Coralogix and Skyflow Redefine Privacy-Safe Observability for the
Coralogix and Skyflow Redefine Privacy-Safe Observability for the
Publish Date: 2026-03-20 16:06:00
Source Domain: natlawreview.com
SAN FRANCISCO & BOSTON–(BUSINESS WIRE)–Coralogix and Skyflow are launching a strategic partnership designed to help organizations safeguard sensitive customer data within logs. This collaboration ensures robust data protection without compromising the ability to perform searches, investigations, or leverage AI-driven operations.
Logs and telemetry play a critical role in debugging, incident response, security analysis, and AI workflows. However, they often contain sensitive customer data, embedded both in structured fields and unstructured text. While many observability tools mitigate this risk through redaction, this approach comes at a cost—eliminating exposure but also stripping away context. The result? Logs become more difficult to query, correlate, and operationalize effectively.
Coralogix and Skyflow take a fundamentally different approach: protect sensitive customer data by default while preserving the usability of observability data across humans and AI systems.
“The traditional approach of redaction creates a false trade-off between safety and usefulness,” said Anshu Sharma, CEO of Skyflow. “Once sensitive data is stripped out, teams lose the ability to search effectively, investigate incidents, or let AI agents reason over what actually happened. As a Runtime AI Data Control Platform, Skyflow ensures sensitive customer data stays governed and isolated, while observability data remains fully usable.”
Ariel Assaraf, Coralogix CEO, said: “Coralogix customers rely on observability data as a trusted system of record—supporting engineers, security teams, and the growing demands of AI-driven automation. They shouldn’t have to choose between safeguarding sensitive customer data and maintaining operational efficiency. By partnering with Skyflow, we ensure they can achieve both seamlessly.”
Why Traditional Approach Falls Short
In conventional observability pipelines, sensitive customer data is simply masked or completely removed…
