See how major platforms rate on privacy. Click any company to see details, or analyze your own policy below.
Analyze a Privacy Policy
Paste the full text of any privacy policy or terms of service below. PrivacyPeep will score it across six dimensions and surface what matters.
Compare Two Policies
Paste two different privacy policies side-by-side to see which one better protects your privacy.
What PrivacyPeep Does
Privacy policies are long, dense, and deliberately opaque. Most people click "I Agree" without reading them — and companies know this.
PrivacyPeep automatically reads a policy for you and provides automated analysis across six dimensions: how much data is collected, how it's shared, how long it's retained, what rights you have, how transparent the language is, and what security commitments are made.
The result is a plain-language scorecard with heuristic findings and pattern-based insights that help you understand what you're really agreeing to before you click Accept.
How Scoring Works
Each policy is analyzed against a library of pattern-matched rules derived from real privacy law obligations (GDPR, CCPA, HIPAA, BIPA) and consumer-rights best practices. Findings are weighted by severity:
- Critical — High-risk clauses like biometric data collection, indefinite data retention, or selling your data without opt-out. Each deducts 20 points from the affected category.
- Warning — Concerning but lower-risk patterns like cross-device tracking, vague data retention language, or third-party data enrichment. Each deducts 10 points.
- Info — Notable practices worth being aware of. Each deducts 5 points.
- Positive — Explicit privacy protections, data minimization commitments, strong user rights. Each adds up to 5 bonus points (capped at +15 per category).
The overall score is a weighted average across six categories:
Indicator Scale
Scores are converted to letter indicators to make them easier to compare at a glance. These are automated assessments, not legal determinations:
Strong protections
Above average
Acceptable, gaps exist
Below average, concerning
Serious risks — read carefully
Readability Analysis
PrivacyPeep also measures how readable the policy itself is using the Flesch-Kincaid formula. A policy written at a 16th-grade (graduate) reading level is effectively inaccessible to most adults — which is often intentional.
The readability score is separate from the privacy score. A perfectly written policy with terrible privacy practices still gets a bad privacy score.
Methodology
This tool uses rule-based pattern detection derived from publicly available privacy frameworks (e.g., GDPR, CCPA, HIPAA, BIPA). It does not perform legal interpretation or guarantee compliance accuracy.
The pattern library consists of approximately 100 rules that match specific language patterns commonly found in privacy policies. Each pattern is assigned a severity level (critical, warning, info, or positive) and a category. The overall score is a weighted average of six category scores, each computed by deducting points for negative findings and adding limited bonus points for positive findings.
Results represent automated, heuristic findings — not legal conclusions. The analysis is only as good as the patterns in the library and the text provided.
Limitations
PrivacyPeep is a pattern-matching tool, not a lawyer. It:
- Cannot interpret legal nuance or jurisdiction-specific edge cases
- May miss issues phrased in unusual ways not yet in the pattern library
- Does not verify whether a company actually follows its stated policy
- Should be used as a starting point for informed decision-making, not as legal advice
Built by Cast Net Technology
PrivacyPeep is a product of Cast Net Technology — Governed intelligence, not guesswork.
We build operator-grade AI tools for compliance, healthcare, and regulatory operations. PrivacyPeep demonstrates our commitment to transparent, explainable, locally-executed intelligence.
Policy text is processed locally in your browser. We do not store submitted policy content on our servers. No tracking. No telemetry.