If you look at how most security teams are forced to operate today, it is clear that our biggest bottleneck isn’t a lack of telemetry, but rather a severe structural disconnect between infrastructure execution and data reality. We have spent years buying brilliant, specialized tools to monitor different layers of our stack, yet we’ve accidentally forced security engineers to act as manual human middleware. On one side, you have Cloud-Native Application Protection Platforms (CNAPPs) like Upwind tracking live, running workloads at the kernel level; on the other, you have Data Detection and Response (DDR) platforms like Matters.AI mapping out the semantic value, lineage, and movement of sensitive files. Because these two systems rarely talk to one another, answering a simple question during an active incident feels less like engineering and more like a high-stakes guessing game.
To bridge this operational gap, we are launching a native integration that effectively connects Upwind’s real-time CNAPP runtime context with Matters.AI’s advanced DDR capabilities. Instead of treating workload anomalies and data exfiltration as two parallel tracks, this partnership blends them into a single, cohesive control plane across your entire cloud landscape.
When Runtime Telemetry Meets Dynamic Data Lineage
Matters.AI brings data context into the runtime security equation. Its DDR engine continuously discovers, classifies, and maps sensitive data across cloud environments, giving security teams a clear understanding of where critical data resides and how it moves.
The integration with Upwind extends that intelligence into the execution layer. Upwind’s CNAPP provides runtime telemetry, identity, network, and attack-path context, enabling Matters.AI to correlate the sensitivity of a data store with the infrastructure conditions that could expose it.
This correlation closes a critical gap between data risk and infrastructure risk. Rather than treating vulnerabilities and misconfigurations as isolated findings, security teams can understand which exposures intersect with sensitive data, trace the relevant attack paths, and prioritize remediation based on the potential impact to critical data assets.
The result is a more contextual approach to data security by identifying not just where a vulnerability exists, but which sensitive data is exposed through it and which risks warrant action first.
Redefining Threat Prioritization and Incident Response
When you inject real-time runtime visibility into a data detection and response framework, the immediate byproduct is an incredibly sharp reduction in operational noise and a far more sophisticated approach to triage. This unified architecture fundamentally changes day-to-day operations across several key areas:
- Exploit-Aware Data Prioritization: Instead of treating all exposed workloads equally, Upwind’s CNAPP telemetry allows you to instantly verify if an internet-facing container has active permissions to production databases containing proprietary source code or critical customer IP mapped by Matters.AI. Detections are automatically prioritized based on live execution status and the real-world value of the connected asset.
- A Drastic Reduction in False Positives: Most security leads we talk to are exhausted by the endless parade of high-severity infrastructure alerts that turn out to be completely harmless because the underlying application isn’t even executing or interacting with sensitive data. By combining runtime tracking with DDR data valuation, you can finally clear away the theoretical clutter and focus engineering cycles exclusively on validated, data-targeted risks.
- Accelerated Incident Triage and MTTR: If an unauthorized network call is detected on a live workload, the integrated platform maps the entire attack path from the initial application threat vector straight to the target data store. Rather than forcing a SecOps analyst to cross-reference multiple dashboards while the clock is ticking, the system delivers a single, high-fidelity timeline that shows exactly which workload needs to be isolated or which block of code requires an emergency patch.
- Automated Posture Convergence: This synchronization works beautifully in reverse; if Matters.AI’s DDR engine discovers anomalous data movement or a newly spun-up, unencrypted datastore, Upwind immediately references its live runtime map to identify every single workload currently interacting with it, keeping compliance, security, and engineering teams perfectly aligned without any manual intervention.
Frictionless Implementation
We knew that for this partnership to be genuinely valuable, the onboarding experience had to be entirely seamless for enterprise teams. Upwind customers can now generate API credentials directly within their existing console and connect to Matters.AI using a pre-built connector.
Within a matter of minutes, your live runtime telemetry will begin mapping alongside deep data intelligence, completely enriched with execution states, resource ownership, and verified data asset values, allowing you to drive maximum security ROI without disrupting developer workflows.