AI for Privacy Utilization - TLDR a privacy policy

I know there is some manner of distrust towards the efficacy of AI / LLMs, but honestly I’ve found a use case that is quite solid.

I am utilizing Proton’s Lumo for this, at which I simply ask the following question

You are an experienced legal consultant tasked with analyzing the privacy policy for anything that would flag for surveillance capitalism. Identify areas of risk associated with a degree of [high, medium, low] confidence towards the likelihood towards exploitation. Further, take note of how much this likely compares with other services’ policies with a degree of [close , somewhat , distant]. Communicate assumptions towards confidence levels with justified explanations. Do not assume maliciousness or benevolence, focus on facts. With this information, provide a table which summarizes the risk, the confidence band, the justification towards that risk, possible mitigations and remediations against this, and the similarity band to other services.
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At which I get a completely digested policy with possible mitigations towards said policy. I don’t actually assume high / medium / low is actually of that risk, but I mainly value the possible directions and ideas I can use to determine if its worth it to continue forward.

Example utilizing Tuta's Privacy Policy

Privacy Statement | Tuta

Risk Area Confidence Band Justification Possible Mitigations Similarity to Other Services
End-to-End Encryption Claims Low Document states user data encrypted such that Tutao GmbH has no access to unencrypted data. This fundamentally limits ability to monetize content. Verify technical implementation independently (security audits, source code review) Close to Proton Mail; distant from Gmail/Outlook/Yahoo which scan emails for ad targeting
Metadata Collection Medium Email addresses of senders/recipients, timestamps, and subject line data stored unencrypted. Necessary for service but creates communication graph metadata. Request metadata deletion after delivery; minimize metadata in communications Somewhat close to Proton Mail; closer to baseline industry minimum; distant from services collecting message content for targeting
Device-Level Random ID Tracking Medium Single random ID shared by all accounts on one device, transmitted with usage statistics. While anonymous, creates persistent device fingerprint enabling cross-account correlation within Tuta ecosystem. Disable usage statistics in settings; clear local ID periodically Somewhat distant from Facebook/Google device graphs (they link to real identities); close to other privacy-focused services using anonymous telemetry
IP Address Processing (Abuse Prevention) Low IPs stored anonymized for abuse prevention and fraud detection. No customer IPs in server logs per statement. Use VPN to mask IP; periodic account credential rotation Distant from ISPs/proxies that log full IPs indefinitely; close to standard hosting security practices
Campaign Attribution Tracking Medium Cryptographic hash of IP + user agent stored with campaign ID for 30 days. Allows marketing ROI measurement while preventing direct inference of IP. Use private browsing or privacy-focused referral links; disable tracking cookies Somewhat close to Google Ads conversion tracking; distant from traditional ad networks linking to personally identifiable information
Payment Data Collection High* Collects full banking details (IBAN/BIC, account holder), credit card data, PayPal username for billing. This creates financial records regardless of data sovereignty claims. Use cryptocurrency payments if available (Tuta supports crypto historically); third-party payment processors Close to any paid service requiring direct payment; distinguishable from services accepting cash/vouchers
Legal Disclosure Obligations High* States binding to provide content, traffic, and inventory data under valid court orders. Jurisdictional vulnerability given German/EU legal framework. Legal counsel consultation; use jurisdiction-divergent backup systems Close to all EU-based services; varies significantly from offshore jurisdictions claiming absolute confidentiality
Usage Statistics Voluntary Opt-In Low Requires explicit prior consent; user-controlled revocation; data anonymized with inability to draw conclusions about users. Simply opt-out in settings if desired Distant from Google Analytics/opt-out-required model; close to truly privacy-preserving analytics
No Third-Party Selling Clause Low Explicitly states There will be no sale of data. Strong signal against core surveillance capitalism revenue model. None needed beyond trust in commitment Distant from Meta/Google whose core business model relies on advertiser-accessible user data
Server Log Retention (7 Days) Low Limited 7-day retention for diagnostics and abuse. Short window reduces historical reconstruction capability. Periodic account activity monitoring; assume minimal forensic traceability exists Close to security best practices; shorter than many cloud providers retaining logs 30-90+ days

*High confidence here indicates HIGH CERTAINTY of the stated risk—not necessarily high severity relative to alternatives.

I have to counter-flag two issues:

  1. LLMs are not lawyers.
  2. Surveillance capitalism is structurally embedded within and outside the Internet, not cleanly isolated within privacy policies for your convenience.