Enterprise leaders are racing to capture the promise of Generative AI. The vision is compelling: security teams that respond in seconds, IT operations that optimize themselves, executives who can query enterprise performance in natural language. Yet for all the hype, reality is sobering.

MIT research shows that 95% of enterprise AI projects fail. The 5% that succeed share one trait: they don’t bolt GenAI onto legacy systems; they build on infrastructure that was designed for AI from the ground up. OpenAI recently launched its Forward Deployed Engineer (FDE) program for precisely this reason while acknowledging that enterprise AI adoption has become bottlenecked not by imagination, but by architecture.

For CISOs, CIOs, CTOs, and CEOs,  this is no longer just about experimentation. It’s about whether your enterprise AI adoption strategy will scale securely, reduce operational risk, and deliver competitive advantage.

What is AI-native infrastructure?

“AI-native” is more than a buzzword. It represents a decisive break from retrofitting existing tools and processes to accommodate the generative AI enterprise transformation.

AI-native infrastructure is built to anticipate the needs of machine intelligence, not adapt to them later. Key characteristics include:

• AI-ready structured data stores → optimized for training, reasoning, and multi-modal input.
• AI-first protocols like Model Context Protocol (MCP) → enabling AI agents to safely and seamlessly connect with enterprise systems.
• Semantic layers and context-rich data fabrics → ensuring that data is enriched, normalized, and explainable for both humans and machines.
• Agentic AI operations → autonomous systems that can parse, repair, and optimize data pipelines in real time.
• Headless architectures → decoupling data from applications to prevent tool lock-in and accelerate interoperability.

Contrast this with legacy stacks: rigid schemas, siloed tools, proprietary formats, and brittle integrations. These were designed for dashboards and humans – not reasoning engines and autonomous agents. AI-native infrastructure, by design, makes AI a first-class citizen of the enterprise technology stack.

The impact of GenAI failure in enterprises

The promise of the GenAI enterprise transformation is breathtaking: instant responsiveness, autonomous insight, and transformative workflows. But in too many enterprises, the reality is wasted effort, hallucinated outputs, operational risks, and even new security threats.

Wasted Time & Effort, with Little ROI

Despite billions of dollars in investment, generative AI has failed to deliver meaningful business outcomes for most organizations. The MIT study cited poor integration, unrealistic expectations, and a lack of industry-specific adaptation as the reason for 95% of enterprise AI projects are failing. You end up with pilots, not platforms - costs spiral, momentum stalls, and leaders grow skeptical.

Hallucinations, Errors, & Reputational Damage

GenAI systems often generate outputs that are plausible but wrong. Deloitte warns that hallucinations can lead to faulty decisions, regulatory penalties, and public embarrassment. Inaccuracy isn’t just an annoyance – it’s a business liability.

Security & Compliance Risks

Generative AI increases cyber vulnerability in unexpected ways:

• Deepfakes and phishing → impersonating leaders to trick employees.
• Malicious prompt manipulation → steering AI to disclose sensitive data.
• System vulnerabilities → adversarial prompts that can inject malicious code into enterprise workflows.
• Shadow AI & Governance Blind Spots

When organizations rush into generative AI without governance, “shadow AI” proliferates – teams adopt AI tools without oversight, risking data exposure and non-compliance. PwC underscores that GenAI amplifies threats related to privacy, compliance, intellectual property, and legal risk, reinforcing the need for trust-by-design, not just speed.

AI Arms Race – Defenders Can’t Keep Up

Cybercriminals are adopting GenAI just as quickly, if not faster. Security leaders report they can’t match the pace of AI-powered adversaries. The risk isn’t just hallucination – it’s being outpaced in an escalating AI arms race.

Without a foundation built for AI – one that guards against hallucination, ensures governance, secures against manipulation, and embeds human-in-the-loop oversight –Generative AI becomes not a driver of transformation, but a vector of failure.

Why are SOCs struggling to harness the potential for Generative AI  

A few systemic traps in cybersecurity and telemetry ecosystems:

• The Legacy Retrofit Problem
  Duct-taping GenAI onto SIEMs, CRMs, or observability platforms built for human dashboards doesn’t work. These systems weren’t built for autonomous reasoning, and they choke on unstructured, noisy, or redundant data.

• Data Chaos and Schema Drift
  AI can’t learn from broken pipelines. Unpredictable data flows, ungoverned enrichment, and constant schema drift undermine trust. The result: hallucinations, blind spots, and brittle AI outputs.

• The DIY Trap
  Some enterprises try to build AI-ready infra in-house. Research shows this approach rarely scales: the talent is scarce, the maintenance overhead crippling, and the results fragile. Specialized vendors succeed where DIY fails.

• Cost Explosion
  When data isn’t filtered, tiered, and governed before it reaches AI models, compute and storage bills spiral. Enterprises pay to move and process irrelevant data, burning millions without value.

AI can’t thrive on yesterday’s plumbing. Without AI-native foundations, every GenAI investment risks becoming another line item in the 95% failure statistic.

Principles and Best Practices for AI-native infrastructure

So what does it take to build for the 5% that succeed? Forward-looking enterprises are coalescing around four principles:

1. AI-Ready Data
   Structured, normalized, enriched, and explainable. AI outputs are only as good as the inputs; noisy or incomplete data guarantees failure.
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2. Interoperability and Open Protocols
   Embrace standards like MCP, APIs, and headless designs to prevent lock-in and empower agents to operate across the stack.

3. Autonomous Operations
   Agentic AI systems can parse new data sources, repair schema drift, track telemetry health, and quarantine sensitive information – automatically.

4. Future-Proof Scalability
   Design for multi-modal AI: text, logs, video, OT telemetry. Tomorrow’s AI won’t just parse emails; it will correlate camera feeds with log data and IoT metrics to detect threats and inefficiencies.

External research reinforces this: AI models perform disproportionately better when trained on high-quality, AI-ready data. In fact, data readiness is a stronger predictor of success than model selection itself.

The lesson: enterprises must treat AI-native infrastructure as the strategic layer beneath every GenAI investment.

Why we built DataBahn this way

At DataBahn, we saw this shift coming. That’s why our platform was not adapted from observability tools or legacy log shippers – it was built AI-native from day one.

We believe the AI-powered SOC of the future will depend on infrastructure that can collect, enrich, orchestrate, and optimize telemetry for AI, not just for humans. We designed our products to be the beating heart of that transformation: a foundation where agentic AI can thrive, where enterprises can move from reactive dashboards to proactive, AI-driven operations.

This isn’t about selling tools. It’s about ensuring enterprises don’t fall into the 95% that fail.

The question every CXO must answer

Generative AI isn’t waiting. Your competitors are already experimenting, learning, and building AI-native foundations. The real question is no longer if GenAI will transform your enterprise, but whether your infrastructure will allow you to keep pace.

Legacy plumbing won’t carry you into the AI era. AI-native infrastructure isn’t a luxury; it’s table stakes for survival in the coming decade.

For CXOs, the call to action is clear: audit your foundations, re-architect for AI, and choose partners who can help you move fast without compromise.

At DataBahn, we’re looking forward to powering this future.

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!

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.