How Privacy Tech Can Make AI Safer for Everyone: A New Report Explains
A recent report from the U.S. Government Accountability Office (GAO) suggests that privacy-enhancing technologies (PETs) could be a key piece of making artificial intelligence safer for widespread use. The report, published in May 2026 and first covered by MeriTalk, arrives as concerns about how AI tools handle personal data have become more urgent. But what does this actually mean for someone who uses ChatGPT, Google Gemini, or any other AI service? This article cuts through the policy language and explains what these technologies are, why the government is paying attention, and what you can do to protect your data right now.
What Happened
The GAO report argues that privacy tech—specifically technologies like differential privacy, federated learning, and homomorphic encryption—could help address some of the biggest privacy risks associated with AI. The report was prepared as a response to growing public scrutiny of how AI systems collect, store, and use data. The GAO’s role is to provide nonpartisan analysis to Congress, so this is not a regulatory announcement but a signal that policymakers are looking seriously at technical solutions to AI privacy problems.
The report highlights that current AI systems often rely on massive datasets that may contain personal information. Even when companies claim to anonymize data, traditional methods can be reversed. PETs offer a way to train and run AI models without exposing raw personal data.
Why It Matters for Everyday Users
If you’ve ever wondered whether your queries to a chatbot or the photos you upload to an AI image generator are being stored or used to train future models, you’re not alone. Most major AI providers have privacy policies that allow them to use your data for model improvement, often with limited transparency about what exactly happens to it.
Privacy-enhancing technologies aim to change that. Here’s a quick, non-technical breakdown of the three PETs mentioned in the report and how they might protect you:
Differential privacy adds statistical noise to the data before it’s used. This means the AI can learn general patterns (e.g., “people born in the 1980s often ask about 90s music”) without being able to identify any specific individual. Apple and Google have used this for years in iOS analytics and Chrome’s usage statistics.
Federated learning keeps your data on your own device. Instead of sending your messages or photos to a central server, the AI model gets updated on your phone or laptop, and only the encrypted summary of that update is shared. Google’s Gboard keyboard uses this to improve typing prediction without sending your keystrokes to a server.
Homomorphic encryption allows computations to be performed on encrypted data without decrypting it first. The AI can process your private query while it remains scrambled, and only you can see the final result. It’s still computationally heavy, so it’s not widely deployed yet, but it’s promising for high-sensitivity use cases like medical AI.
The GAO report essentially says that if more AI companies adopt these technologies, consumers would face less risk of their personal data being exposed in a breach or used without consent. That matters because the stakes are high: AI systems often become more capable with more data, creating a direct tension between useful products and privacy.
What You Can Do Right Now
You don’t need to wait for companies to adopt new privacy tech. Here are practical steps you can take today to reduce your exposure:
Check the privacy settings of every AI tool you use. Most services let you opt out of having your conversations used for training. Look for terms like “improve the model” or “help train AI” and disable them if possible.
Ask the provider about their privacy technologies. Email support or check the technical documentation. Questions you can ask: “Does your AI use differential privacy or federated learning? How do you handle my data after I submit it?” If they won’t give a clear answer, that’s a red flag.
Limit sensitive information. Never share personally identifiable details (full name, address, health information) with an AI assistant unless you’re certain the service is end-to-end encrypted and doesn’t retain input. Assume chats are not private by default.
Use privacy-focused alternatives when available. Some AI tools are built specifically with privacy in mind. For example, Brave’s AI assistant runs locally, and Apple’s on-device AI processing, including upcoming features, uses differential privacy.
Stay informed about new policies. The GAO report signals that Congress may push for stronger privacy requirements in AI. Follow news from the Federal Trade Commission (FTC) and the National Institute of Standards and Technology (NIST) for updates. The consumer protection group Consumer Reports also publishes regular guides on AI privacy.
The Bigger Picture
The GAO report is not a silver bullet. Many of these privacy technologies have trade-offs: they can make AI slower, less accurate, or more expensive to run. And no single technique is foolproof. Differential privacy, for instance, only works well if the noise level is calibrated carefully. Too much noise and the AI becomes useless; too little and privacy leaks remain.
Still, the report is a useful signal that the U.S. government recognizes privacy as a core part of safe AI adoption—not an afterthought. For everyday users, the main takeaway is that you don’t have to choose between using AI and protecting your data. By asking tough questions and adjusting your settings, you can tip the balance back in your favor.
Sources
- MeriTalk article: “GAO: Privacy Tech Could Be Key to Safer AI Adoption” (May 20, 2026) — summarized via Google News.
- U.S. Government Accountability Office (GAO) – official report on privacy-enhancing technologies for AI (May 2026).
- Apple differential privacy overview: support.apple.com/en-us/HT204025.
- Google federated learning documentation: ai.googleblog.com/2017/04/federated-learning-collaborative.html.