App stores
AuraScore 81/100

Consumer Mobile App Privacy Label and SDK Audit Checklist

Audit retail mobile application SDKs and tracking flows for App Store Privacy Nutrition Labels and Data Safety sections.

Use this prompt to audit third-party analytics, ad tech, and CRM SDKs embedded within retail mobile applications. It produces an exhaustive data disclosure checklist to ensure compliance with Apple ATT and Google Play Data Safety requirements.

Template

Role: Mobile Security and Privacy Compliance Engineer specializing in consumer data governance and app store privacy declarations.

Context

  • Retail application: {{app_name}}
  • Ecosystem targets: {{target_store_ecosystems}}
  • Integrated analytics & adtech SDKs: {{third_party_sdks}}
  • Consumer data collected: {{data_collection_types}}
  • Account deletion & data management mechanisms: {{account_deletion_flow}}
  • Loyalty and behavioral profiling tier: {{loyalty_program_tier}}

Task

Generate an exhaustive mobile privacy audit checklist to accurately map data flows from {{third_party_sdks}} and {{loyalty_program_tier}} into Apple Privacy Nutrition Labels and Google Play Data Safety declarations for {{app_name}} across {{target_store_ecosystems}}.

Method

  1. Inventory all data types collected in {{data_collection_types}} (e.g., precise location, purchase history, device identifiers).
  2. Trace data access, transmission, and retention across each SDK listed in {{third_party_sdks}}.
  3. Map data collection purposes (App Functionality, Analytics, Developer Advertising, Personalization) against platform-specific disclosure taxonomies.
  4. Audit App Tracking Transparency (ATT) implementation and prompt timing relative to user onboarding.
  5. Verify that {{account_deletion_flow}} satisfies store mandates for direct, in-app account and data deletion.
  6. Evaluate encryption in transit and ephemeral data handling for checkout and payment instrumentation.
  7. Assemble an actionable pre-submission audit checklist with data mapping verifications for each store platform.

Constraints

  • Checkpoints MUST explicitly differentiate between Apple App Store Privacy Details and Google Play Data Safety section requirements.
  • The checklist MUST require affirmative proof of in-app account deletion compliance.
  • You MUST NOT approve third-party SDKs that collect device fingerprinting signals without user consent.
  • The audit MUST address user data sharing with third-party advertising brokers.

Output format

  • Section 1: SDK Data Interception & Purpose Mapping Matrix (Markdown table)
  • Section 2: App Store Privacy Nutrition Label Checklist (Apple iOS specific, structured with checkboxes [ ], Data Category, Purpose, and Linked to Identity status)
  • Section 3: Google Play Data Safety Declaration Checklist (Google Play specific, structured with checkboxes [ ], Data Type, Shared vs. Collected, and Security Practices)
  • Section 4: Account Deletion and Consent Flow Verification Protocol (5-8 strict pass/fail criteria)

Self-review

  • Are all 6 variables ({{app_name}}, {{target_store_ecosystems}}, {{third_party_sdks}}, {{data_collection_types}}, {{account_deletion_flow}}, {{loyalty_program_tier}}) fully utilized?
  • Does the checklist distinctly cover both Apple Privacy Nutrition and Google Play Data Safety requirements?
  • Is the account deletion requirement evaluated in strict compliance with current store guidelines?
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

developers
developers-app-stores
retail-consumer-goods
app-stores
privacy-compliance
retail-security