Tenable One Powers AI-Driven Cyber Risk Decisions with the Release of the Open Connector

Tenable One Open Connector was announced by Tenable Holdings, Inc. as a new capability for its Tenable One Exposure Management Platform, aiming to bring more data into exposure management. Tenable said the change is intended to improve prioritization and to enable more remediation actions.

The release linked the connector to a problem of fragmented visibility created by rigid integration sets. It said the connector differs from closed approaches by allowing data from any source to be brought in and operationalized within Tenable One.

The company described Tenable One as providing a unified view of risk across the attack surface by combining third-party tools, internal systems, and native telemetry. It also said the Open Connector ingested data from AI models, internal systems, unsupported tools, and common file formats, then turned that data into prioritized insights and automated remediation. It added that synchronization kept exposure data current, with optional manual uploads, and that customers could customize data mapping and structuring for analysis, reporting, and remediation use cases.

Tenable also announced the Tenable Open Partner Ecosystem Network (OPEN). Eric Doerr said, “Closed platforms dictate what data customers can use. We believe customers should decide,” and added, “Tenable Open Connector extends our ability to bring in even more types of security and business context into the Tenable One exposure graph. This data powers Tenable Hexa AI, our agentic engine to deliver sharper prioritization, more accurate insights and faster, more effective remediation.” The release further said the OPEN network was intended to bring together disparate security tools into a more unified and proactive defense.

Forward-looking statements in the release covered expected capabilities of OPEN, anticipated functionality and performance of the Open Connector and bi-directional integrations within Tenable One, and risks that could cause results to differ materially from those expectations.