TLDR: Traditional security data pipelines operate on a “bytes in, bytes out” model that requires extensive manual configuration and ongoing professional services. AI-native security data pipelines leverage machine learning to understand data content and context, enabling dynamic filtering, adaptive learning, and automated management. This paradigm shift reduces operational overhead by 90% while improving security outcomes, allowing teams to focus on threat analysis rather than pipeline maintenance.

The security landscape has fundamentally shifted. As threat vectors multiply and data volumes explode, security teams find themselves caught between the need for comprehensive visibility and the harsh reality of spiraling costs. Traditional security data pipelines, once revolutionary, are now revealing their limitations in an AI-driven world.

Having worked with hundreds of security teams navigating this challenge, I’ve observed a critical inflection point: the emergence of AI-native security data pipelines represents more than incremental improvement—it’s a paradigm shift that’s redefining how we approach security data management.

The Legacy Pipeline Paradigm: Bytes In, Bytes Out

Traditional security data pipelines operate on a fundamental principle of “bytes in, bytes out.” Whatever data enters one end of the pipeline emerges largely unchanged at the other end. These systems apply generic filtering rules based on data sources—if you’re processing firewall logs, you get standard firewall filtering capabilities. If you’re handling endpoint data, you receive generic endpoint-based rules.

This approach worked well when security infrastructures were simpler and data volumes were manageable. However, as organizations have expanded their digital footprints and adopted cloud-first strategies, the limitations have become apparent.

The challenge with legacy pipelines extends beyond their basic functionality. These systems lack contextual understanding of the data flowing through them. A customer running a specific implementation of a security tool may generate logs with unique characteristics, but legacy pipelines treat all logs from that vendor identically.

This generic approach creates a cascading series of manual interventions. Security teams must continuously right-size filtering rules, manually configure destination routing, and adjust processing logic as their environments evolve. What appeared to be a one-time professional services investment becomes an ongoing operational burden.

Consider this scenario: A security team implements a traditional pipeline to reduce SIEM costs by filtering out low-value logs. Initially, they see some cost reduction, but over time, they discover they’ve simply shifted costs from their SIEM to pipeline management and professional services. The promised ROI erodes as manual configuration requirements accumulate.

There’s something security leaders need to understand about legacy pipeline implementations: what appears to be a one-time professional services investment quickly becomes an ongoing operational burden.

As organizations add different data sources, as destinations change, and as security requirements evolve, legacy pipelines require continuous professional services engagement. This isn’t a bug—it’s a feature of systems that lack inherent intelligence about the data they’re processing.

AI-native approaches flip this dynamic. Instead of requiring armies of consultants to configure and reconfigure processing logic, these systems leverage machine learning to automate much of this work. The result? Security teams can focus on what actually matters: analyzing threats and protecting their organizations.

The Specialization Question

Some traditional pipeline providers have expanded into observability, financial operations, and other domains. While this diversification demonstrates market opportunity, it raises questions about focus and specialization.

Security data has unique characteristics: it’s highly sensitive, requires specialized compliance handling, and benefits from security-specific intelligence. A pipeline designed for general observability may lack the nuanced understanding needed for optimal security data processing.

Organizations must consider whether they prefer a generalist platform or a specialist solution designed specifically for security use cases. The answer often depends on the organization’s priorities and the specific challenges it faces.

The AI-Native Difference: Transparent Intelligence

AI-native security data pipelines fundamentally reimagine this process by creating transparent windows into data flows. Instead of treating logs as opaque bytes, these systems analyze real-time data contents, understanding field types, sub-contents, and contextual relationships within the data.

This transparency enables several critical capabilities:

Dynamic Filtering Intelligence: Rather than applying generic rules, AI-native pipelines examine actual log contents to create tailored filtering rules. This approach can achieve the same filtering outcomes in a tenth of the time while providing superior cost reduction through precision targeting.

Context-Aware Routing: These systems understand not just the source of data, but the specific configuration generating that data and the preferences of destination systems. This intelligence eliminates much of the manual configuration burden.

Operational Health Monitoring: These systems provide sophisticated health monitoring that tracks the operational status of both data sources and destinations, automatically alerting teams when connectivity issues, performance degradations, or configuration changes occur.

Schema Drift Detection: When security vendors change or augment their log formats without customer notification, AI-Native systems detect schema drift. Traditional pipelines fail silently when schemas change, potentially dropping critical security data or causing downstream processing errors. AI-native systems identify these changes in real-time, automatically adapting parsing rules and alerting security teams to ensure continuous data flow integrity. This capability alone can prevent security blind spots that might otherwise go undetected for weeks or months.

Perhaps the most significant difference between legacy and AI-native approaches is the shift from configuration-heavy implementations to turnkey experiences. Traditional pipelines require extensive professional services for initial setup and ongoing optimization. AI-native systems leverage machine learning to automate much of this configuration.

This shift has profound implications for security team resource allocation. Instead of dedicating staff to pipeline management, teams can focus on threat analysis, incident response, and strategic security initiatives.

Strategic Implications for Security Leaders

As security leaders evaluate their data pipeline strategies, several key considerations emerge:

Total Cost of Ownership: Look beyond initial licensing costs to include ongoing professional services, manual labor, and operational overhead.

Future-Proofing: Consider how pipeline solutions will adapt as your security infrastructure evolves and new data sources emerge.

Integration Complexity: Evaluate how easily pipeline solutions integrate with your existing security stack and planned additions.

Scalability: Ensure your chosen approach can handle projected data volume growth without linear cost increases.

Team Impact: Consider how pipeline management requirements affect your security team’s ability to focus on core security objectives.

The market has reached a clear inflection point. Leading vendors like CrowdStrike and SentinelOne are signaling the same trend: legacy data pipelines and batch-based SIEM workflows are no longer sufficient for today’s SOC. Their recent acquisitions of pipeline companies Onum and Observo underscore the need to rearchitect the data layer around AI-native, real-time intelligence.

This shift validates what we have seen in the field. Security is becoming a data problem, and solving it requires pipelines that are adaptive, context-aware, and built for the scale of modern telemetry. The future SOC will depend on enriched, filtered, and intelligently routed data delivered in real time, not on generic rules or endless manual configuration.

Realm is purpose-built for this moment. While platform vendors extend into pipelines as an add-on to their broader ecosystems, Realm focuses entirely on security data. Our AI-native approach is designed from the ground up to reduce costs, eliminate noise, and provide SOC teams with the clarity they need to act faster.

As security operations evolve toward agentic and autonomous models, the pipeline itself becomes a foundational asset for security teams. Realm is positioned to be a force multiplier for security teams, helping them transform data from an operational burden into a competitive advantage.