In this post, I will talk about the rise of the security data fabric and converging SIEM, data engineering, and AI.
For years, cybersecurity analytics has promised better visibility, faster detection, and more decisive response. In reality, many organizations found themselves buried under sprawling data pipelines, rigid SIEM setups, and an alert volume no team could realistically keep up with. Security teams did not lack data. They lacked a practical way to turn overwhelming and diverse telemetry into timely, trustworthy decisions.
That gap is driving a fundamental rethink of how security data is collected and used. In 2025, many organizations are embracing the security data fabric, not as another tool, but as an architectural shift.
By applying modern data engineering and AI directly to security workflows, telemetry becomes fluid, connected, and actionable. Detection adapts as threats evolve, signals correlate across cloud, endpoint, identity, and network domains, and AI moves into the operational core. The result is faster decisions, smarter automation, and security operations built to scale.
Table of Contents
Why Traditional SIEMs Can’t Keep Up
Traditional SIEM platforms were architected for perimeter-based networks, predictable log sources, and bounded data volumes. That model breaks down in modern environments defined by cloud native workloads, SaaS adoption, identity-centric access, APIs, and highly distributed endpoints.
Security teams now ingest high-velocity telemetry across multiple domains, spanning structured, semi-structured, and unstructured data that rarely conforms to a single schema or timing model.
Traditional SIEMs struggle because they:
- Enforce rigid schemas that delay the onboarding of new telemetry sources.
- Rely on static correlation rules that fail against evolving attack techniques.
- Degrade in performance and cost efficiency as data volumes scale.
- Lack native AI and ML pipelines to detect behavioral anomalies and unknown threats.
As a result, SOC teams spend more time engineering data pipelines than hunting adversaries. Detection becomes delayed, response becomes reactive, and analyst effectiveness declines.
This is where modern data engineering concepts are redefining how security operations are built and scaled.
Data Engineering Meets Cyber Defense
At its core, a Security Data Fabric applies modern data engineering discipline to security telemetry, treating it as a continuously available, analytics ready asset. Instead of funneling everything through monolithic log pipelines, telemetry is ingested through flexible extract, transform, and load workflows and stored in normalized schemas within a security focused data lake.
Key data engineering capabilities now reshaping SOC architecture include:
- Schema normalization to translate diverse sources such as Syslog, JSON events, and API telemetry into a unified model.
- Data lakes and lake houses that store raw and enriched telemetry for scalable analytics, threat hunting, and AI training.
- Metadata tagging and lineage tracking to preserve context, support investigations, and meet audit requirements.
- Streaming and micro batch processing to enable near real time detection and enrichment without traditional SIEM latency.
By embedding these capabilities into security operations, organizations convert fragmented telemetry into consistent, actionable cyber intelligence.
Proof of the Architectural Shift
Modern security platforms are increasingly built on data lake-driven architectures that ingest multi domain telemetry across endpoint, cloud, network, identity, and application layers.
Rather than relying on rigid ingestion pipelines, these systems normalize, correlate, and enrich signals through AI assisted workflows, reducing manual integration and correlation effort. Their design reflects mature data engineering patterns, including scalable object storage, event streaming, and schema on read flexibility.
This approach is extending to the edge, where OT and IoT telemetry is captured near the source, enriched locally, and forwarded upstream in structured form. The result is AI ready security data infrastructure capable of supporting hybrid, distributed environments.
SOCs gain unified visibility without prolonged integration cycles, allowing analysts to focus less on data preparation and more on active threat disruption.
The Executive Challenge: Complexity and Cost
The promise of a security data fabric is powerful, but execution raises real concerns for executive leadership. For CISOs and CIOs, three challenges dominate strategy discussions.
- Data volume and cost.
As telemetry explodes, storage, processing, and analytics costs rise quickly. Without governance, AI-driven SOCs risk becoming expensive data sinkholes.
Way forward:Apply tiered ingestion, adaptive retention, and signal scoring to ensure analytics focus on high value security telemetry. - Integration complexity.
Hybrid environments, legacy tools, and proprietary formats complicate ingestion and correlation. Many SOCs lack deep data engineering expertise.
Way forward:Treat the SOC as a data platform and align security analysts, engineers, and data specialists around shared pipelines. - Governance and trust.
AI-powered detection demands transparency, lineage, and regulatory alignment.
Way forward:Enforce explainable analytics, strong metadata controls, and zero trust access models.
The message is clear. The modern SOC is evolving from a security function into a data driven defense platform.
AI as the Intelligence Layer
Once telemetry is structured and governed, AI becomes the force multiplier. Modern security operations are moving beyond static analytics to AI systems that continuously learn, correlate, and act across the data fabric.
Machine learning models surface behavioral anomalies and link weak signals across domains. Large language models accelerate investigations by summarizing incidents and suggesting remediation steps.
Agentic AI applies controlled automation, executing containment actions within defined confidence thresholds while preserving human oversight. The result is a shift from reactive alert handling to adaptive, intelligence driven defense.
Strategic Way Forward: From Visibility to Intelligence
CISOs do not need to rip and replace existing tools to move forward. Progress comes through phased modernization. Start by unifying telemetry using schema on read approaches.
Establish strong data governance with clear ownership and access controls. Favor modular, API driven architectures that support AI natively. Align security, data, and AI talent under a single operating model.
Introduce AI first as decision support, then expand toward automation with clear guardrails. Organizations that follow this path turn visibility into scalable security intelligence.
Conclusion: Engineering Resilience Through Intelligence
The security data fabric marks a shift from collecting information to engineering intelligence.
By treating telemetry as strategic capital and applying data engineering and AI with strong governance, SOCs can move beyond reactive alerting toward continuous learning, seamless correlation, and proactive resilience.
The future of cybersecurity belongs to architecture driven intelligence, not dashboards.
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About the Author:
Prassanna Rao Rajgopal is a cybersecurity leader with over 22 years of global experience spanning security operations, risk management, enterprise security architecture, and partner-led defense strategies. He currently leads cybersecurity initiatives for the Americas region, heading partner engineering efforts and advising CISOs and senior security leaders across Fortune 500 organizations. His work focuses on building scalable, measurable, and risk-aligned security programs that help enterprises move beyond reactive defense toward integrated, future-ready security operations. Prassanna regularly writes on emerging cybersecurity and AI topics, with published articles and commentary featured on platforms such as RSA Conference blogs, HackerNoon, and Security Boulevard. His writing is grounded in real-world practitioner experience and aims to translate complex security architectures, SOC transformation, and AI-driven defense into actionable insights for security leaders. He is an active member of IEEE, ISACA, and EC-Council.
