Cape Privacy

Automatically identifies and masks sensitive data elements while maintaining statistical properties and relationships needed for analytics and machine learning. Uses AI to balance privacy protection with data usability for downstream analysis.

Replaces sensitive data values with tokens while preserving data format and structure for system compatibility. Enables encrypted data to flow through existing systems without modification.

Manages user consent for data collection and processing, tracks consent status, and ensures data handling aligns with granted permissions. Integrates consent requirements into data pipelines.

Generates realistic synthetic datasets that mimic the statistical properties of sensitive data without containing actual personal information. Enables safe testing and development without exposing real data.

Automatically enforces data retention and deletion policies by identifying data that has exceeded retention periods and triggering deletion or archival. Ensures compliance with retention requirements.

Enables analytics and reporting on sensitive data while protecting individual privacy through aggregation, noise injection, or other privacy-preserving techniques. Generates insights without exposing personal information.

Implements fine-grained access controls that restrict who can view or process sensitive data based on roles and purposes. Enforces principle of least privilege for data access.

Automatically enforces GDPR requirements including data minimization, right to erasure, and consent tracking directly within data processing workflows. Embeds compliance checks into pipeline execution.

Implements California Consumer Privacy Act requirements including consumer access rights, opt-out mechanisms, and sale-of-data restrictions within data systems. Automates compliance tracking and reporting.

Enforces HIPAA requirements for protected health information including access controls, audit logging, and encryption standards within healthcare data systems. Integrates compliance checks into data workflows.

Translates privacy policies into executable rules that automatically enforce data handling requirements across systems. Monitors data flows to ensure they comply with stated privacy commitments.

Tracks the origin, movement, and transformations of sensitive data through systems to provide complete visibility into data flows. Enables audit trails and impact analysis for privacy incidents.

Applies differential privacy techniques to datasets to provide mathematical guarantees of privacy while enabling statistical analysis. Adds controlled noise to protect individual records while preserving aggregate patterns.

Automatically identifies and classifies data elements by sensitivity level using AI, then applies appropriate protection tags. Enables consistent handling of sensitive data across the organization.

Automates the creation and execution of privacy impact assessments (PIAs) by analyzing data flows, processing activities, and risks. Generates assessment reports and identifies mitigation requirements.