Google Play’s policy package released this month brings third-party AI integrations explicitly under the User Data policy, and closes off READ_CALL_LOG as a workaround some apps were using for account verification. For Android teams, the practical effect is the same shift Apple’s App Store already forced on iOS apps last year: it’s no longer enough to have an AI feature and a generic privacy policy link. Every SDK or API call that sends user data to a third-party AI model or service — a support chatbot, an AI-driven personalisation engine, an analytics pipeline feeding an insights model — is now a named, disclosed data flow that Play Console review can check against what the app actually does at runtime.
Most Android apps built their AI features incrementally, adding an LLM-backed chatbot here, an AI recommendation call there, often through whichever vendor a given team picked without a central register of who receives what. That’s exactly the gap Play’s policy update targets, and it’s also the gap that trips teams up during review — not because they’re hiding anything, but because nobody instrumented which SDK calls actually leave the device with user data attached, so the Data Safety form and the real behaviour have quietly drifted apart. Without tracking those calls directly, “our AI vendor list is accurate” is an assumption, not a fact.
Data Points to Track
- Every outbound call to a third-party AI or LLM endpoint, logged with vendor name, data fields included in the payload, and the feature that triggered it
READ_CALL_LOGand adjacent sensitive permission usage, flagged per app version, since Play now expects this specific verification workaround to be gone entirely- Data Safety form declarations versus runtime SDK behaviour, reconciled on every release rather than only when the form is first filled in
- Consent state at the time of each AI-bound data call, so a call made without valid consent is visible immediately rather than discovered in review
- Vendor-level data volume, so a growing or newly added AI integration surfaces before it becomes a policy-compliance surprise
Setup Steps
- Inventory every SDK and API integration that touches an AI or LLM service today, including ones added quietly for a single feature, not just the headline AI functionality.
- Remove or replace any remaining
READ_CALL_LOG-based verification flow before the policy’s enforcement window closes, since this is the specific mechanism Play named. - Instrument outbound calls to AI vendors at the network or SDK layer, capturing vendor and payload shape, so the actual data flow is measurable rather than assumed.
- Reconcile the Data Safety form against that instrumentation on every release, not just at initial submission, since AI integrations change faster than most teams update disclosure forms.
- Gate AI-bound data calls on current consent state, and track rejections so a broken consent check surfaces as a metric rather than a review rejection.
Actionable Insights
Any AI vendor call showing up in the runtime instrumentation that isn’t already named in the Data Safety form is the highest-priority fix on this list — it’s the exact mismatch Play’s review process is now built to catch, and finding it internally first is far cheaper than a policy rejection on submission. A READ_CALL_LOG permission request appearing in a build after this policy update is a hard signal that an old verification path is still live somewhere and needs removing, not patching around. And a rising trend in AI-bound data calls made without valid consent recorded is worth escalating immediately, since it points to a consent-gating bug rather than a policy paperwork gap.
Related Resources
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