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Ret on Privacy: Practical Advice from an Alum

AI Threats to Privacy

By Gene Ret (September 2026)

 
The rapid rise of artificial intelligence has transformed how personal data is collected, analyzed and exploited, turning modern privacy into a critical vulnerability. As AI algorithms quietly process vast amounts of digital footprints, individuals face unprecedented risks from automated surveillance, identity theft and predictive profiling.
 
The same technology driving these risks, however, also offers the tools to counter them. Protecting personal data requires a multi-layered approach that combines proactive digital hygiene, cutting-edge privacy software and robust legal frameworks to strip control back from invasive algorithms.
 
AI threatens consumer privacy by vacuuming up massive datasets without your explicit consent, using predictive algorithms to profile users, and exposing sensitive inputs to hackers or unintended third parties. Because AI tools continuously log and learn from your prompts, any personal information shared can be ingested into a system and potentially leaked. 
 
How AI Threatens Consumer Privacy
 
  • Non-Consensual Data Harvesting: AI developers often "scrape" publicly available online data, including personal blogs, social media and images, to train foundational models. 

 

  1. Data Leakage and Exposure: Public chatbots and AI-integrated apps copy your inputs into system logs and model outputs, which can result in sensitive records being exposed during data breaches or accidentally served to other users.

 

  • Invasive Profiling: AI systems can link disparate pieces of data to infer highly private traits about you—such as your health status, location patterns, or financial habits—even if you never explicitly stated them. 

 

  • Automated Cyberattacks: Hackers use AI to automate spear-phishing and craft highly convincing impersonation scams tailored to the personal details you leave online. 

How to Prevent and Protect Against AI Threats

 

  • Disable AI Training: Take control of your data by actively turning off "chat history" or opting out of data collection in the settings of AI platforms (e.g., OpenAI Data Controls or Google Privacy Center). 

 

  • Never Share Sensitive Inputs: Treat AI chatbots like public forums. Never input personal identifying details (like your Social Security Number), passwords or financial information into public AI tools. 

 

  • Review App Permissions: Be mindful of device permissions. If you use AI-driven apps (such as meeting transcribers or virtual assistants), restrict their access to your microphone, camera and contacts.

 

  • Upgrade to Paid/Enterprise Plans: As highlighted by cybersecurity discussions on ⁠Reddit r/cybersecurity, free-tier AI tools are highly likely to use your chat data to train their models, whereas paid enterprise tiers often provide strict data isolation.

 

  • Adopt Digital Hygiene: Safeguard your AI accounts using strong passphrases and Multi-Factor Authentication (MFA) to prevent hackers from accessing your past prompts or AI workspaces. 

How Organizations Must Respond

  • Privacy by design: Build data protection into the foundational architecture of AI systems rather than adding it later.
  • Data minimization: Collect and process only the exact data required for a specific AI task.
  • Anonymization and synthetic data: Remove personal identifiers or use artificial datasets to train models safely.
  • Strict access controls: Limit internal and third-party access to consumer data pipelines.

Regulatory and Legal Measures

  • Enforce compliance: Apply frameworks like the EU AI Act and regional privacy laws to mandate algorithmic transparency and impact assessments.
  • User rights: Guarantee consumers the right to access, correct or delete personal data utilized by automated systems.

 

 
Other columns by Gene Ret:
 
 
 
 
    About Gene Ret
 
Eugene (Gene) Ret is a 30-year veteran of Chase Bank and has been in financial services for 45+ years. He has been a Privacy Professional for 23 of these years and was a Senior Privacy Compliance Officer for Chase, as well as HSBC.
 
Ret was one of the first to develop and employ “best in class” privacy protocols in the buildout of the Privacy Office function and participated in the early development of bankers' recommendations to federal regulators regarding effective and appropriate Privacy standards. Ret has been a presenter at bank trade associations’ symposiums on various Privacy matters and has been a frequent contributor to online Privacy forums and discussions on new and trending topics. 
 
Ret is currently an independent consultant in General Banking Compliance, specializing in Privacy. He is a certified Privacy Professional  – CIPP-US –  a certification by the International Association of Privacy Professionals.    
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