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.
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.”
In conventional observability pipelines, sensitive customer data is simply masked or completely removed breaking functionality:
- Identifiers no longer match across events
- Search and correlation degrade
- AI tools lose critical context
- Teams introduce risky exceptions to get work done
Instead of permanently removing sensitive values, Skyflow replaces them with consistent, privacy-preserving tokens, allowing logs to remain searchable and analyzable while the underlying data is centrally controlled, access-governed, and auditable.
Coralogix already enables customers to deploy observability workloads in specific geographic regions to meet data residency requirements. By combining this with Skyflow’s runtime data control capabilities, organizations can continue to meet strict data sovereignty obligations—ensuring sensitive customer data is governed, isolated, and accessed only under policy, while observability data remains local, usable, and compliant across regions. This approach helps organizations operating in regulated or multi-region environments reduce cross-border data exposure while maintaining full visibility and operational effectiveness.
The joint approach enables organizations to:
- Keep sensitive customer data out of logs, dashboards, and downstream tools
- Preserve search, filtering, and correlation across events
- Enable AI agents to operate safely on telemetry, without direct access to raw sensitive data
- Allow policy-based rehydration only for approved workflows
- Reduce data sprawl and strengthen compliance across the observability stack
The result is observability that is privacy-safe by design, operationally effective, and ready for AI-native workflows.
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