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API Field Audit Service: Managed Field-Exposure Scanning & Remediation

A managed service that scans live production APIs (via traffic interception or log analysis) to detect which fields are actually being returned, flags unintended data exposure (PII, secrets, internal IDs), and generates remediation playbooks with code snippets for the team. Works with existing APIs—no library installation required. Runs weekly scans and alerts teams to drift (new fields added to responses without authorization).

SERVICE

11 weeks • 70% confidence

Value Proposition

Discovers unintended data leaks that developers don't know they're exposing (e.g., a new field added by a junior engineer that includes user IDs); provides proof of compliance for auditors; works with ANY existing API (no code changes required); catches drift automatically instead of relying on manual code review.

Target Audience

Enterprise and mid-market companies (100+ engineers) with legacy or distributed API ecosystems; compliance-heavy industries (fintech, healthcare, insurance) that need audit trails; teams with multiple backend teams that lack centralized API governance

Key Features

  • Passive API traffic analysis (integrates with reverse proxy, WAF, or API gateway logs)
  • Field-level exposure mapping (shows which fields are returned by which endpoints to which user roles)
  • PII & secret detection (flags SSN, credit card, API keys, internal IDs automatically)
  • And more, with full implementation detail...

Tech Stack

Python (log processing, PII detection) PostgreSQL (field mappings, audit logs) AWS Lambda or Kubernetes (scalable log ingestion) Presidio or spaCy (PII detection)
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Original Problem

Developers waste time manually specifying which fields to include in API responses instead of excluding unwanted ones

Backend developers building APIs must tediously list every single field they want to return when working with large data models, even when they only need to exclude 1-2 fields. This creates repetitive, error-prone code that becomes harder to maintain as data structures grow. Current API response libraries force developers to use the 'whitelist' approach (only/include) rather than the more intuitive 'blacklist' approach (exclude/except) when dealing with large datasets.

Score: 17.5%