App Privacy Label Change Intelligence

Turn app-store privacy metadata snapshots into app-level data-collection, tracking and disclosure drift intelligence.

Why use this Actor

Security, procurement and competitive teams need to detect when an app changes what data it claims to collect or use for tracking. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Snapshot comparison of privacy labels instead of review extraction.

• App-level tracking and data-category drift scoring.

• New disclosure and removed disclosure detection.

• Recurring vendor/privacy monitoring actions.

• Gen2 Dataset-input workflow for automation.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• Privacy teams.
• Mobile app intelligence.
• Vendor risk.
• Security research.
• AI agents.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Input

Provide currentItems as JSON records or point currentDatasetId to an Apify Dataset. For recurring comparisons, provide previousItems or previousDatasetId. Keep stable entity identifiers across snapshots whenever possible. maxItems limits Dataset reads. Optional Gen2 fields can compare the current decision metric with a prior analysis and estimate economic impact only when the user explicitly supplies an impact value.

Output

The default Dataset receives one structured intelligence report with record counts, new and removed records, ranked entity signals, decision confidence, regression status, executive decision, recommended action, and transparent economic-impact fields when enabled. The same report is stored in INTELLIGENCE_REPORT for downstream automations and agent workflows.

Compatible upstream datasets

This Actor analyzes normalized records rather than scraping restricted sources itself. Useful upstream families include:

• Apple App Store product metadata.
• normalized privacy-label datasets.
• mobile-app compliance datasets exposing tracking or sensitive-data disclosures.

The input aliases were expanded for these workflows. Verify field semantics when an upstream dataset uses source-specific names; snapshot identifiers should remain stable between periods.