South Korea establishes AI privacy framework with new guidelines

The Personal Information Protection Commission of South Korea unveiled draft guidelines on August 2025 addressing personal data processing for generative artificial intelligence development and services. According to the commission, these guidelines aim to clarify legal uncertainties while enhancing privacy protection for individuals in the context of AI training using publicly available data.

The announcement represents South Korea's first comprehensive framework for handling personal information in AI development contexts. This regulatory guidance arrives as global authorities grapple with balancing AI innovation against privacy protection concerns. The commission explicitly addresses the challenges facing AI developers who require massive datasets for model training while navigating complex personal data protection requirements.

Technical framework establishes legitimate interests standard

The guidelines establish the concept of legitimate interests as the primary legal basis for processing publicly available personal data in AI training contexts. According to the commission's framework, AI developers must demonstrate three core requirements: legitimacy of purpose, necessity of data processing, and assessment of associated interests between data processors and data subjects.

The legitimate interests provision requires AI developers to specify intended purposes for model development, such as large language models supporting medical diagnosis, credit rating systems, or text generation and translation capabilities. The framework explicitly excludes irrelevant data collection, citing the example of excluding individual income and property information when developing medical diagnostic AI systems.

Technical safeguards outlined in the guidelines include examining sources of training datasets, implementing measures to prevent personal data breaches through erasure and de-identification, secure storage and management protocols, and applying prompt and output filtering functions. The commission also addresses machine unlearning techniques for removing targeted training data points from models.

Administrative requirements target organizational compliance

The guidelines establish comprehensive administrative safeguards for AI business operators. Organizations must establish criteria for collecting and using training datasets while incorporating these standards into privacy policies. The framework recommends conducting Privacy Impact Assessments and operating dedicated AI Privacy Red Teams to monitor potential vulnerabilities.

Implementation requirements allow flexible adoption of detailed safeguards given rapid AI technological advancement. The commission clarifies that businesses need not implement every stipulated safeguard but should select optimal combinations considering intended functions, potential side effects including performance degradation and bias, and AI technology maturity levels.

Data subject rights protections receive significant attention in the framework. The guidelines require incorporating publicly available data collection status and primary sources into privacy policies. Organizations must uphold data subjects' rights including measures for exercising deletion and suspension rights for data breaches during AI training and deployment phases.

Industry consultation shapes practical implementation

The commission developed these guidelines through extensive stakeholder engagement. After announcing its "Policy Direction for Safe Usage of Personal Data in the Age of AI" in August 2023, the commission conducted discussions with the Public-Private Policy Advisory Council for AI Privacy, comprising 30 AI experts organized into three subcommittees.

Professor Byoung Pil Kim of KAIST, who served as head of the Advisory Council's Subcommittee on Criteria for Data Processing, stated "it is part of our endeavors to meet halfway between protecting personal data and encouraging AI-driven innovation. This will be a great guidance material for the development and usage of trustworthy AI."

The guidelines reflect consultation with academia, industry, and civil society stakeholders. Head of LG AI Research Kyunghoon Bae, co-chairperson of the Advisory Council, emphasized that "the guideline provides a lawful basis to safely process personal data from publicly available data to mitigate legal uncertainties in developing AI technologies."

Global coordination influences regulatory approach

The commission explicitly focuses on establishing internationally interoperable standards, recognizing current global trends toward balancing AI innovation with safety considerations. Major countries including the United States and European Union member states are establishing privacy-related norms and standards for processing publicly available data in AI applications.

The framework aligns with global regulatory approaches while maintaining Korea-specific implementation requirements. The commission conducted preliminary inspections of AI services in March 2024, sharing results with major Large Language Model service providers to help businesses identify optimal compliance combinations.

Chairperson Haksoo Ko emphasized the regulatory gap these guidelines address: "Clarification is not sufficient enough as to how to ensure legality and safety in using publicly available data for AI model training, even though AI technology is advancing at an exponential rate."

Implementation timeline establishes compliance roadmap

The draft guidelines enter immediate effect while the commission plans periodic updates reflecting technological advancement and evolving legal frameworks. The authority committed to materializing lawful basis and criteria for processing users' personal data through continued stakeholder consultation across academia, industry, and civil society.

The commission announced plans to communicate with AI-powered businesses through innovation support schemes including the Prior Adequacy Review Scheme, Regulatory Sandbox, and Personal Information Safety Zone. These mechanisms will monitor technological advancement and market conditions while accumulating best practices for future regulatory updates.

Related enforcement activities demonstrate practical application of the framework. The commission conducted preliminary inspections of major AI services, issuing recommendations for improvement regarding disclosure of personal information handling, user input data transparency, and information subject rights implementation.

Technical specifications address development lifecycle

The guidelines provide detailed technical specifications covering the entire AI development lifecycle. Data preprocessing requirements include source verification, implementing measures to prevent personal information exposure, secure storage protocols, and applying privacy-enhancing technologies during training phases.

Model-level protections encompass fine-tuning procedures to add safeguards, alignment techniques to ensure appropriate responses, and implementing filtering functions for both input prompts and output generation. System-level controls include access management for AI systems and comprehensive input/output filtering mechanisms.

The framework addresses emerging AI applications including retrieval-augmented generation systems and AI agents requiring external knowledge base access. Organizations implementing these technologies must apply additional safeguards to prevent personal information leakage through external database integration.

International precedent influences Korean approach

The guidelines reference similar regulatory developments in major jurisdictions. The European Union, United States, and other significant markets are establishing comparable frameworks for AI data processing, with data protection authorities actively developing privacy-related norms and standards.

This regulatory coordination reflects recognition that AI development increasingly operates across international boundaries. Korean authorities explicitly designed their framework to maintain compatibility with global standards while addressing specific domestic requirements for AI innovation and privacy protection.

The commission's approach emphasizes practical implementation guidance rather than restrictive prohibitions. This methodology enables AI developers to pursue innovation while maintaining robust privacy protections, establishing Korea as a significant player in global AI governance discussions.

Timeline

• August 2023: PIPC announces "Policy Direction for Safe Usage of Personal Data in the Age of AI"
• March 2024: Commission conducts preliminary inspections of major AI services
• August 2025: PIPC unveils "Guideline on Processing Publicly Available Data for AI Development and Services"
• May 2024: German authorities issue first AI privacy guidelines
• December 2024: European Data Protection Board clarifies AI privacy rules
• December 2024: Google updates AI prohibited use policies
• July 2025: European Commission releases comprehensive AI model guidelines

Key Terms Explained

Personal Information Protection Commission (PIPC): South Korea's primary data protection authority responsible for enforcing privacy regulations and developing guidance on emerging technologies. The commission serves as the central regulatory body overseeing compliance with personal information protection laws, conducting investigations, and issuing recommendations to organizations processing personal data. PIPC's role has expanded significantly with the rise of artificial intelligence technologies, requiring specialized expertise in both privacy law and emerging technological applications.

Legitimate Interests: A legal basis under data protection frameworks that allows processing of personal information without explicit consent when organizations demonstrate compelling justifications that outweigh individual privacy concerns. This concept requires a three-part assessment examining the legitimacy of processing purposes, necessity of data collection, and balancing of interests between data controllers and subjects. In AI contexts, legitimate interests enable developers to use publicly available data for model training while implementing appropriate safeguards to protect individual rights.

AI Development: The comprehensive process of creating artificial intelligence systems encompassing data collection, model training, algorithm refinement, and deployment phases. This process involves multiple technical stages including data preprocessing, feature engineering, model architecture design, training procedures, validation testing, and continuous improvement cycles. AI development in privacy-regulated environments requires careful consideration of data sources, processing methodologies, and safeguards to ensure compliance with personal information protection requirements.

Data Processing: The systematic handling of information through various operations including collection, storage, organization, analysis, modification, retrieval, transmission, and deletion activities. In artificial intelligence contexts, data processing encompasses the entire lifecycle from initial data acquisition through model training, inference generation, and eventual data destruction. Privacy regulations impose specific requirements on data processing activities, mandating lawful bases, purpose limitations, data minimization principles, and individual rights protections.

Technical Safeguards: Protective measures implemented through technological solutions to prevent unauthorized access, data breaches, and privacy violations during information processing activities. These safeguards include encryption protocols, access control mechanisms, data anonymization techniques, secure storage systems, and monitoring capabilities designed to detect potential security incidents. In AI systems, technical safeguards also encompass model-specific protections such as differential privacy, federated learning approaches, and output filtering mechanisms to prevent sensitive information exposure.

Privacy Protection: The comprehensive framework of legal, technical, and organizational measures designed to safeguard individual privacy rights and prevent unauthorized disclosure of personal information. This concept encompasses both proactive measures implemented during system design phases and reactive responses to privacy incidents or rights requests. Privacy protection in AI contexts requires specialized approaches addressing unique challenges such as data memorization in machine learning models, inference attacks, and the difficulty of implementing traditional privacy rights in trained systems.

Training Data: The foundational information used to teach artificial intelligence models how to perform specific tasks, recognize patterns, and generate appropriate responses to various inputs. Training data quality, diversity, and volume significantly impact model performance, with modern AI systems often requiring massive datasets containing billions of data points. Privacy considerations in training data management include ensuring lawful collection, implementing appropriate anonymization techniques, and establishing procedures for handling requests to remove individual information from trained models.

Administrative Safeguards: Organizational policies, procedures, and governance frameworks designed to ensure proper handling of personal information and compliance with privacy regulations. These safeguards include staff training programs, incident response procedures, privacy impact assessment processes, data retention policies, and regular compliance auditing activities. In AI development contexts, administrative safeguards also encompass specialized roles such as AI Privacy Officers, ethics review boards, and cross-functional teams responsible for evaluating privacy implications throughout the development lifecycle.

Data Subject Rights: The fundamental entitlements granted to individuals regarding their personal information, including rights to access, rectify, delete, restrict processing, data portability, and object to certain processing activities. These rights represent core privacy principles ensuring individual control over personal information and imposing corresponding obligations on organizations processing such data. In AI systems, implementing data subject rights presents unique technical challenges, particularly regarding deletion requests for information embedded in trained models, requiring innovative approaches such as machine unlearning techniques.

International Standards: The globally recognized frameworks, principles, and best practices for privacy protection and AI governance developed through collaborative efforts among multiple jurisdictions. These standards facilitate cross-border data flows, enable consistent regulatory approaches, and provide guidance for organizations operating in multiple markets. International standards in AI privacy include frameworks from organizations such as the International Organization for Standardization, guidelines from regional bodies like the European Data Protection Board, and bilateral agreements establishing adequacy determinations for international data transfers.

Summary

Who: The Personal Information Protection Commission (PIPC) of South Korea, in collaboration with the Public-Private Policy Advisory Council for AI Privacy comprising 30 experts, including Professor Byoung Pil Kim of KAIST and LG AI Research head Kyunghoon Bae.

What: Comprehensive guidelines establishing legal standards for processing publicly available personal data in AI development, introducing legitimate interests framework, technical safeguards, administrative requirements, and data subject rights protections.

When: Draft guidelines announced August 2025, following policy direction established August 2023 and stakeholder consultations throughout 2024, with periodic updates planned based on technological advancement.

Where: South Korea, with explicit focus on establishing internationally interoperable standards compatible with European Union GDPR, United States frameworks, and other major jurisdictions' AI governance approaches.

Why: To resolve legal uncertainties in AI development while balancing innovation with privacy protection, addressing the critical gap between rapid AI technological advancement and existing personal data protection frameworks in the context of massive dataset requirements for model training.