The recent controversy surrounding DeepSeek, a Chinese AI company accused of training its models on unlicensed content, has reignited concerns about AI governance. Much of the discussion has focused on regulatory loopholes and the need for global oversight. However, a critical aspect has been overlooked: user consent and data sovereignty. The case underscores the urgent need to assess what data AI companies use, where it originates, and whether individuals or organizations consented to its use. 

This all starts with an AI inventory to assess the data leveraged, consent obtained, and the decisions made so you can properly evaluate the risk, place the proper contractual limitations, and transparently describe the application of AI. Leveraging DeepSeek or a vendor that utilizes DeepSeek does not necessarily contravene proper usage but can exacerbate the risks. This blog delves into the privacy implications of AI training, highlighting how issues of consent, data governance, and model operations should shape the future of AI policy and risk management.  

Understanding the Use Case: AI Training and Data Collection 

Large AI models, such as those developed by DeepSeek, require massive datasets for training. This data often includes text, images, and even proprietary content scraped from the internet. AI developers argue that this data helps improve model accuracy and functionality, but the process raises fundamental privacy questions: 

• Where does the data come from? Many AI models use publicly available datasets, but these often contain copyrighted or sensitive materials. 

• Who owns the data? Just because data is accessible online does not mean its use is legally or ethically permissible. 

• Did users consent to their data being used? AI training datasets often include user-generated content, but there is little transparency around whether individuals gave explicit permission. 

While regulatory discussions focus on AI governance at a broad level, the core issue is whether AI systems respect data sovereignty—the right of individuals and organizations to control their own data. 

The Privacy Risks AI Developers Overlook 

Beyond copyright concerns, the DeepSeek case illustrates deeper privacy risks that AI developers and regulators must address: 

1. Inadequate User Consent Mechanisms 

Most AI models are trained on vast, aggregated datasets where consent is often assumed but rarely confirmed. The implications include: 

• Lack of explicit user permissions: AI developers rely on “implied consent” when scraping online data, assuming public availability equals permission. 

• No opt-out mechanisms: Users are rarely given an option to exclude their data from training sets. 

• Retroactive consent concerns: Even when companies offer opt-out features after the fact, the damage is already done—the data has already influenced the model. 

2. Data Sovereignty and Cross-Border Data Transfers 

DeepSeek, as a China-based AI firm, likely sourced data from multiple jurisdictions, raising questions about data sovereignty—the legal right of a country or entity to govern data generated within its borders. Key concerns include: 

• Jurisdictional conflicts: If AI models trained on EU citizen data fail to comply with GDPR, who enforces accountability? 

• Lack of localization controls: Without strict regional governance, user data can be transferred and processed globally without oversight. 

• National security risks: Countries worry that AI firms accessing foreign data could create models that bypass national data protection laws. 

3. Model Risks and Unintended Data Leakage 

Even if AI companies attempt to anonymize or aggregate data, privacy risks persist in the form of data leakage—where personal information resurfaces within an AI model’s outputs. Risks include: 

• Memorization of sensitive data: AI models can inadvertently store and reproduce confidential details from training datasets. 

• Inference attacks: Bad actors can exploit AI responses to reconstruct original training data, compromising user privacy. 

• Regulatory blind spots: Current AI regulations focus more on output controls (e.g., preventing harmful content) rather than monitoring how data was obtained and whether it was used lawfully. 

A Call for Privacy-First AI Governance 

Addressing these privacy risks requires a shift from reactive AI regulation to proactive AI governance. Key recommendations include: 

Mandatory Data Provenance Tracking 

• AI firms should maintain detailed records of where their training data comes from. 

• Transparency reports should disclose which datasets were used, how they were acquired, and whether consent was obtained. 

Consent Mechanisms for Data Inclusion 

• AI models should incorporate opt-in systems where content creators explicitly agree to their data being used. 

• Platforms could implement metadata tags that signal whether data can be used for AI training. 

Stronger Data Localization Policies 

• Countries should enforce data residency laws requiring AI models to comply with regional privacy standards. 

• AI developers must allow users to specify geographic restrictions on their data usage. 

Stricter Model Testing for Privacy Risks 

• Before deployment, AI systems should undergo privacy impact assessments to detect memorization and unintended data retention. 

• Regulators should mandate independent audits to ensure AI companies comply with data protection laws. 

The Future of AI is Consent-Driven 

The DeepSeek controversy is a warning sign for the AI industry. While the debate has focused on intellectual property and regulatory gaps, the deeper issue is one of privacy, consent, and data sovereignty. AI models cannot continue to operate in a gray area where user data is absorbed without clear permissions. 

For AI to be ethical and sustainable, privacy-by-design principles must be embedded into model training and governance. This includes explicit consent mechanisms, stricter data governance rules, and improved transparency. Without these safeguards, AI will continue to erode digital privacy, undermining public trust and exposing companies to regulatory and reputational risks. 

The future of AI isn’t just about what models can do—it’s about whether they respect the fundamental rights of the people whose data fuels them.