Privacy Tech Can Make AI Safer: What the GAO Report Means for You

Earlier this month, the U.S. Government Accountability Office (GAO) published a report arguing that privacy-enhancing technologies are essential if we want to use artificial intelligence safely. The report, released on May 20, 2026, is aimed at federal agencies, but its findings have direct implications for anyone who uses AI tools—whether that’s ChatGPT, an AI photo editor, or a health recommendation app. Here’s what the GAO found, why it matters, and how you can protect your data right now.

What Happened

The GAO examined how well current AI systems protect personal information and concluded that existing privacy measures are often insufficient. Because many large AI models are trained on vast amounts of personal data—conversations, images, browsing habits—the risk of re-identifying individuals or leaking sensitive details is real. The report recommends that agencies adopt a set of privacy technologies to reduce those risks, including differential privacy, federated learning, and homomorphic encryption.

These aren’t brand-new inventions, but the GAO’s endorsement signals that they are becoming practical enough for broader use. The report also notes that while some technologies are already deployed by companies like Apple and Google, many government and commercial AI systems still lack basic privacy safeguards.

Why It Matters

For everyday consumers, the GAO report is a reminder that the AI tools you use may be collecting more data than you realize—and doing less to protect it. Without strong privacy tech, the convenience of AI comes with trade-offs: your personal information could be used to train future models, shared with third parties, or exposed in a breach.

The three technologies highlighted in the report each address a different part of the problem:

  • Differential privacy adds carefully calculated noise to data so that no individual’s information can be identified from the results. It’s used by Apple to collect usage statistics without seeing your specific activity, and by companies like Uber to analyze rider behavior without tracking individuals.
  • Federated learning keeps your data on your device. Instead of sending everything to a central server, the model learns from local data and only sends anonymized updates. Google uses this for Gboard’s predictive typing—your keystrokes never leave your phone in raw form.
  • Homomorphic encryption allows computation on encrypted data without decrypting it first. That means an AI could analyze your health records without ever having access to the actual numbers. The trade-off is speed: it’s still too slow for many real-time uses, but it’s improving.

These technologies are not silver bullets. Differential privacy requires tuning to avoid destroying accuracy, federated learning still needs careful aggregation, and homomorphic encryption is not yet practical for large-scale AI. But together they represent a shift toward building privacy into AI, rather than bolting it on after the fact.

What Readers Can Do

You don’t need to become a cryptographer to take advantage of these protections. Here are practical questions to ask before using any AI service:

  1. Does the tool explicitly mention privacy tech in its documentation? Look for terms like “differential privacy,” “federated learning,” or “on-device processing” in privacy policies or FAQs. Apple and Google often highlight these features for their AI services. Many consumer-facing AI products do not.

  2. What data does the tool collect, and where is it stored? If the service says it “may collect” anything you type, assume it’s storing your inputs on a server. Some tools offer local processing—for example, AI writing assistants that run entirely on your device.

  3. Can you opt out of having your data used for training? Some AI providers, like ChatGPT, now allow you to disable training on your conversations. Check the settings menu. If the option isn’t there, your data is likely fair game.

  4. Does the company have a track record of privacy incidents? A quick search for “company name data breach” or “company name privacy settlement” can tell you a lot.

  5. Is the tool open-source or externally audited? Open-source privacy technologies can be inspected by independent researchers. For closed-source tools, look for third-party audits or certifications like SOC 2.

Sources

  • GAO Report: Privacy Enhancing Technologies: Key Tools for Safer AI Adoption (May 20, 2026). Available at gao.gov.
  • MeriTalk coverage: “GAO: Privacy Tech Could Be Key to Safer AI Adoption” (May 20, 2026).
  • Apple’s Differential Privacy Overview, Google’s Federated Learning Research papers (publicly available).

The GAO report is a step forward in acknowledging that privacy and AI don’t have to be at odds. But the responsibility doesn’t end with agencies or companies. Consumers who understand these technologies—and who demand them—can influence how quickly safer AI becomes the norm.