New Report: These Privacy Tools Could Make AI Safer for Everyone

New Report: These Privacy Tools Could Make AI Safer for Everyone

If you’ve used a chatbot, image generator, or even a personalized shopping recommendation lately, you’ve probably wondered: What happens to my data? It’s a fair question. AI tools often need large amounts of information to learn and improve, but that doesn’t mean your private details have to be part of the deal.

A recent report from the U.S. Government Accountability Office (GAO) looked at exactly this tension. The finding? Several privacy technologies already exist that can help companies build smarter AI without collecting your raw data. Understanding what those technologies are—and which ones to look for—can help you make safer choices when using AI services.

What happened: The GAO’s take on privacy tech

The GAO, a nonpartisan agency that evaluates federal programs, issued a report on privacy-enhancing technologies (PETs) and their role in AI adoption. According to coverage from MeriTalk, the report highlights three specific approaches that are especially relevant for consumer-facing AI: differential privacy, federated learning, and on-device processing.

These aren’t futuristic concepts. They’re already used by companies like Apple, Google, and some newer AI startups. But many everyday users have no idea they exist or why they matter for their personal information.

Why it matters: What’s at stake when you use AI

When you use an AI tool, your prompts, photos, or browsing behavior can become part of a training dataset. Without privacy protections, that data could be exposed in a breach, used for secondary purposes, or even linked back to you. The GAO report explicitly warns that “adversaries can use AI to re-identify individuals from anonymous data,” which is a real risk if companies don’t put proper safeguards in place.

Here’s a plain‑English breakdown of the three technologies the GAO points to:

Differential privacy means the system adds a small amount of mathematical noise to your data before it’s used for training. This makes it nearly impossible to tell whether any specific person’s information was included, while still allowing the AI to learn general patterns. Apple uses it for keyboard predictions and emoji suggestions.

Federated learning lets the AI model train locally on your device. Only the updated model parameters—not your raw data—are sent back to the central server. Google uses something similar for Gboard’s predictive text. Your typing stays on your phone.

On-device processing means the AI runs entirely on your phone or computer. Nothing is sent to the cloud at all. Recent smartphones can now handle small language models locally for things like text summarization or photo editing.

Each approach has limitations. Differential privacy can reduce accuracy. Federated learning requires coordination. On-device AI may be less capable than a cloud model. But for many everyday tasks, these trade‑offs are worth it for the privacy gain.

What readers can do: How to protect your data today

You don’t need to be a cryptographer to demand better privacy. Here are practical steps you can take right now:

Check the privacy policy. Look for specific mentions of the three technologies above. Vague promises like “we care about your privacy” mean little. If a company doesn’t explain how it protects your data during AI training, treat it as a red flag.

Opt out of training where possible. Many AI services now allow you to disable the use of your data for improving their models. For example, OpenAI’s ChatGPT lets you turn off training in the settings (though this may limit some personalization). Do this for every AI tool you use regularly.

Prefer on-device features. If your phone offers AI capabilities (like smart photo albums or text prediction) that run locally, use those instead of cloud‑based alternatives. Apple’s latest on‑device models or Google’s Android Private Compute Core are good starting points.

Ask questions before signing up. When trying a new AI service, email customer support or check their documentation. Ask: “Do you use differential privacy? Is federated learning supported? Can I use the AI without sending my data to your servers?” If they can’t answer clearly, consider using a different tool.

Be skeptical of “free” AI. Many free tools monetize by collecting and selling data. A paid plan with clear privacy protections might be a better deal for your long‑term safety.

The GAO report essentially validates what many privacy researchers have been saying for years: the tools to make AI safer exist. The missing piece is consumer demand. By knowing what to look for and asking the right questions, you can push companies toward practices that respect your privacy without sacrificing innovation.

Sources

• MeriTalk, “GAO: Privacy Tech Could Be Key to Safer AI Adoption” (May 2026)
• U.S. Government Accountability Office, report on privacy‑enhancing technologies and AI (title and date to be confirmed from official GAO website)