App Privacy Label Change Monitor

Turn app-store privacy metadata snapshots into app-level data-collection, tracking and disclosure drift intelligence. Returns structured, machine-readable results for repeatable research, monitoring, automation, Apify Tasks, API workflows, and AI agents. Returns structured.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "appId": "com.acme.app",
      "appName": "Acme",
      "dataCategory": "Location",
      "trackingFlag": 1,
      "updatedAt": "2026-09-10"
    },
    {
      "appId": "com.acme.app",
      "appName": "Acme",
      "dataCategory": "Contacts",
      "trackingFlag": 1,
      "updatedAt": "2026-09-10"
    },
    {
      "appId": "com.beta.app",
      "appName": "Beta",
      "dataCategory": "Diagnostics",
      "trackingFlag": 0,
      "updatedAt": "2026-09-09"
    }
  ],
  "previousItems": [
    {
      "appId": "com.acme.app",
      "appName": "Acme",
      "dataCategory": "Location",
      "trackingFlag": 0,
      "updatedAt": "2026-08-01"
    },
    {
      "appId": "com.beta.app",
      "appName": "Beta",
      "dataCategory": "Diagnostics",
      "trackingFlag": 0,
      "updatedAt": "2026-08-01"
    }
  ],
  "maxItems": 20000
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current records: Current source or normalized rows to analyze.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
• maxItems — Maximum records: Maximum records loaded from a Dataset input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.012000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Detailed documentation

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.

Reliability and comparable evidence (26 October 2026)

Inputs must be arrays of objects. Current and previous records are deduplicated by explicit stable ID, or by full record when no stable ID exists. The output includes inputQuality counts, so repeated feed rows do not inflate confidence and directional signals. An absent previous snapshot produces BASELINE_REQUIRED without claims of change; an explicitly empty previousItems array is a valid comparison baseline. Invalid maxItems or oversized arrays are rejected; non-finite and boolean numeric values are treated as zero. Pricing and output schema for existing fields are preserved.