New GAO Report: How Privacy Tech Can Keep You Safe When Using AI

If you’ve used a chatbot, an AI image generator, or even your phone’s autocorrect, you’ve traded some of your data for that convenience. How much, and where it ends up, isn’t always clear. A new report from the U.S. Government Accountability Office (GAO) suggests that a set of privacy technologies could help tip the balance in favor of consumers—making it possible to use powerful AI without handing over the keys to your personal information.

What happened

The GAO, a federal agency that audits and advises Congress, released a report on privacy‑enhancing technologies (PETs) for artificial intelligence. The report examines several technical approaches that allow AI systems to learn from user data or to process that data without exposing the raw information itself. According to the report, these methods could be critical to “safe AI adoption,” especially as AI tools become embedded in everything from healthcare to personal assistants. The findings were covered by MeriTalk, a publication focused on government IT.

Among the PETs highlighted: differential privacy, federated learning, and homomorphic encryption. Each tackles the privacy problem from a different angle, but they share a common goal—giving you the benefit of AI without requiring companies to collect and store your actual conversations, photos, or keystrokes.

Why it matters

Most consumer AI tools today work by sending your input (a prompt, a photo, a voice recording) to a server, where a model processes it and returns a result. That server logs everything—often indefinitely. If that server is breached, or if the company later uses your data in ways you didn’t expect, you lose control over your own information.

Privacy technologies change that dynamic:

  • Differential privacy injects carefully calibrated “noise” into the data before it leaves your device or before it’s aggregated. This makes it nearly impossible for anyone to trace a specific answer back to you, while still allowing the AI to spot useful patterns across millions of users.

  • Federated learning trains an AI model across many devices without moving the training data to a central server. Your phone learns from your typing habits, but only the model updates—not the words themselves—are sent back to improve the next version of the model.

  • Homomorphic encryption lets a server perform calculations on encrypted data as if it were unencrypted. The server never sees your data in plaintext, yet it can still return a useful result. This is more computationally expensive, but it’s being explored for sensitive applications like medical diagnostics.

  • On‑device processing means the AI model runs entirely on your phone or laptop. Apple’s recent on‑device models for Siri suggestions and Google’s Private Compute Core are examples. No data leaves your device at all.

The practical benefit for you: less of your personal information sits on company servers. That reduces the risk of data breaches, lowers the chance of your data being sold or repurposed, and gives you more control over what happens to your digital footprint.

What you can do

You don’t need to become a privacy engineer to take advantage of these protections. Here are a few simple steps:

  1. Look for “differential privacy” or “federated learning” in privacy policies. Some companies—like Apple, Google, and Microsoft—mention these techniques in their privacy documentation. If they do, it’s a sign they’ve invested in limiting data exposure. If the policy is silent on how training data is handled, that’s a red flag.

  2. Prefer tools that run AI on your device. For tasks like writing suggestions, photo organization, or voice dictation, check if the model works offline or on‑device. On‑device AI is increasingly common in newer smartphones and laptops.

  3. Ask questions before using a new AI service. Simple ones: “Where is my data processed?” “Is it stored?” “Can it be deleted?” A service that gives clear, straightforward answers is more likely to be trustworthy than one that buries the details.

  4. Limit what you share voluntarily. Even with privacy tech in place, avoid feeding sensitive personal information into AI tools you don’t fully trust. Privacy tech reduces risk, but it doesn’t eliminate it entirely.

  5. Support stronger privacy practices. As a consumer, your choices matter. Companies pay attention when users demand better data protection. If enough people choose tools that use PETs, more providers will adopt them.

Sources

  • GAO report on privacy‑enhancing technologies for AI (May 2026), as covered by MeriTalk and other outlets.
  • Apple Privacy documentation on differential privacy and on‑device processing.
  • Google’s Private Compute Core and federated learning overview.
  • General overview of homomorphic encryption from the National Institute of Standards and Technology (NIST).

The GAO’s report doesn’t claim that any single technology is a silver bullet. Privacy tech has limits—differential privacy can make models slightly less accurate, and homomorphic encryption remains slower than unencrypted processing. But the report makes a strong case that these tools are no longer just theoretical. They’re ready for broader adoption, and they could be the difference between an AI future that respects your privacy and one that treats your data as a cheap resource.