The Right of Personal Action: A Game-Changer for Data Privacy in the Age of AI
There is an intersection of artificial intelligence (AI) and data privacy which will force the hand of our politicians. AI continue to drive into various sectors, from smart assistants to predictive analytics, and the implications for data privacy are profound. A recent article by Laleh Ispahani in The Hill↗ emphasized the need for robust data privacy regulations, especially in the context of AI, when there are clear mistakes being made in things like policing and government tracking of things like facial recognition. While regulations and government oversight are essential, there's also a pressing need for a right of personal action, similar to what is afforded under the Americans with Disabilities Act.↗ This would empower individuals to take legal action against entities that misuse their data, ensuring accountability and deterring potential violations.
The Double-Edged Sword of AI
AI offers a plethora of benefits, from enhancing business efficiency to predicting cyber threats. However, as highlighted by Kate O'Flaherty in Information Age↗, AI poses significant risks to privacy. Deep learning models can de-anonymize data, potentially infringing on every person's privacy. Common mistakes by organizations include using data for unintended purposes, storing data longer than necessary, and gathering irrelevant information. Such oversights could lead to violations of regulations like the GDPR.
The Need for Personal Action
The right of personal action would serve as a significant deterrent against data misuse. Without such rights, companies might merely pay lip service to regulations. The real change will manifest when there's a tangible threat of repercussions for non-compliance.
AI's Role in Data Privacy
AI isn't just a potential threat to privacy; it can also be a solution. AI can be employed as a privacy-enhancing technology (PET), helping organizations adhere to data protection by design obligations. For instance, AI can generate synthetic data, mirroring the patterns of personal data without compromising individual privacy. Furthermore, AI can bolster privacy by encrypting personal data, reducing human errors, and identifying potential cybersecurity threats.
The Current Regulatory Landscape
While AI is currently governed by regulations like the GDPR, more stringent laws are on the horizon. The EU, known for its robust AI-related privacy protections, is planning to introduce more AI-specific regulations. The UK is also gearing up to unveil its stance on AI regulation by the end of 2023.
Protecting Personal Information
Given the evolving landscape, it's crucial to safeguard personal information. As reported by The Washington Post↗, everything we do online is tracked, making data privacy even more critical. From using encrypted messaging apps to being cautious about sharing medical information, individuals must take proactive steps to protect their data. Conclusion The conversation around AI and data privacy is multifaceted. While AI holds the promise of revolutionizing industries, it also brings forth challenges in data privacy. The right of personal action could be the catalyst for ensuring that companies prioritize data privacy, not just in letter but in spirit. As the discourse around data privacy evolves, it's imperative to consider the perspectives of all stakeholders to establish a robust and effective framework.
How AI-Powered Data Collection Affects Your eCommerce Store
Your eCommerce platform relies on AI to personalize shopping experiences—recommendation engines, predictive analytics, and behavioral targeting all run on customer data. But here's the tension: the more data you collect to fuel these AI systems, the more privacy risk you create.
When you use tools like Google Analytics 4, Meta Pixel, or Shopify's native analytics, you're feeding AI models with detailed customer behavior. That data trains algorithms that predict what customers want to buy next. The problem is that many eCommerce brands don't clearly disclose to customers that their browsing history, purchase patterns, and even abandoned cart behavior are being analyzed by AI systems.
Your brand needs to be explicit about this. If you're using AI to segment customers, send personalized emails via Klaviyo, or retarget ads on Facebook, your privacy policy should explain that AI processes customer data—not just that you collect it. Many privacy policies still read as if humans are the only ones reading customer information.
Additionally, the data you retain for AI training purposes may need to be kept longer than strictly necessary for the transaction itself. This conflicts with the data minimization principle in privacy regulations. You need a clear retention schedule that balances business needs (AI model accuracy) with privacy obligations (not keeping data indefinitely).
What "Right of Personal Action" Means for Your Brand's Liability
The concept of a private right of action—letting individuals sue companies directly for privacy violations—could reshape how eCommerce brands approach compliance. Rather than waiting for regulators to investigate and fine you, customers could bring their own lawsuits if your AI systems misuse their data.
This matters because it shifts accountability from "Did the government catch us?" to "Could a customer sue us and win?" For mid-market brands, this is a serious operational risk. You might face class action lawsuits from customers claiming their data was used for unauthorized AI training, sold to third parties without consent, or processed in ways that weren't disclosed.
To reduce this exposure, audit what your AI vendors actually do with customer data. If you're using a third-party recommendation engine or marketing automation platform, verify their data handling practices. Don't assume consent language in your cookie banner covers AI processing—courts increasingly scrutinize whether consent was truly informed. Your banner needs to specifically explain AI use cases in plain language, not buried in legal prose.
Building Privacy Into AI Systems at the Product Level
Privacy-by-design isn't just compliance theater—it's a product strategy that reduces your legal and operational risk. Many eCommerce brands bolt on privacy after building their tech stack, then struggle to retrofit compliance.
Instead, consider privacy constraints when you select or build AI tools. For example, if you're using machine learning to detect fraudulent orders, ask the vendor: Does the model retain customer data after making predictions? Can customers request that their data be removed from the training dataset? Does the system create inferences that go beyond fraud detection?
Synthetic data and federated learning are emerging techniques that let you improve AI models without centralizing sensitive customer information. While these are still cutting-edge for mid-market eCommerce, understanding them helps you ask smarter questions of vendors.
Your brand should also implement automated data deletion workflows. If a customer requests deletion via a Data Subject Access Request (DSAR), your AI systems need to be able to remove their data from active models, not just from your database. This is technically complex but increasingly expected by regulators and savvy customers.
The intersection of AI and eCommerce means your brand is collecting and processing customer data at scale. The regulatory and liability landscape is shifting faster than most compliance teams can move. Effective consent management—capturing explicit, informed consent for AI use cases and maintaining audit trails—is becoming essential infrastructure, not optional overhead.