multiple pseudonymized datasets, and assessing identifiability risks from synthetic data. 

4. Institutional design features that make the mechanisms credible

4.1 Building credibility and maintaining active channels of engagement

Compatibility is not achieved by guidance alone. Pro-innovation tools require institutional credibility. From the perspective of businesses, communicating with regulators can readily trigger anxiety. Businesses may worry that information they share could invite unwanted scrutiny. Given this anxiety, regulators need to be proactive and send out a consistent and coherent signal that information gathered through these mechanisms will not be used against the participating businesses. Maintaining sustained and reliable communication channels is critical. 

4.2 Expertise and professionalism as regulatory infrastructure

Case-by-case reviews, sandboxes, and risk models are only credible if the regulator has expertise in data engineering, AI system design, security, and privacy risk measurement—alongside legal and administrative capacity. To be effective, principle-based regulation requires sophisticated interpretive capability.

5. Implications: Why compatibility is plausible

Korea’s experience shows that the “innovation vs. privacy” framing is analytically under-specified. At an operational level, greater challenges tend to occur at the intersection of uncertainty, engagement, and institutional capacity. When legal and regulatory interpretations are vague and enforcement is unpredictable, innovators may perceive privacy as a barrier. When safeguards are demanded but not operationalized, privacy advocates may perceive innovation policy as de facto deregulation.

Korea’s mechanisms have attempted to resolve new challenges by translating principles into implementable controls, creating structured engagement and experimentation pathways. Privacy law does not inherently block innovation; poorly engineered compliance pathways do.

6. Conclusion

Korea’s experience supports a disciplined proposition: innovation and data privacy are compatible when compatibility is properly designed and executed. Compatibility does not come from declaring a balance; it comes from mechanisms that reduce uncertainty for innovators while increasing the credibility of the adopted safeguards for data subjects.

Korea’s toolkit—a principle-based approach combined with risk-based operationalization, structured risk management frameworks, active engagement channels, and credibility supported by professionalism and expertise—offers privacy professionals and policymakers a practical reference point for governance in the AI era.