How Privacy Tech Can Make AI Safer for You (GAO Report Insights)

The news cycle around artificial intelligence is relentless, but a recent report from the Government Accountability Office (GAO) offers a quieter, more practical take: the building blocks for safer AI might already exist, and they center on privacy technology. This isn’t about government regulation alone—it’s about the tools that can protect your data when you use an AI chatbot, a recommendation engine, or any smart app. Here’s what the report says and why it matters for everyday internet users.

What Happened: The GAO Report on Privacy Tech

On May 20, 2026, MeriTalk reported on a new GAO report titled “Privacy Tech Could Be Key to Safer AI Adoption” (the article is available via Google News). The GAO, a nonpartisan watchdog, examined how federal agencies and private developers can deploy AI in ways that minimize data exposure. The report highlights several privacy-enhancing technologies (PETs) that allow AI systems to train and operate without collecting raw personal information.

Among the key technologies named are differential privacy, federated learning, and homomorphic encryption. These sound technical, but they share a simple goal: let AI learn from your data without actually seeing it.

  • Differential privacy adds statistical noise to data so that no individual’s information can be singled out, even if an attacker gains access to the output.
  • Federated learning trains an AI model across many devices (like your phone or laptop) without ever sending your raw data to a central server. Only encrypted model updates leave your device.
  • Homomorphic encryption allows computations to be performed on encrypted data, meaning the AI never sees the plaintext version of your inputs.

The report suggests these technologies are already viable and could become standard in consumer AI products, reducing both privacy risks and the temptation for companies to hoard personal data.

Why It Matters for You

Most people interact with AI services—ChatGPT, Google Assistant, Netflix recommendations, and countless mobile apps—without knowing how their data is handled. Typically, that data flows to a company’s cloud, where it can be stored, analyzed, or even sold. Even with anonymization, researchers have repeatedly shown that supposedly de-identified data can be re-identified.

If privacy tech becomes the norm, that dynamic changes. You could use a chatbot that learns to answer your questions better while your conversation remains encrypted on your phone. A health app could predict your risk of a condition without sending your personal medical history to a server. The GAO report underscores that these technologies aren’t just for experts; they can be built into consumer products transparently.

For now, the reality is mixed. Some major tech companies already use differential privacy (Apple, Google) or federated learning (Google’s Gboard keyboard, Apple’s Siri improvements). But many AI startups and smaller apps rely on centralised data collection because it’s easier to implement. The GAO’s interest signals that policymakers see privacy tech as a necessary foundation for wider AI adoption.

What Readers Can Do

You don’t need to become a cryptography expert to benefit. Here are practical steps to choose AI tools that respect your privacy:

  1. Look for explicit privacy tech mentions – When evaluating an AI app, check its privacy policy or website for terms like “on-device processing,” “differential privacy,” “federated learning,” or “encrypted computation.” If you see these, it’s a good sign the company has invested in protecting your data.

  2. Prefer tools that process data locally – For tasks like photo editing, note-taking, or even some voice assistants, options that run entirely on your device (without an internet connection) inherently avoid sending your data elsewhere. Examples include Apple’s on-device speech recognition or local LLM apps like Ollama.

  3. Be wary of “free” AI services – If an AI tool offers a generous free tier, it’s worth asking how it makes money. Often, that model involves monetising user data. Paid services with clear privacy commitments may be safer.

  4. Use privacy-focused browsers or extensions – When interacting with web-based AI, tools like Brave Browser or privacy-focused search engines (DuckDuckGo) can limit tracking. Some even offer AI features with built-in protections.

  5. Stay informed about updates – The GAO report is a starting point, but privacy tech evolves quickly. Keep an eye on reliable tech news (like MeriTalk, Ars Technica, or consumer watchdog reports) to know which services adopt these technologies.

Sources

  • “GAO: Privacy Tech Could Be Key to Safer AI Adoption,” MeriTalk, May 20, 2026. (Google News)
  • “The Wrap: CISA Leak Sparks Scrutiny; Anthropic v Pentagon; Safer AI Starts Here,” LinkedIn, May 20, 2026.
  • “The AI EO: How Its Ambitious Goals Can Be Realized,” MeriTalk, January 30, 2024.

Note: The GAO report itself has not yet been published in full as of this writing; the above is based on the news coverage and quoted findings.