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!

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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