The developer of DePIN network, Verida, has raised $5 million to address the growing concerns over user data privacy in the age of Big Tech AI. The seed funding round included participation from O-DE Capital Partners, ChaiTech Ventures, Simurg Labs, as well as Gate Labs, HASH CIB, Bison Capital, Amesten Capital and Mysten Labs’ Evan Cheng. The $5 million was also raised through community pre-sales of the Verida Storage Credit token.

The capital will fund the development of alternative data storage solutions geared toward internet users who want to prevent their data from being used to train AI. Verida’s latest round of funding brings its valuation to $50 million. The freshly raised capital will develop Verida's personal data ownership infrastructure. The network aims to offer decentralized storage and encryption for users’ personal information, with data being kept among numerous nodes managed by different network participants, the company said Friday in a statement.

As the AI market grows, so too have concerns about language-learning models’ use of internet users’ data. The AI market's growth has also led to a rise in generative AI tools that can pose risks to data privacy in several ways. These tools often require access to data, which may include personal or sensitive information and may be in different forms. If not properly protected, it can pose risks to individuals' privacy and could lead to unauthorized access, identity theft, or misuse of personal information.

Verida is poised to disrupt the AI landscape by putting users back in control of their data, unlocking more secure, privacy-preserving personalized AI for everyone. The company's latest round of funding brings its valuation to $50 million. The freshly raised capital will develop Verida's personal data ownership infrastructure. The network aims to offer decentralized storage and encryption for users’ personal information, with data being kept among numerous nodes managed by different network participants.

Generative AI tools can pose risks to data privacy in several ways. These tools often require access to data, which may include personal or sensitive information and may be in different forms. If not properly protected, it can pose risks to individuals' privacy and could lead to unauthorized access, identity theft, or misuse of personal information. As generative AI tools often require access to data, which may include personal or sensitive information and may be in different forms, if not properly protected, it can pose risks to individuals' privacy and could lead to unauthorized access, identity theft, or misuse of personal information.

Therefore, protecting personal or sensitive data is crucial to maintain user trust, complying with privacy regulations, and ensuring ethical AI practices. Using AI tools requires organizations to familiarize themselves with relevant data protection regulations and to ensure that their AI systems comply with them. Compliance with these laws helps protect individuals' privacy rights and mitigates the risks associated with the processing of data by AI. Two very significant data protection regulations that have implications for AI tools are the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

In general, it is evident that the intersection of generative AI and data privacy presents both opportunities and challenges. However, implementing the right strategies and measures will help organizations effectively manage and mitigate the risks while maintaining the benefits of generative AI tools.