Streaming security analytics provider Abstract Security now partners with cloud security firm Netskope to enable joint customers to detect threats directly within live security data streams, reducing reliance on traditional log indexing workflows. The collaboration targets organizations operating cloud-first and hybrid environments that struggle with growing security data volumes, rising storage costs, and delayed threat detection.
The integration focuses on Netskope One telemetry generated across security service edge (SSE) environments. By ingesting high-fidelity Netskope data directly into Abstract Security’s streaming-first analytics pipeline, customers can analyze, enrich, and route security events before they are stored or indexed.
The approach is designed to eliminate delays inherent in legacy security information and event management pipelines, where detection typically occurs only after data has been ingested and indexed.
In-Stream Analytics for Cloud Security
Modern enterprise environments generate massive amounts of security telemetry from users, devices, and applications. In many cases, security teams are forced to choose between full visibility and manageable costs, as high-volume log ingestion can quickly overwhelm SIEM platforms and data lakes. According to Abstract Security, the joint solution addresses this challenge by shifting detection earlier in the data lifecycle.
Through the integration, Netskope log streaming data is processed in motion, allowing Abstract Security to identify anomalies, suspicious patterns, and potential threats as data flows through the pipeline. Contextual enrichment – such as identity attributes, geographic indicators, and threat intelligence – can be applied in real time, ensuring that downstream systems receive high-value, actionable events rather than raw telemetry.
The solution also enables dynamic routing of security data. Instead of forwarding all events to storage or analytics platforms, customers can selectively send only relevant signals to SIEMs, data lakes, or response systems. This model aims to reduce unnecessary ingestion and storage while preserving data sovereignty and forensic visibility.
Executives from both companies frame the partnership as a response to the limitations of index-first security architectures. Mike Anderson, vice president of business development at Abstract Security, said the collaboration is intended to deliver faster detections, fewer false positives, and measurable return on investment through reduced storage requirements and accelerated response times.
For security operations teams, the practical benefits include earlier detection of risks, lower operational overhead, and improved signal quality. Abstract Security estimates that organizations can reduce log ingestion and storage costs by up to 70% while maintaining detailed Netskope metadata needed for investigations and compliance reporting.
Adopting Streaming Without Rearchitecting Environments
The integration is designed as a lightweight deployment, developed in close coordination with Netskope, allowing customers to adopt the streaming model without rearchitecting existing environments. By consolidating inspection, enrichment, and analytics into a single adaptive layer, the companies argue that enterprises can simplify fragmented security stacks while scaling performance without introducing additional latency.
Abstract Security’s platform combines streaming data pipelines, analytics, and AI-assisted enrichment to inspect and correlate security events as they occur. Rather than forwarding all data downstream for later analysis, the system evaluates events in motion and forwards only those deemed relevant, aligning with broader industry efforts to reduce alert fatigue and improve detection confidence.
As organizations continue to adopt SSE and cloud-native security architectures, the partnership reflects a growing emphasis on real-time analytics and cost-efficient data handling. For enterprises grappling with security data growth, the move highlights an alternative approach to detection that prioritizes immediacy and precision over volume.
Executive Insights FAQ
What problem does the partnership address?
Delayed threat detection and high costs caused by indexing large volumes of security logs.
How does in-stream detection differ from traditional SIEM workflows?
Detection occurs as data flows, rather than after logs are ingested and indexed.
What type of data is analyzed?
High-fidelity Netskope One SSE telemetry, enriched with contextual security data.
What are the cost implications for customers?
Reduced log ingestion and storage costs, with estimates of up to 70% savings.
Who benefits most from this integration?
Organizations operating cloud-first or hybrid environments with high security data volumes.
