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Showing posts with label legal challenges. Show all posts

More U.S. Investors Join Legal Dispute With South Korea Over Coupang Data Breach

 



A fresh wave of U.S.-based investment firms has joined an ongoing legal confrontation with the government of South Korea over its handling of a large scale cybersecurity incident involving Coupang.

On February 11, it was confirmed that three additional investors, Abrams Capital, Durable Capital Partners, and Foxhaven Asset Management, have formally moved to participate in arbitration proceedings. These firms are aligning with Greenoaks Capital and Altimeter Capital, which had already initiated legal action. By filing official notices, the new claimants are adopting and supporting the earlier case rather than launching a separate one.

At the center of the dispute is an allegation that South Korean authorities unfairly targeted Coupang and, by extension, other U.S.-linked businesses operating in the country. The investors claim that Seoul’s regulatory response following a large-scale consumer data breach amounted to discriminatory treatment that caused severe financial harm.

The controversy traces back to a disclosure made in November, when Coupang announced that personal information belonging to roughly 33 million customers in South Korea had been exposed in a cyber incident. Data breaches of this scale typically involve unauthorized access to customer records, which may include names, contact information, and other identifying details. The announcement triggered widespread public concern, political scrutiny, legal complaints, and cross-border tensions.

According to the investors pursuing arbitration, the government’s actions after the breach significantly affected shareholder value, resulting in losses amounting to billions of dollars. They argue that the regulatory measures taken were disproportionate and damaged investor confidence.

In addition to arbitration efforts, the newly joined investors have sent letters supporting calls for a formal review by U.S. authorities into South Korea’s conduct. Neil Mehta, founder and managing partner of Greenoaks Capital, stated that American policymakers and investors increasingly view the case as an example of the need to defend U.S. companies against what they see as unfair foreign government actions.

Coupang was established in 2010 by Korean-American entrepreneur Bom Kim, a graduate of Harvard University. Over the past decade, it has become the most widely used e-commerce platform in South Korea, surpassing long-established domestic conglomerates such as Shinsegae in online retail presence. The company has expanded beyond traditional online shopping into food delivery services, streaming platforms, and financial technology offerings, further strengthening its footprint in the country’s digital economy.

South Korea’s Justice Ministry has confirmed receipt of additional notices signaling intent to arbitrate. In an official statement, the ministry said it would respond in a systematic and professional manner through its International Investment Dispute Response Team, indicating that the government intends to formally defend its position.

The issue has also contributed to rising trade friction between Washington and Seoul. U.S. President Donald Trump has warned that tariffs on South Korean goods could increase to as much as 25 percent amid broader economic tensions.

Separately, the United States House Committee on the Judiciary recently issued a subpoena to Coupang as part of an ongoing investigation examining alleged discriminatory treatment of American companies operating abroad.

As arbitration proceedings advance, the case is expected to test not only corporate accountability in the wake of major data breaches, but also the strength of international investment protections and the diplomatic balance between two long-standing economic partners.

What are the Legal Implications and Risks of Generative AI?


In the ever-evolving AI landscape, dealing with the changing regulations and securing data privacy has become a new challenge. With more efficient human capabilities, AI must not replace humans, especially in a world where its standards are still developing globally. 

There are certain risks that the unchecked generative AI possesses with the overabundant information it may hold. Companies run the risk of disclosing their valuable assets when they feed private, sensitive data into open AI models. Some businesses choose to localize AI models on their systems and train them using their confidential data in order to reduce this danger. However, for best outcomes, such a strategy necessitates a well-organized data architecture.

Risks of Unchecked Generative AI

The appealing elements of generative AI and Large Language Models (LLMs) are their capabilities to compile information to produce fresh ideas, but these skills also carry inherent risks. If not carefully handled, gen AI can unintentionally result in issues like: 

Personal Data Security 

AI systems must handle personal data with the utmost care, especially sensitive or special category personal data. Concerns about unintentional data leaks that could lead to data privacy violations are raised by the growing integration of marketing and consumer data into LLMs.

Contractual Violations 

It is occasionally illegal to use consumer data in AI systems, which has negative legal repercussions. As companies adopt AI, they must carefully negotiate this treacherous terrain to ensure they uphold contractual commitments.

Customer Transparency and Disclosure 

The goals of current and potential future AI regulations focus on a transparent and lucid disclosure of AI technology. For instance, the business must disclose whether a person or an AI is handling a customer's engagement with a chatbot on a support website. Maintaining trust and upholding ethical standards depend on adherence to such restrictions.

Legal Challenges and Risks for Businesses 

Recent legal actions against eminent AI companies highlight the significance of handling data responsibly. The importance of strict data governance and transparency is highlighted by these lawsuits, which include class action cases involving copyright infringement, consumer protection, and data protection issues. They also suggest possible conditions for exposing the origins of AI training data.

Since their use of copyrighted data to build and train their models, AI giants have been the main targets of various lawsuits. Allegations of copyright infringement, consumer protection violations, and data protection legislation violations are made in recent class action lawsuits filed in the Northern District of California, including one filed on behalf of authors and another on behalf of victim users. These submissions emphasize the value of treating data responsibly and could indicate that in the future it will be necessary to identify the sources of training data.

Moreover, businesses possess serious risks when they significantly rely on AI models, not just AI developers like OpenAI. The case of how many of the apps implement improper AI model training may taint entire products. The parent business Everalbum was forced to destroy improperly gathered data and AI models after the Federal Trade Commission (FTC) accused Everalbum of misleading consumers about the use of face recognition technology and data retention. This forced Everalbum to cease in 2020.

How to Mitigate AI Risks? 

Despite the legal challenges, CEOs are under pressure to adopt generative AI if they wish to increase their business’ productivity. Businesses can create best practices and get ready for new requirements by using the frameworks and legislation currently in place. AI systems are covered by provisions in existing data protection regulations, such as those requiring transparency, notice, and the protection of individual privacy rights. Some of these best practices involve:

  • Transparency and Documentation: Businesses are recommended to clearly mention the AI usage, and document AI logic, applications and potential impacts on the data subjects. Also, businesses must keep a record of data transactions and detailed logs of confidential information in order to maintain proper governance and data security.
  • Localizing AI Models: By ensuring that models are trained on pertinent, organization-specific information, internal localization and training with private data can lower data security risks and boost efficiency.
  • Discovering and Connecting: Companies must utilize generative AI to unveil new perspectives and create unexpected connections across different departments and information silos.
  • Preserving Human Element: Gen AI should improve human performance rather than completely replace it. To reduce model biases and data inaccuracies, human monitoring, critical decision review, and content verification of AI-created information are essential.