How AI Is Inferring Your Secrets From Your Data—And What You Can Do About It

The grocery store knows you bought pregnancy test kits. The streaming service knows you’ve been watching documentaries about chronic illness. From those two facts alone, an AI system can infer with unsettling accuracy that you are pregnant and concerned about a health condition—something you never explicitly told anyone. This isn’t science fiction. It’s a rapidly maturing capability that privacy experts and regulators are starting to take seriously.

What’s Happening

Recent legal analysis by Dykema and coverage from Bloomberg Tax have highlighted a growing concern: AI-powered inference techniques allow companies to derive sensitive personal data from seemingly harmless information you provide—or that is collected passively about you. This “inferred data” can reveal your health status, political leanings, sexual orientation, income bracket, and more. Unlike data you knowingly share (like filling out a form), inferred data is created behind the scenes, often without your awareness or consent.

The Dykema article (July 2026) outlines how U.S. and EU privacy laws are struggling to keep up. Under the GDPR, inferred data may sometimes qualify as “personal data,” but its coverage is not always clear. The CCPA (California Consumer Privacy Act) historically allowed opt-out rights only for personal information that is “collected,” not necessarily for data generated through inference—though recent updates and enforcement actions have begun to challenge that gap. The Bloomberg Tax piece notes that the FTC has also started scrutinizing companies that use AI to infer sensitive attributes, especially when those inferences lead to harmful outcomes like discriminatory pricing or denial of services.

Why It Matters

The risks go beyond mere creepiness. Inferred data can be used to:

  • Discriminate: Insurers might infer a health condition and raise premiums; employers could infer political leanings and influence hiring.
  • Manipulate: Ads targeting inferred vulnerabilities—gambling urges, emotional states, financial stress—can exploit you without your knowledge.
  • Expose secrets: A breach of a dataset that includes inferred attributes can reveal information you never consented to share. Unlike an explicit self-report, you have no easy way to correct or delete a wrong inference.

The key problem is loss of control. You can choose to stop posting on social media, but you can’t easily stop companies from connecting the dots between your browsing habits, purchase history, location data, and device usage. AI models are becoming better at filling in the blanks, and the data you think is anonymous often isn’t.

What You Can Do

You cannot completely prevent inference—any available data can be analyzed. But you can make it harder for companies to draw accurate conclusions about you. Here are concrete steps:

  1. Audit app permissions regularly. Go through your phone’s settings and revoke access to location, contacts, and microphone for apps that don’t absolutely need them. Each permission feeds inference engines.

  2. Use privacy-focused browsers and extensions. Browsers like Firefox (with Enhanced Tracking Protection) or Brave, combined with extensions like uBlock Origin and Privacy Badger, block many tracking scripts that collect behavioral data for inference. Consider using a VPN to make it harder to link your IP address across sites, but remember—VPNs don’t stop all tracking.

  3. Limit social media sharing. Avoid posting details like your medical appointments, vacation plans, or even frequent purchases. The less you put out there, the fewer data points available for inference. Review past posts and delete old content that might be revealing.

  4. Opt out of data selling where possible. Under CCPA (if you live in California), you can submit opt-out requests to companies that sell personal information. For the rest of the U.S., services like the Digital Advertising Alliance’s opt-out page can help reduce behavioral targeting. These don’t eliminate inference, but they shrink the pool.

  5. Check your credit report and consider credit freezes. Credit scoring models use inferred data from payment histories, but also from utility bills and other sources. A freeze prevents new accounts from being opened without your permission, reducing the risk of inferences being combined with identity theft.

  6. Know your rights. Under GDPR, you have the right to request what data a company holds about you—including inferred data. Under CCPA, you can ask businesses to disclose the categories of personal information they have sold or shared. If you’re in the EU or California, exercise these rights to see what’s being inferred. The responses are often incomplete, but they can give you a sense of how companies view you.

  7. Use burner accounts and temporary addresses. For services that don’t need your real identity, consider using email aliases (like Apple’s Hide My Email or SimpleLogin) and virtual payment cards. This limits the ability to link your online activity to your real-world identity.

What Regulators Are Doing

The legal picture is evolving. The Dykema article notes that the GDPR’s Article 22 gives individuals the right not to be subject to decisions based solely on automated processing that produce legal effects—this could cover some inferences. The Bloomberg Tax analysis highlights that the FTC has recently warned companies about using AI to infer sensitive data without adequate safeguards, and several class-action lawsuits have been filed under state privacy laws. However, enforcement is still patchy. The law hasn’t caught up with the technology, so consumer vigilance remains essential.

Sources

  • “AI-Powered Inferred Data Poses New Threats for Consumer Privacy” – Dykema (July 2026)
  • “AI-Powered Inferred Data Poses New Threats for Consumer Privacy” – news.bloombergtax.com (July 2026)

Note: The above articles were accessed via Google News RSS. The legal landscape described reflects their analysis as of July 2026; readers should check for updates in their jurisdiction.