Enterprises are rapidly shifting to hybrid data pipeline security as the cornerstone of modern cybersecurity strategy. Telemetry data no longer lives in a single environment—it flows across multi-cloud services, on-premise infrastructure, SaaS platforms, and globally distributed OT/IoT systems. For CISOs, CIOs, and CTOs, the challenge is clear: how do you secure hybrid data pipelines, cut SIEM costs, and prepare telemetry for AI-driven security operations?

With global data creation expected to hit 394 zettabytes by 2028, the stakes are higher than ever. Legacy collectors and agent-based pipelines simply can’t keep pace, often driving up costs while creating blind spots. To meet this challenge, organizations need systems designed to encrypt, govern, normalize, and make telemetry AI-ready across every environment. This guide covers the best practices security leaders should adopt in 2025 and 2026 to protect critical data, reduce vulnerabilities, and future-proof their SOC. 

What enterprises need today is a hybrid data pipeline security strategy – one that ensures telemetry is securely collected, governed, and made AI-ready across all environments. This article outlines the best practices for securing hybrid data pipelines in 2025 and 2026: from reducing blind spots to automating governance, to preparing pipelines for the AI-native SOC.

What is a Hybrid Data Pipeline?

In the context of telemetry, hybrid data pipelines refer to multi-environment data networks. This can consist of a collection of the following – 

• Cloud: Single cloud (one provider, such as AWS, Azure, GCP, etc.) or multiple cloud providers and containers for logs and SaaS telemetry;
• On-Prem: Firewalls, databases, legacy infrastructure;
• OT/IoT: Plants, manufacturing sensors, medical devices, fleet, and logistics tracking

One of our current customers serves as a great example. They are one of the largest biopharmaceutical companies in the world, with multiple business units and manufacturing facilities globally. They operate a multi-cloud environment, have on-premises systems, and utilize geospatially distributed OT/IoT sensors to monitor manufacturing, logistics, and deliveries. Their data pipelines are hybrid as they are collecting data from cloud, on-prem, and OT/IoT sources.

How can Hybrid Data Pipelines be secured?

Before adopting DataBahn, the company relied on SIEM collectors for telemetry data but struggled to manage data flow over a disaggregated network. They operated 6 data centers and four additional on-premises locations, producing over four terabytes of data daily. Their security team struggled to –

• Track and manage multiple devices and endpoints, which number in the tens of thousands;
• Detect, mask, and quarantine sensitive data that was occasionally being sent across their systems;
• Build collection rules and filters to optimize and reduce the log volume being ingested into their SIEM

Hybrid Data Pipeline Security is the practice of ensuring end-to-end security, governance, and resilience across disparate hybrid data flows. It means:

• Encrypting telemetry in motion and at rest.
• Masking sensitive fields (PII, PHI, PCI data) before they hit downstream tools.
• Normalizing into open schemas (e.g., OCSF, CIM) to reduce vendor lock-in.
• Detecting pipeline drift, outages, and silent data loss proactively.

In other words, hybrid data pipeline security is about building a sustainable security data and telemetry management approach that protects your systems, reduces vulnerabilities, and enables you to trust your data while tracking and governing your system easily. 

Common Security Challenges with Hybrid Data Pipelines

Every enterprise security team grappling with hybrid data pipelines knows that complexity kills clarity and leaves gaps that make them more vulnerable to threat actors or missing essential signals.

• Unprecedented Complexity from Data Variety:
  Hybrid systems span cloud, on-prem, OT, and SaaS environments. That means juggling structured, semi-structured, and unstructured data from myriad sources, all with unique formats and access controls. Security professionals often struggle to unify this data into a continuously monitored posture.
• Overwhelmed SIEMs & Alert Fatigue:
  Traditional SIEMs weren’t built for such scale or variety. Hybrid environments inflate alert volumes, triggering fatigue and weakening detection responses. Analysts often ignore alerts – some of which could be critical.
• Siloed Threat Investigation:
  Data scattered across domains adds friction to incident triage. Analysts must navigate different formats, silos, and destinations to piece together threat narratives. This slows investigations and increases risk.
• Security Takes a Backseat to Data Plumbing and Operational Overhead:
  As teams manage integration, agent sprawl, telemetry health, and failing pipelines, strategic security takes a backseat. Engineers spend their time patching collectors instead of reducing vulnerabilities or proactively defending the enterprise.

Why this matters in 2025 and 2026

These challenges aren’t just operational problems; they threaten strategic security outcomes. With Cloud Repatriation becoming a trend among enterprises, with 80% of IT decision-makers moving some flows away from cloud systems [IDC Survey, 2024], companies need to ensure their hybrid systems are equipped to deal with the security challenges of the future.

• Cloud Cost Pressures Meet Telemetry Volume:
  Cloud expenses rise, telemetry grows, and sensitive data (like PII) floods systems. Securing and masking data at scale is a daunting task.
• Greater Regulatory Scrutiny:
  Regulations such as GDPR, HIPAA, and NIS2 now hold telemetry governance to the same scrutiny as system-level defenses. Pipeline breaches equal pipeline failures in risk.
• AI Demands Clean, Contextual Data:
  AI-driven SecOps depends on high-quality, curated telemetry. Messy or ungoverned data undermines model accuracy and trustworthiness.
• Visibility as Strategic Advantage:
  Compromising on visibility becomes the norm for many organizations, leading to blind spots, delayed detection, and fractured incident response.
• Acceptance of Compromise:
  Recent reports reveal that over 90% of security leaders accept trade-offs in visibility or integration, which is an alarming normalization of risk due to strained resources and fatigued security teams.

In 2025, hybrid pipeline security is about building resilience, enforcing compliance, and preparing for AI – not just reducing costs.

Best Practices for Hybrid Data Pipeline Security

• Filter and Enrich at the Edge:
  Deploy collectors to reduce noise (such as heartbeats) before ingestion and enhance telemetry with contextual metadata (asset, geo, user) to improve alert quality.
• Normalize into Open Schemas:
  Use OCSF or CIM to standardize telemetry while boosting portability and avoiding vendor lock-in, while enhancing AI and cross-platform analytics.
• Automate Governance & Data Masking:
  Implement policy-driven redaction and build systems that automatically remove PII/PHI to lower compliance risks and prevent leaks.
• Multi-Destination Routing:
  Direct high-value data to SIEM, send bulk logs to cold storage, and route enriched datasets to cold storage or data lakes, reducing costs and maximizing utility.
• Schema Drift Detection:
  Utilize AI to identify and adapt to log format changes dynamically to maintain pipeline resilience despite upstream alterations.
• Agent / Agentless Optimization:
  Unify tooling into a single collector with hybrid (agent + agentless) capabilities to cut down sprawl and optimize data collection overhead.
• Strategic Mapping to MITRE ATT&CK:
  Link telemetry to MITRE ATT&CK tactics and techniques – improving visibility of high-risk behaviors and focusing collection efforts for better detection.
• Build AI-Ready Pipelines: Ensure telemetry is structured, enriched, and ready for queries, enabling LLMs and agentic AI to provide accurate, actionable insights quickly.

How DataBahn can help

The company we used as an example earlier came to DataBahn looking for SIEM cost reduction, and they achieved a 50% reduction in cost during the POC with minimal use of DataBahn’s in-built volume reduction rules. However, the bigger reason they are a customer today is because they saw the data governance and security value in using DataBahn to manage their hybrid data pipelines.

For the POC, the company routed logs from an industry-leading XDR solution to DataBahn. In just the first week, DataBahn discovered and tracked over 40,000 devices and helped identify more than 3,000 silent devices; the platform also detected and proactively masked over 50,000 instances of passwords logged in clear text. These unexpected benefits of the platform further enhanced the ROI the company saw in the volume reduction and SIEM license fee savings.

Enterprises that adopt DataBahn’s hybrid data pipeline approach realize measurable improvements in security posture, operational efficiency, and cost control.

• Reduced SIEM Costs Without Losing Visibility
  By intelligently filtering telemetry at the source and routing only high-value logs into the SIEM, enterprises regularly cut ingestion volumes by 50% or more. This reduces licensing costs while preserving complete detection coverage.
• Unified Visibility Across IT and OT
  Security leaders finally gain a single control plane across cloud, on-prem, and operational environments. This eliminates silos and enables analysts to investigate incidents with context from every corner of the enterprise.
• Stronger, More Strategic Detection
  Using agentic AI, DataBahn automatically maps available logs against frameworks like MITRE ATT&CK, identifies visibility gaps, and guides teams on what to onboard next. This ensures the detection strategy aligns directly with the threats most relevant to the business.
• Faster Incident Response and Lower MTTR
  With federated search and enriched context available instantly, analysts no longer waste hours writing queries or piecing together data from multiple sources. Response times shrink dramatically, reducing exposure windows and improving resilience.
• Future-Proofed for AI and Compliance
  Enriched, normalized telemetry means enterprises are ready to deploy AI for SecOps with confidence. At the same time, automated data masking and governance ensure sensitive data is protected and compliance risks are minimized.

In short: DataBahn turns telemetry from a cost and complexity burden into a strategic enabler – helping enterprises defend faster, comply smarter, and spend less.

Conclusion

Building and securing hybrid data pipelines isn’t just an option for enterprise security teams; it is a strategic necessity and a business imperative, especially as risk, compliance, and security posture become vital aspects of enterprise data policies. Best practices now include early filtration, schema normalization, PII masking, aligning with security frameworks (like MITRE ATT&CK), and AI-readiness. These capabilities not only provide cost savings but also enable enterprise security teams to operate more intelligently and strategically within their hybrid data networks.

Suppose your enterprise is using or is planning to use a hybrid data system and wants to build a sustainable and secure data lifecycle. In that case, they need to see if DataBahn’s AI-driven, security-native hybrid data platform can help them transform their telemetry from a cost center into a strategic asset.  

Ready to benchmark your telemetry collection against the industry’s best hybrid security data pipeline? Book a DataBahn demo today!

Why Security Engineers Struggle with Data Pipelines

Picture this: It's 3 AM. Your SIEM is screaming about a potential breach. But, instead of hunting threats, your security engineer is knee-deep in parsing errors, wrestling with broken log formats, and frantically writing custom rules to make sense of vendor data that changed overnight, AGAIN!

The unfortunate truth of cybersecurity isn't the sophistication of attacks, it's that most security teams spend over 50% of their time fighting their own data instead of the actual threats.

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Every day, terabytes of security logs flood in: JSON from cloud services, syslog from network devices, CEF from security tools, OTEL from applications, and dozens of proprietary vendor formats. Before your team can even think about threat detection, they're stuck building normalization rules, writing custom parsers, and playing an endless game of whack-a-mole with schema drift.

Here's the kicker: Traditional data pipelines weren't built for security. They were designed for batch analysis with security bolted on as an afterthought. The result? Dangerous blind spots, false positives flooding your SOC, and your best security minds wasting their expertise on data plumbing instead of protecting your organization.

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Garbage in, garbage out

In cybersecurity, garbage data is the difference between detection and disaster. Traditional pipelines were not designed with security as a primary goal. They were built for batch analysis, with security as an afterthought. These pipelines struggle to handle unstructured log formats and enrichment at scale, making it difficult to deliver clean, actionable data for real-time detection. On top of that, every transformation step introduces latency, creating dangerous blind spots where threats can slip by unnoticed.

This manual approach is slow, resource-draining, and keeps teams from focusing on real security outcomes. This is where traditional pipeline management is failing today.

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Automated Data Parsing : Way forward for Security Teams

At DataBahn, we built Cruz to solve this problem with one defining principle: automated data parsing must be the foundation of modern data pipeline management.

Instead of requiring manual scripts or rulebooks, Cruz uses agentic AI to autonomously parse, detect, and normalize telemetry at scale. This means:

• Logs are ingested in any format and parsed instantly.
• Schema drift is identified and corrected in real time.
• Pipelines stay resilient without constant engineering intervention.

With Cruz, data parsing is no longer a manual bottleneck; it’s an automated capability baked into the pipeline layer.

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How does Automated Data Parsing Work?

Ingest Anywhere, Anytime

Cruz connects to any source : firewalls, EDRs, SaaS apps, cloud workloads, and IoT sensors without predefined parsing rules.

Automated Parsing and Normalization

Using machine learning models trained on millions of log structures, Cruz identifies data formats dynamically and parses them into structured JSON or other formats. No manual normalisation required.

Auto-Heal Schema Drift

When vendors add, remove, or rename fields, Cruz automatically adjusts parsing and normalization logic, ensuring pipelines don’t break.

Enrich Before Delivery

Parsed logs can be enriched with metadata like geo-IP, user identity, or asset context, making downstream analysis smarter from the start.

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The Impact of Automated Data Parsing for Enterprises

The biggest challenge in today’s SOCs and observability teams isn’t lack of data; it’s unusable data. Logs trapped in broken formats slow everything down. Cruz eliminates this barrier with automated parsing at the pipeline layer. It means security engineers can finally focus on detection, response, and strategy, keeping alert fatigue at bay.

Security and observability teams using Cruz see:

• Up to 80% less time wasted on manual parsing and normalization
• 2–3x faster MTTR (mean time to resolution)
• Scalable pipelines across hundreds of sources, formats, and vendors

With Cruz, pipelines don’t just move data; they transform messy logs into actionable intelligence automatically. This is data pipeline management redefined: pipelines that are resilient, compliant, and fully autonomous. Experience the future of data pipeline management here.

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SIEM migration is a high-stakes project. Whether you are moving from a legacy on-prem SIEM to a cloud-native platform, or changing vendors for better performance, flexibility, or cost efficiency, more security leaders are finding themselves at this inflection point. The benefits look clear on paper, however, in practice, the path to get there is rarely straightforward.

SIEM migrations often drag on for months. They break critical detections, strain engineering teams with duplicate pipelines, and blow past the budgets set. The work is not just about switching platforms. It is about preserving threat coverage, maintaining compliance, and keeping the SOC running without gaps. And let’s not forget, the challenge of testing multiple SIEMs before making the switch and, what should be a forward-looking upgrade, can quickly turn into a drawn-out struggle.

In this blog, we’ll explore how security teams can approach SIEM migration in a way that reduces risk, shortens timelines, and avoids costly surprises.

What Makes a SIEM Migration Difficult and How to Prepare

Even with a clear end goal, SIEM migration is rarely straightforward. It’s a project that touches every part of the SOC, from ingestion pipelines to detection logic, and small oversights early on can turn into major setbacks later. These are some of the most common challenges security teams face when making the switch.

Data format and ingestion mismatches
Every SIEM has its own log formats, field mappings, and parsing rules. Moving sources over often means reworking normalization, parsers, and enrichment processes, all while keeping the old system running.

Detection logic that doesn’t transfer cleanly
Rules built for one SIEM often fail in another due to differences in correlation methods, query languages, or built-in content. This can cause missed alerts or floods of false positives during migration.

The operational weight of a dual run
Running the old and new SIEM in parallel is almost always required, but it doubles the workload. Teams must maintain two sets of pipelines and dashboards while monitoring for gaps or inconsistencies.

Rushed or incomplete evaluation before migration
Many teams struggle to properly test multiple SIEMs with realistic data, either because of engineering effort or data sensitivity. When evaluation is rushed or skipped, ingest cost issues, coverage gaps, or integration problems often surface mid-migration. A thorough evaluation with representative data helps avoid these surprises.  

In our upcoming SIEM Migration Evaluation Checklist, we’ll share the key criteria to test before you commit to a migration, from log schema compatibility and detection performance to ingestion costs and integration fit.

How DataBahn Reinvents SIEM Migration with a Security Data Fabric

Many of the challenges that slow or derail SIEM migration come down to one thing: a lack of control over the data layer. DataBahn’s Security Data Fabric addresses this by separating data collection and routing from the SIEM itself, giving teams the flexibility to move, test, and optimize data without being tied to a single platform.

Ingest once, deliver anywhere
Connect your sources to a single, neutral pipeline that streams data simultaneously to both your old and new SIEMs. With our new Smart Agent, you can capture data using the most effective method for each source — deploying a lightweight, programmable agent where endpoint visibility or low latency is critical or a hybrid model where agentless collection suffices. This flexibility lets you onboard sources quickly without rebuilding agents or parsers for each SIEM.

Native format delivery
Route logs in the exact schema each SIEM expects, whether that’s Splunk CIM, Elastic UDM, OCSF, or a proprietary model, without custom scripting. Automated transformation ensures each destination gets the data it can parse and enrich without errors or loss of fidelity.

Dual-run without the overhead
Stream identical data to both environments in real time while continuously monitoring pipeline health. Adjust routing or transformations on the fly so both SIEMs stay in sync through the cutover, without doubling engineering work.

AI-powered data relevance filtering
Automatically identify and forward only security-relevant events to your SIEM, while routing non-critical logs into cold storage for compliance. This reduces ingest costs and alert fatigue while keeping a complete forensic archive available when needed.

Safe, representative evaluation
Send real or synthetic log streams to candidate SIEMs for side-by-side testing without risking sensitive data. This lets you validate performance, rule compatibility, and integration fit before committing to a migration.

Unified Migration Workflow with DataBahn

When you own the data layer, migration becomes a sequence of controlled steps instead of a risky, ad hoc event. DataBahn’s workflow keeps both old and new SIEMs fully operational during the transition, letting you validate detection parity, performance, and cost efficiency before the final switch.  

With this workflow, migration becomes a controlled, reversible process instead of a risky, one-time event. You keep your SOC fully operational while gaining the freedom to test and adapt at every stage.

For a deeper look at this process, explore our SIEM Migration use case overview —  from the problems it solves to how it works, with key capabilities and outcomes.

Key Success Metrics for a SIEM Migration

Successful SIEM migrations aren’t judged only by whether the cutover happens on time. The real measure is whether your SOC emerges more efficient, more accurate in detection, and more resilient to change. Those gains are often lost when migrations are rushed or handled ad hoc, but by putting control of the data pipeline at the center of your migration strategy, they become the natural outcome.

• Lower migration costs by eliminating duplicate ingestion setups, reducing vendor-specific engineering, and avoiding expensive reprocessing when formats don’t align.

• Faster timelines because sources are onboarded once, and transformations are handled automatically in the pipeline, not rebuilt for each SIEM.

• Detection parity from day one in the new SIEM, with side-by-side validation ensuring that existing detections still trigger as expected.

• Regulatory compliance by keeping a complete, audit-ready archive of all security telemetry, even as you change platforms.

• Future flexibility to evaluate, run in parallel, or even switch SIEMs again without having to rebuild your ingestion layer from scratch.

These outcomes are not just migration wins, they set up your SOC for long-term agility in a fast-changing security technology landscape.

Making SIEM Migration Predictable

SIEM migration will always be a high-stakes project for any security team, but it doesn’t have to be disruptive or risky. When you control your data pipeline from end to end, you maintain visibility, detection accuracy, and operational resilience even as you transition systems.

Your migration risk goes up when precursor evaluation relies on small or unrepresentative datasets or when evaluation criteria are unclear. According to industry experts, many organizations launch SIEM pilots without predefined benchmarks or comprehensive testing, leading to gaps in coverage, compatibility, or cost that surface only midway through migration.

To help avoid that level of disruption, we’ll be sharing a SIEM Evaluation Checklist for modern enterprises — a practical guide to running a complete and realistic evaluation before you commit to a migration.

Whether you’re moving to the cloud, consolidating tools, or preparing for your first migration in years, pairing a controlled data pipeline with a disciplined evaluation process positions you to lead the migration smoothly, securely, and confidently.

Download our SIEM Migration one-pager for a concise, shareable summary of the workflow, benefits, and key considerations.