SentinelOne Labs Advocates for Agentic SOCs to Overcome Data Scalability Challenges
SentinelOne Labs proposes an 'agentic SOC' model, leveraging AI-driven data pipelines to normalize disparate telemetry, thereby enhancing defensive velocity and addressing the administrative burden on security analysts.
Security Operations Centers (SOCs) are grappling with significant scalability issues, stemming from both structural complexities and the cognitive load placed upon analysts. The daily reality for many SOC analysts involves a daunting queue of unvetted alerts, each requiring manual investigation. This process often necessitates pivoting between multiple consoles, manually enriching data like IP addresses and file hashes, and cross-referencing potentially outdated asset inventories. Only after these time-consuming aggregation steps can the actual threat assessment and decision-making begin.
The core problem, as highlighted by SentinelOne Labs, is the "upstream data problem." Threat actors operate at machine speed, automating lateral movement and exfiltration, often outpacing human defenders. Expecting analysts to counter these rapid attacks by manually sifting through fragmented and inconsistent telemetry data from diverse sources—including firewalls, endpoint sensors, and cloud workloads—is an "architectural failure." This raw telemetry arrives in various formats and "dialects," requiring extensive manual normalization before any meaningful analysis or AI processing can occur.
SentinelOne argues that this manual data synthesis represents a critical misallocation of highly skilled human intelligence. The asymmetry in modern cybersecurity is not just about speed but about how finite analyst time is spent. When the majority of an analyst's shift is dedicated to wrangling data rather than actively investigating threats, the SOC's foundational architecture is inadequate. Achieving true "defensive velocity" requires addressing this data foundation as a mandatory prerequisite for improving all downstream security functions.
To combat this, SentinelOne advocates for advanced data pipelines, such as its Singularity AI Data Pipelines. These pipelines are designed to ingest telemetry from any source and in any format, normalizing it into standardized structures aligned with frameworks like the Open Cybersecurity Schema Framework (OCSF). This process transforms raw, fragmented logs into immediately actionable, structured data, eliminating the need for analysts to write complex queries or regular expressions simply to reconcile data formats from different vendors.
Beyond normalization, efficient data ingestion involves dynamic optimization. Storing all generated logs in expensive, highly indexed tiers is often unsustainable. Modern data pipelines can filter out noise, trim excess volume, and route logs based on their analytical value. High-value security events are indexed for rapid retrieval, while lower-priority logs are sent to more cost-effective storage. This results in reduced infrastructure costs, a higher signal-to-noise ratio, and a data foundation ready for immediate investigation.
When these data pipelines automatically enrich logs with crucial context—such as identifying the user and asset associated with a suspicious connection—the output shifts from a raw data point to a definitive starting point for investigation. This end-to-end data problem-solving approach is a key differentiator, with SentinelOne being recognized by IDC MarketScape for its AI SIEM capabilities.
With a clean, structured data foundation established, downstream security tools can operate with significantly accelerated performance. Modern detection engines, like the Singularity AI SIEM, can leverage indexless architectures to manage petabyte-scale telemetry with minimal latency. Detection logic runs continuously against this stream of clean, correlated data, transforming vast amounts of disparate logs into centralized dashboards that provide immediate situational awareness. This approach allows organizations to execute queries up to 70% faster, with AI Data Pipelines further enhancing the process by providing cleaner data for AI analysis and improving overall detection and response capabilities.