The modern security operations center (SOC) has many challenges, but having enough data isn't one of them. SOC analysts experience ongoing challenges with too many alerts (59%), dealing with too many false positives (55%), and deciphering alerts that lack context (46%), according to the Splunk State of Security 2025 Report. Data, in other words, is abundant. The problem is making it useful.
Today's SOC analysts are drowning in information they can't easily act on. Potential assets become liabilities when teams are left manually stitching context together, interpreting dashboards in isolation, and pivoting between disconnected tools that each reveal only a fraction of the full picture. Even as AI capabilities advance, outcomes still hinge on humans approving agents to execute changes across a fragmented toolchain, often without full context those decisions demand.
The result is a daily crisis of context. Security professionals routinely make high-stakes decisions based on data they can't see, correlate, or trust in time. The consequences of material latency, inconsistency, missed opportunities, and elevated risk are not theoretical, but operational reality.
Data fabric architecture offers a way forward. By providing a unified, intelligent layer across disparate data sources from SecOps, ITOps, and NetOps, a data fabric breaks down silos and delivers context-rich insights at speed for an AI-driven future.
Barriers to scaling AI initiatives
This crisis didn't happen overnight. It accumulated incrementally, one tool, one workaround at a time, until fragmentation became the defining characteristics of the modern SOC. According to Cisco's 2026 Data and Privacy Benchmark Study, nearly seven out of 10 organizations report ongoing difficulty accessing relevant, high-quality data efficiently. They often cite the cost and effort of data preparation as a barrier to scaling AI initiatives.
Consider a routine investigation of a suspicious login alert. The analysts start the SIEM dashboard to review the alerts, switch to the EDR platform to check endpoint activity, then cross-reference several spreadsheets containing threat intelligence indicators. Each discovery in one tool triggers a manual search in the next in a cycle the industry calls "swivel chair syndrome." According to Splunk's 2025 State of Security Report, 46% of SOC teams spend more time maintaining tools than actually defending their organizations. Meanwhile, the AI tools meant to help are often silos themselves, fragmented assistants with limited scope that compound the problem rather than resolve it.
The cost goes beyond wasting time. Constant context-switching makes it easy to miss critical correlations. Without a holistic picture, analysts cannot build the strategies they need for increasingly sophisticated attacks.
Unifying data across the SOC
Addressing these challenges requires more than incremental improvements — it demands a fundamentally different data strategy. The majority of security users spend their time in search, dashboard, and data management tools. Workflows in those environments need to be faster, simpler, and more productive, powered by both assistive and agentic AI embedded directly where users already work. That means transforming search into a guided experience, improving the clarity of dashboards, and surfacing inconsistencies and risks proactively.
That shift begins with data management, and more specifically, with data fabric architecture. A data fabric is not just another layer of technology, but a fundamental reimagining of how data is accessed, enriched, and operationalized. The fabric abstracts and federates the full spectrum of metrics, events, logs and traces (MELT data), as well as raw logs, isolated alerts from endpoints, networks, clouds, and applications. It then stitches together threat intelligence, asset information, user identities, and behavioral anomalies. The result is visibility that extends from the granular to the panoramic.
For security teams, this means transforming raw telemetry into high-quality, previously unused data that can drive analytics and fuel agentic workflows. By seamlessly integrating cross-domain data sources, a data fabric provides the consistent foundation that enables AI models to adapt, scale, and make accurate decisions. It's important to distinguish this architectural approach from the common industry trend of simple tool consolidation. Consolidation often forces organizations into a "rip and replace" cycle to achieve vendor parity, mandating heavy data migration into a single proprietary repository.
A data fabric takes the opposite approach. It utilizes intelligent federation, allowing data to remain in its native environment while providing a unified data layer for analysis. The result is architectural flexibility without the disruption of wholesale migration.
Return to the "swivel chair syndrome" example from above. Under a data fabric architecture, the manual odyssey of switching between four or more tools is replaced by a unified, context-aware workspace. Because the fabric correlates telemetry in real time, a suspicious login alert arrives pre-enriched with endpoint activity, network flows, and behavior history. The analyst operates from a single, logical layer where the heavy lifting is already done. The "pivot tax" is eliminated, and the time-to-correlation drops from hours of manual investigation to mere seconds of verification.
The benefits of data fabric architecture
For SOC teams, data fabric architecture offers a path toward context that adapts as needs evolve, enabling them to make the leap from reactive firefighting to predictive, intelligent operations.
The benefits are tangible:
- Optimized data and greater insights: Better filtering and prioritizing allow SecOps teams to zero in on the most damaging threats and focus on the investigations that matter most.
- Fewer false alerts: Richer context enables more accurate threat detection, reduces false positives, and frees analysts to focus on critical, high-value work.
- Demonstrable value to leadership: Real-time dashboards and comprehensive audit trails provide clear evidence of security effectiveness, strengthening communication between the SOC and the boardroom.
The crisis of context in the SOC is not a passing challenge. It is the cumulative result of years of data silos, tool fragmentation, and disparate policies that now often define environments. Overcoming it will require a new approach, one that is grounded in data fabric architecture and powered by a new generation of AI agents.
Organizations that unify and enrich their data will unlock their full potential, transforming their SOCs from overwhelmed responders into proactive defenders.
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This post was created by Splunk with Insider Studios.
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