Cybersecurity Data Fabric: What on earth is security data fabric?

Understand what a Security Data Fabric is, and why an enterprise security team needs one to achieve better security while reducing SIEM and storage costs

March 12, 2024

Book a Demo

Back to Articles

On this page

Why are Legacy SIEMs a problem?

What on earth is security data fabric, and why do we suddenly need one?

Every time I am at a security conference, a new buzzword is all over most vendors’ signage, one year it was UEBA (User Entity and Behavioral Analytics), next EDR (Endpoint Detection and Response), then XDR (Extended Detection and Response), then it was (ASM) Attack Surface Management. Some of these are truly new and valuable capabilities, some of these are rebranding of an existing capability. Some vendors have something to do with the new capability (i.e., buzzword), and some are just hoping to ride the wave of the hype. This year, we will probably hear a lot on GenAI and cybersecurity, and on the security data fabric. Let me tackle the latter in this article, with another article to follow soon on GenAI and Cybersecurity.

Problem Statement:

Many organizations are dealing with an explosion of security logs directed to the SIEM and other security monitoring systems, Terabytes of data every day!

How to better manage the growing cost of the security log data collection?
Do you know if all of this data clogging your SIEM storage has high security value?
Are you collecting the most relevant security data?

To illustrate, here is an example of windows security events and a view on what elements have high security value compared to the total volume typically collected:

Do you have genuine visibility into potential security log data duplication and underlying inconsistencies? Is your system able to identify missing security logs and security log schema draft fast enough for your SOC to avoid missing something relevant?

As SIEM and security analytics capabilities evolve, how do to best decouple security log integration from SIEM and other threat detection platforms to allow not only easier migration to lasted technology but provide cost-effective and seamless access of this security data for threat hunting and other user groups?

Major Next Gen SIEMs operate on a consumption-based model expecting end users to break down queries by data source and/or narrowed time range; which increases the total # of queries executed and increases your cost significantly!! Major Next-Gen SIEMs operate on a consumption-based model expecting end users to break down queries by data source and/or narrowed time range; which increases the total # of queries executed and increases your cost significantly!!

As security practitioners, we either accepted these issues as the cost of running our SOC, handled some of these issues manually, or hoped that either the cloud and/or SIEM vendors would one day have a better approach to deal with these issues, to no avail. This is why you need a security data fabric.

What is a Security Data Fabric (SDF)?

A data fabric is a solution that connects, integrates, and governs data across different systems and applications. It uses artificial intelligence and metadata automation to create flexible and reusable data pipelines and services. For clarity, a data fabric is simply a set of capabilities that allows you a lot more control of your data end to end, on how this data is ingested and where to forward it and stores it, in service of your business end goals, compared to just collecting and hoarding a heap of data in an expensive data lake, and hoping one day some use will come of it. The security data fabric is meant to tightly couple these principles with deep security expertise and the use of artificial intelligence to allow mastery of your security data and optimize your security monitoring investments and enable enhanced threat detection.

They key outcome of a security data fabric is to allow security teams to focus on their core function (i.e., threat detection) instead of spending countless hours tinkering with data engineering tasks, which means automation, seamless integration and minimal overhead on ongoing operations.

Components of a Security Data Fabric (SDF):

Smart Collection:

This is meant to decouple the collection of the security data logs from the SIEM/UEBA vendor you are using. This allows the ability to send the relevant security data to the SIEM/UEBA, sending a copy to a security data lake to create additional AI-enabled threat detection use cases (i.e., AI workbench) or to perform threat hunting, and send compliance-related logs to cold storage.

Why important?

Minimize vendor lock-in and allow your system to leverage this data in various environments and formats, without needing to pay multiple times to use your own security data outside of the SIEM - particularly for requirements such as threat hunting and the creation of advanced threat-detection use cases using AI.‍

Eliminate data loss with traditional SIEM log forwarders, syslog relay servers.

Eliminate custom code/scripts for data collection.‍

Reduced data transfer between cloud environments, especially in the case of having a hybrid cloud environment.

Security Data Orchestration:

This is where the security expertise in the security data fabric becomes VERY important. The security data orchestration includes the following elements:

Normalize, Parse, and Transform: Apply AI and security expertise for seamless normalization, parsing, and transforming of security data into the format you need for ingestion into your SIEM/UEBA tool, such as OCSF, CEF, CIM, or to a security data lake, or other data storage solutions.

Data Forking: Again, applying AI and security expertise to identify which security logs have the right fields and attributes that have threat detection value and should be sent to the SIEM, and which other logs should be sent straight to cold storage for compliance purposes, as an example.

Data Lineage and Data Observability: These are well-established capabilities in data management tools. We are applying it here to security data, so we no longer need to wonder if the threat detection rule is not firing because the log source is dead/MIA or because there are no hits. Existing collectors do not always give you visibility for individual log sources (at the level of the Individual device and log attribute/telemetry). This capability solves this challenge.

Data Quality: Ability to monitor and alert on schema drift and track the consistency, completeness, reliability, and relevance of the security data collected, stored, and used

Data Enrichment: This is where you start getting exciting value. The security data fabric uses its visibility to all your security data with insights using advanced AI such as:
‍
- Correlate with threat intel showing new CVEs or IoCs impacting your assets, here is how it looks in the MITRE Att&ck kill chain and provides a historical view of the potential presence of these indicators in your environment.
- Recommendations on new threat detection use cases to apply based on your threat profile.

Why important?

Automation: At face value, existing tools promise some of these capabilities, but they usually need a massive amount of manual effort and deep security expertise to implement. This allows the SOC team to focus on their core function (i.e., threat detection) instead of spending countless hours tinkering with data engineering tasks.
‍
Volume Reduction: This is the most obvious value of using a security data fabric. You can reduce 30-50% of the data volume being sent to your SIEM by using a security-intelligent data fabric, as it will only forward data that has security value to your SIEM and send the rest to cheaper data storage. Yes, you read this correctly, 30-50% volume reduction! Imagine the cost savings and how much new useful security data you can start sending to your SIEM for enhanced threat detection.
‍
Enhanced Threat Detection: An SDF will enable the threat-hunting team to run queries more effectively and cheaply by giving them the ability to access a separate data lake, you get full control of your security data, and ongoing enrichments in how to improve your threat detection capabilities. Isn’t this what a security solution is about at the end of the day?

See all articles

The Future of the SOC is Intelligent Data Pipelines

A reflection on the recent Software Analyst Cybersecurity Research (SACR) report, and a note on the future of security telemetry

January 23, 2026

Every industry goes through moments of clarity, moments when someone steps back far enough to see not just the technologies taking shape, but the forces shaping them. The Software Analyst Cybersecurity Research (SACR) team’s latest report on Security Data Pipeline Platforms (SDPP) is one of those moments. It is rare for research to capture both the energy and the tension inside a rapidly evolving space, and to do so with enough depth that vendors, customers, and analysts all feel seen. Their work does precisely that.

Themes from the Report

Several themes stood out to us at Databahn because they reflect what we hear from customers every day. One of those themes is the growing role of AI in security operations. SACR is correct in noting that AI is no longer just an accessory. It is becoming essential to how analysts triage, how detections are created, and how enterprises assess risk. For AI to work effectively, it needs consistent, governed, high-quality data, and the pipeline is the only place where that foundation can be maintained.

Another theme is the importance of visibility and monitoring throughout the pipeline. As telemetry expands across cloud, identity, OT, applications, and infrastructure, the pipeline has become a dynamic system rather than just a simple conduit. SOC teams can no longer afford blind spots in how their data flows, what is breaking upstream, or how schema changes ripple downstream. SACR’s recognition of this shift reflects what we have observed in many large-scale deployments.

Resilience is also a key theme in the report. Modern security architecture is multi-cloud, multi-SIEM, multi-lake, and multi-tool. It is distributed, dynamic, and often unpredictable. Pipelines that cannot handle drift, bursts, outages, or upstream failures simply cannot serve the SOC. Infrastructure must be able to gracefully degrade and reliably recover. This is not just a feature; it is an expectation.

Finally, SACR recognizes something that is becoming harder for vendors to admit: the importance of vendor neutrality. Neutrality is more than just an architectural choice; it’s the foundation that enables enterprises to choose the right SIEM for their needs, the right lake for their scale, the right detection strategy for their teams, and the right AI platforms for their maturity. A control plane that isn’t neutral eventually becomes a bottleneck. SACR’s acknowledgment of this risk demonstrates both insight and courage.

The future of the SOC has room for AI, requires deep visibility, depends on resilience, and can only remain healthy if neutrality is preserved. Another trend that SACR’s report tracked was the addition of adjacent functions, bucketed as ‘SDP Plus’, which covered a variety of features – adding storage options, driving some detections in the pipeline directly, and observability, among others. The report has cited Databahn for their ‘pipeline-centric’ strategy and our neutral positioning.

As the report captures what the market is doing, it invites each of us to think more deeply about why the market is doing it and whether that direction serves the long-term interests of the SOC.

The SDP Plus Drift

Pipelines that started with clear purpose have expanded outward. They added storage. They added lightweight detection. They added analytics. They built dashboards. They released thin AI layers that sat beside, rather than inside, the data. In most cases, these were not responses to customer requests. They were responses to a deeper tension, which is that pipelines, by their nature, are quiet. A well-built pipeline disappears into the background. When a category is young, vendors fear that silence. They fear being misunderstood. And so they begin to decorate the pipeline with features to make it feel more visible, more marketable, more platform-like.

It is easy to understand why this happens. It is also easy to see why it is a problem.

A data pipeline has one essential purpose. It moves and transforms data so that every system around it becomes better. That is the backbone of its value. When a pipeline begins offering storage, it creates a new gravity center inside the enterprise. When it begins offering detection, it creates a new rule engine that the SOC must tune and maintain. When it adds analytics, it introduces a new interpretation layer that can conflict with existing sources of truth. None of these actions are neutral. Each shifts the role of the pipeline from connector to competitor.

This shift matters because it undermines the very trust that pipelines rely on. It is similar to choosing a surgeon. You choose them for their precision, their judgment, their mastery of a single craft. If they try to win you over by offering chocolates after the surgery, you might appreciate the gesture, but you will also question the focus. Not because chocolates are bad, but because that is not why you walked into the operating room.

Pipelines must not become distracted. Their value comes from the depth of their craft, not the breadth of their menu. This is why it is helpful to think about security data pipelines as infrastructure. Infrastructure succeeds when it operates with clarity. Kubernetes did not attempt to become an observability tool. Snowflake did not attempt to become a CRM. Okta did not attempt to become a SIEM. What made them foundational was their refusal to drift. They became exceptional by narrowing their scope, not widening it. Infrastructure is at its strongest when it is uncompromising in its purpose.

Security data pipelines require the same discipline. They are not just tools; they are the foundation. They are not designed to interpret data; they are meant to enhance the systems that do. They are not intended to detect threats; they are meant to ensure those threats can be identified downstream with accuracy. They do not own the data; they are responsible for safeguarding, normalizing, enriching, and delivering that data with integrity, consistency, and trust.

The Value of SDPP Neutrality

Neutrality becomes essential in this situation. A pipeline that starts to shift toward analytics, storage, or detection will eventually face a choice between what's best for the customer and what's best for its own growing platform. This isn't just a theoretical issue; it's a natural outcome of economic forces. Once you sell a storage layer, you're motivated to route more data into it. Once you sell a detection layer, you're motivated to optimize the pipeline to support your detections. Neutrality doesn't vanish with a single decision; it gradually erodes through small compromises.

At Databahn, neutrality isn't optional; it's the core of our architecture. We don’t compete with the SIEM, data lake, detection systems, or analytics platforms. Instead, our role is to support them. Our goal is to provide every downstream system with the cleanest, most consistent, most reliable, and most AI-ready data possible. Our guiding principle has always been straightforward: if we are infrastructure, then we owe our customers our best effort, not our broadest offerings.

This is why we built Cruz as an agentic AI within the pipeline, because AI that understands lineage, context, and schema drift is far more powerful than AI that sits on top of inconsistent data. This is why we built Reef as an insight layer, not as an analytics engine, because the value lies in illuminating the data, not in competing with the tools that interpret it. Every decision has stemmed from a belief that infrastructure should deepen, not widen, its expertise.

We are entering an era in cybersecurity where clarity matters more than ever. AI is accelerating the complexity of the SOC. Enterprises are capturing more telemetry than at any point in history. The risk landscape is shifting constantly. In moments like these, it is tempting to expand in every direction at once. But the future will not be built by those who try to be everything. It will be built by those who know exactly what they are, and who focus their energy on becoming exceptional at that role.

Closing thoughts

The SACR report highlights how far the category has advanced. I hope it also serves as a reminder of what still needs attention. If pipelines are the control plane of the SOC, then they must stay pure. If they are infrastructure, they must be built with discipline. If they are neutral, they must remain so. And if they are as vital to the future of AI-driven security as we believe, they must form the foundation, not just be a feature.

At Databahn, we believe the most effective pipeline stays true to its purpose. It is intelligent, reliable, neutral, and deeply focused. It does not compete with surrounding systems but elevates them. It remains committed to its craft and doubles down on it. By building with this focus, the future SOC will finally have an infrastructure layer worthy of the intelligence it supports.

5 min read

The State of Observability in 2026: Why “almost observable” Isn’t Enough

Enterprises have more observability data than ever, yet outages keep getting worse. This blog explores why “almost observable” systems fail, how fragmented telemetry hides real signals, and why moving control upstream is becoming critical for reliable, scalable observability.

January 20, 2026

Picture this: it’s a sold-out Saturday. The mobile app is pushing seat upgrades, concessions are running tap-to-pay, and the venue’s “smart” cameras are adjusting staffing in real time. Then, within minutes, queues freeze. Kiosks time out. Fans can’t load tickets. A firmware change on a handful of access points creates packet loss that never gets flagged because telemetry from edge devices isn’t normalized or prioritized. The network team is staring at graphs, the app team is chasing a “payments API” ghost, and operations is on a walkie-talkie trying to reroute lines like it’s 1999.

Nothing actually “broke” – but the system behaved like it did. The signal existed in the data, just not in one coherent place, at the right time, in a format anyone could trust.

That’s where the state of observability really is today: tons of data, not enough clarity – especially close to the source, where small anomalies compound into big customer moments.

Why this is getting harder, not easier

Every enterprise now runs on an expanding mix of cloud services, third-party APIs, and edge devices. Tooling has sprawled for good reasons – teams solve local problems fast – but the sprawl works against global understanding. Nearly half of organizations still juggle five or more tools for observability, and four in ten plan to consolidate because the cost of stitching signals after the fact is simply too high.

More sobering: high-impact outages remain expensive and frequent. A majority report that these incidents cost $1M+ per hour; median annual downtime still sits at roughly three days; and engineers burn about a third of their week on disruptions. None of these are “tool problems” – they’re integration, governance, and focus problems. The data is there. It just isn’t aligned.

What good looks like; and why we aren’t there yet

The pattern is consistent: teams that unify telemetry and move toward full-stack observability outperform. They see radically less downtime, lower hourly outage costs, and faster mean-time-to-detect/resolve (MTTD/MTTR). In fact, organizations with full-stack observability experience roughly 79% less downtime per year than those without – an enormous swing that shows what’s possible when data isn’t trapped in silos.

But if the winning patterns is so clear, why aren’t more teams there already?

Three reasons keep coming up in practitioner and leadership conversations:

Heterogeneous sources, shifting formats. New sensors, services, and platforms arrive with their own schemas, naming, and semantics. Without upstream normalization, every dashboard and alert “speaks a slightly different dialect.” Governance becomes wishful thinking.

Point fixes vs. systemic upgrades. It’s hard to lift governance out of individual tools when the daily firehose keeps you reactive. You get localized wins, but the overall signal quality doesn’t climb.

Manual glue. Humans are still doing context assembly – joining business data with MELT, correlating across tools, re-authoring similar rules per system. That’s slow and brittle.

Zooming out: what the data actually says

Let’s connect the dots in plain English:

Tool sprawl is real. 45% of orgs use five or more observability tools. Most use multiple, and only a small minority use one. It’s trending down, and 41% plan to consolidate – but today’s reality remains multi-tool.

Unified telemetry pays off. Teams with more unified data experience ~78% less downtime vs. those with siloed data. Said another way: the act of getting logs, metrics, traces, and events into a consistent, shared view delivers real business outcomes.

The value is undeniable. Median annual downtime across impact levels clocks in at ~77 hours; for high-impact incidents, 62% say the hourly cost is at least $1M. When teams reach full-stack observability, hourly outage costs drop by nearly half.

We’re still spending time on toil. Engineers report around 30% of their time spent addressing disruptions. That’s innovation time sacrificed to “finding and fixing” instead of “learning and improving.”

Leaders want governance, not chaos. There’s a clear preference for platforms that are more capable at correlating telemetry with business outcomes and generating visibility without spiking manual effort and management costs.

The edge is where observability’s future lies

Back to our almost-dark stadium. The fix isn’t “another dashboard.” It’s moving control closer to where telemetry is born and ensuring the data becomes coherent as it moves, not after it lands.

That looks like:

Upstream normalization and policy: standardizing fields, units, PII handling, and tenancy before data fans out to tools.

Schema evolution without drama: recognizing new formats at collection time, mapping them to shared models, and automatically versioning changes.

Context attached early: enriching events with asset identity, environment, service boundaries, and – crucially – business context (what this affects, who owns it, what “good” looks like), so investigators don’t have to hunt for meaning later.

Fan-out by design, not duplication: once the signal is clean, you can deliver the same truth to APM, logs, security analytics, and data lakes without re-authoring rules per tool.

When teams do this, the graphs start agreeing with each other. And when the graphs agree, decisions accelerate. Every upstream improvement makes all of your downstream tools and workflows smarter. Compliance is easier and more governed; data is better structured; its routing is more streamlined. Audits are easier and are much less likely to surface annoying meta-needs but are more likely to generate real business value.

The AI inflection: less stitching, more steering

The best news? We finally have the toolsto automate the boring parts and amplify the smart parts.

AIOps that isn’t just noise. With cleaner, standardized inputs, AI has less “garbage” to learn from and can detect meaningful patterns (e.g., “this exact firmware + crowd density + POS jitter has preceded incidents five times in twelve months”).

Agentic workflows. Instead of static playbooks, agentic AI can learn and adapt: validate payloads, suggest missing context, test routing changes, or automatically revert a bad config on a subset of edge devices – then explain what it did in human terms.

Human-in-the-loop escalation. Operators set guardrails; AI proposes actions, runs safe-to-fail experiments, and asks for approval on higher-risk steps. Over time, the playbook improves itself.

This isn’t sci-fi. In the same industry dataset, organizations leaning into AI monitoring and related capabilities report higher overall value from their observability investments – and leaders list adoption of AI tech as a top driver for modernizing observability itself.

Leaders are moving – are you?

Many of our customers are finding our AI-powered pipelines – with agentic governance at the edge through the data path – as the most reliable way to harness the edge-first future of observability. They’re not replacing every tool; they’re elevating the control plane over the tools so that managing what data gets to each tool is optimized for cost, quality, and usefulness. This is the shift that is helping our Fortune 100 and Fortune 500 customers convert flight data, OT telemetry, and annoying logs into their data crown jewels.

If you want the full framework and the eight principles we use when designing modern observability, grab the whitepaper, Principles of Intelligent Observability, and share it with your team. If you’d like to explore how AI-powered pipelines can make this real in your environment, request a demo and learn more about how our existing customers are using our platform to solve security and observability challenges while accelerating their transition into AI.

5 min read

Why CISOs are choosing independent, vendor-neutral pipelines

Why enterprise SOCs across the Fortune 500 and Global 2000 want to take back control of their telemetry

January 14, 2026

Enterprise security teams have been under growing pressure for years. Telemetry volumes have increased across cloud platforms, identity systems, applications, and distributed infrastructure. As data grows, - SIEM and storage costs rise faster than budgets. Pipeline failures - happen more often during peak times. Teams lose visibility precisely when they need it most. Data engineers are overwhelmed by the range of formats, sources, and fragile integrations across a stack that was never meant to scale this quickly. What was once a manageable operational workflow has become a source of increasing technical debt and operational risk.

These challenges have elevated the pipeline from a mere implementation detail to a strategic component within the enterprise. Organizations now understand that how telemetry is collected, normalized, enriched, and routed influences not only cost but also resilience, visibility, and the effectiveness of modern analytics and AI tools. CISOs are realizing that they cannot build a future-ready SOC without controlling the data plane that supplies it. As this shift speeds up, a clear trend has emerged among the Fortune 500 and Global 2000 companies - Security leaders are opting for independent, vendor-neutral pipelines that simplify complexity, restore ownership, and deliver consistent, predictable value from their telemetry.

Why Neutrality Matters More than Ever

Independent, vendor-neutral pipelines provide a fundamentally different operating model. They shift control from the downstream tool to the enterprise itself. This offers several benefits that align with the long-term priorities of CISOs.

Flexibility to choose best-of-breed tools

A vendor-neutral pipeline enables organizations to choose the best SIEM, XDR, SOAR, storage system, or analytics platform without fretting over how tooling changes will impact ingestion. The pipeline serves as a stable architectural foundation that supports any mix of tools the SOC needs now or might adopt in the future.

Compared to SIEM-operated pipelines, vendor-neutral solutions offer seamless interoperability across platforms, reduce the cost and effort of managing multiple best-in-breed tools, and deliver stronger outcomes without adding setup or operational overhead. This flexibility also supports dual-tool SOCs, multi-cloud environments, and evaluation scenarios where organizations want the freedom to test or migrate without disruptions.

Unified Data Ops across Security, IT, and Observability

Independent pipelines support open schemas and standardized models like OCSF, CIM, and ECS. They enable telemetry from cloud services, applications, infrastructure, OT systems, and identity providers to be transmitted into consistent and transparent formats. This facilitates unified investigations, correlated analytics, and shared visibility across security, IT operations, and engineering teams.

Interoperability becomes even more essential as organizations undertake cloud transformation initiatives, use security data lakes, or incorporate specialized investigative tools. When the pipeline is neutral, data flows smoothly and consistently across platforms without structural obstacles. Intelligent, AI-driven data pipelines can handle various use cases, streamline telemetry collection architecture, reduce agent sprawl, and provide a unified telemetry view. This is not feasible or suitable for pipelines managed by MDRs, as their systems and architecture are not designed to address observability and IT use cases. 

Modularity that Matches Modern Enterprise Architecture

Enterprise architecture has become modular, distributed, and cloud native. Pipelines tied to a single analytics tool or managed service provider - act as a challenge today for how modern organizations operate. Independent pipelines support modular design principles by enabling each part of the security stack to evolve separately.

A new SIEM should not require rebuilding ingestion processes from scratch. Adopting a data lake should not require reengineering normalization logic.and adding an investigation tool should not trigger complex migration events. Independence ensures that the pipeline remains stable while the surrounding technology ecosystem continues to evolve. It allows enterprises to choose architectures that fit their specific needs and are not constrained by their SIEM’s integrations or their MDR’s business priorities.

Cost Governance through Intelligent Routing

Vendor-neutral pipelines allow organizations to control data routing based on business value, risk tolerance, and budget. High-value or compliance-critical telemetry can be directed to the SIEM. Lower-value logs can be sent to cost-effective storage or cloud analytics services.

This prevents the cost inflation that happens when all data is force-routed into a single analytics platform. It enhances the CISO’s ability to control SIEM spending, manage storage growth, and ensure reliable retention policies without losing visibility.

Governance, Transparency, and Control

Independent pipelines enforce transparent logic around parsing, normalization, enrichment, and filtering. They maintain consistent lineage for every transformation and provide clear observability across the data path.

This level of transparency is important because data governance has become a key enterprise requirement. Vendor-neutral pipelines make compliance audits easier, speed up investigations, and give security leaders confidence that their visibility is accurate and comprehensive. Most importantly, they keep control within the enterprise rather than embedding it into the operating model of a downstream vendor, the format of a SIEM, or the operational choices of an MDR vendor.

AI Readiness Through High-Quality, Consistent Data

AI systems need reliable, well-organized data. Proprietary ingestion pipelines restrict this because transformations are designed for a single platform, not for multi-tool AI workflows.

Neutral pipelines deliver:

consistent schemas across destinations
enriched and context-ready data
transparency into transformation logic
adaptability for new data types and workloads

This provides the clean and interoperable data layer that future AI initiatives rely on. It supports AI-driven investigation assistants, automated detection engineering, multi-silo reasoning, and quicker incident analysis.

The Long-Term Impact of Independence

Think about an organization planning its next security upgrade. The plan involves cutting down SIEM costs, expanding cloud logging, implementing a security data lake, adding a hunting and investigation platform, enhancing detection engineering, and introducing AI-powered workflows.

If the pipeline belongs to a SIEM or MDR provider, each step of this plan depends on vendor capabilities, schemas, and routing logic. Every change requires adaptation or negotiation. The plan is limited by what the vendor can support and - how they decide to support it.

When the pipeline is part of the enterprise, the roadmap progresses more smoothly. New tools can be incorporated by updating routing rules. Storage strategies can be refined without dependency issues. AI models can run on consistent schemas. SIEM migration becomes a simpler decision rather than a lengthy engineering project. Independence offers more options, and that flexibility grows over time.

Why Independent Pipelines are Winning

Independent pipelines have gained momentum across the Fortune 500 and Global 2000 because they offer the architectural freedom and governance that modern SOCs need. Organizations want to use top-tier tools, manage costs predictably, adopt AI on their own schedule, and retain ownership of the data that defines their security posture. Early adopters embraced SDPs because they sat between systems, providing architectural control, flexibility, and cost savings without locking customers into a single platform. As SIEM, MDR, and data infrastructure players have acquired or are offering their own pipelines, the market risks returning to the very vendor dependency that SIEMs were meant to eliminate. In a practitioner’s words from SACR’s recent report, “we’re just going to end up back where we started, everything re-bundled under one large platform.”

According to Francis Odum, a leading cybersecurity analyst, “ … the core role of a security data pipeline solution is really to be that neutral party that’s able to ingest no matter whatever different data sources. You never want to have any favorites, as you want a third-party that’s meant to filter.” When enterprise security leaders choose their data pipelines, they want independence and flexibility. An independent, vendor-neutral pipeline is the foundation of architectures that keep control with the enterprise.

Databahn has become a popular choice during this transition because it shows what an enterprise-grade independent pipeline can achieve in practice. Many CISOs worldwide have selected our AI-powered data pipeline platform due to its flexibility and ease of use, decoupling telemetry ingestion from SIEM, lowering SIEM costs, automating data engineering tasks, and providing consistent AI-ready data structures across various tools, storage systems, and analytics engines.

The Takeaway for CISOs

The pipeline is no longer an operational layer. It is a strategic asset that determines how adaptable, cost-efficient, and AI-ready the modern enterprise can be. Vendor-neutral pipelines offer the flexibility, interoperability, modularity, and governance that CISOs need to build resilient and forward-looking security programs.

This is why independent pipelines are becoming the standard for organizations that want to reduce complexity, maintain freedom of choice and unlock greater value from their telemetry. In a world where tools evolve quickly, where data volumes rise constantly and where AI depends on clean and consistent information, the enterprises that own their pipelines will own their future.