Tool Overview:
Divebell

About divebell

Divebell is a data protection and privacy compliance platform that processes data within the customer's environment. The platform combines data discovery, risk intelligence, compliance management, and security actions into a unified solution. Built on a SaaS architecture, Divebell can be deployed in under an hour without requiring vendor security approvals.

What does divebell do?

The platform's core functionality centers on automated data scanning and classification across diverse storage locations including databases, warehouses, S3 buckets, and document stores. Using contextual content detection and machine learning techniques, Divebell creates a dynamic data map showing where sensitive information resides and who has access to it. This continuous scanning process maintains an up-to-date inventory of data assets without burdening existing infrastructure.

For compliance management, Divebell implements a policy rule engine that automatically flags risks and enforces compliance requirements. The platform generates risk scores based on data sensitivity, quantity, access patterns, and customizable business context. These risks are displayed in a heatmap interface that tracks changes over time and provides clear remediation actions to data owners.

A key component is the Subject Tracing technology, which identifies and links together multiple attributes of individual subjects across different data sources. This capability supports Data Subject Access Request (DSR) fulfillment by automating searches across the entire data landscape. The platform can process DSRs end-to-end, from request intake to final response, with customizable workflows that adapt to specific business processes and compliance requirements.

What makes divebell different?

Unlike traditional privacy tools that focus primarily on workflow management through surveys and task assignment, Divebell takes a discovery-first approach by scanning and understanding the actual data landscape. This method aims to eliminate the uncertainty and incompleteness often associated with manual data inventories and privacy assessments.

The platform's architecture processes all data privately within the customer's environment, avoiding the need for external data transfers that could complicate security approvals. This design choice, combined with its lightweight deployment model, enables rapid implementation and frequent data refreshes - as often as hourly - to maintain current risk visibility.

Use cases and industries

Divebell serves various sectors requiring robust data protection and privacy compliance. The platform addresses specific challenges in cloud migration scenarios, helping organizations maintain data security and privacy during transitions to cloud-based systems. Its automated risk remediation capabilities include implementing dynamic masking policies, column and row-level access controls, and quarantine measures.

The platform supports compliance with multiple privacy regulations including CPRA, PIPL, and GDPR. It maintains data retention strategies by tracking data history and subject attributes, enabling organizations to identify and act on data that requires deletion. The system also monitors week-over-week changes in data presence and can notify administrators when new personal data elements are collected.

For security operations, Divebell can trigger encryption or de-identification solutions and notify security operations centers for targeted remediation. The platform integrates with existing security infrastructure through connector architecture and open APIs, allowing organizations to coordinate data protection across their technology stack while maintaining compliance with evolving regulatory requirements.