For decades, enterprise data infrastructure has been built around systems designed for a slower and more predictable world. CRUD-driven applications, batch ETL processes, and static dashboards shaped how leaders accessed and interpreted information. These systems delivered reports after the fact, relying on humans to query data, build dashboards, analyze results, and take actions.  

Hundreds and thousands of enterprise data decisions were based on this paradigm; but it no longer fits the scale or velocity of modern businesses. Global enterprises now run on an ocean of transactions, telemetry, and signals. Leaders expect decisions to be informed, not next quarter, or even next week – but right now. At the same time, AI is setting the bar for what’s possible: contextual reasoning, proactive detection, and natural language interactions with data.

The question facing every CIO, CTO, CISO, and CEO is simple: Is your enterprise data infrastructure built for AI, or merely patched to survive it?

Defining Modern Enterprise Data Infrastructure

Three design patterns shaped legacy data infrastructure:

• CRUD applications (Create, Read, Update, Delete) as the foundation of enterprise workflows; for this, enterprise data systems would pool data into a store and use tools that executed CRUD operations on this data at rest.

• OLTP vs. OLAP separation, where real-time transactions lived in one system and analysis required exporting it into another

• Data lakes and warehouses are destinations for data, from where queries and dashboards become the interface for humans to extract insights.

These systems have delivered value in their time, but they embedded certain assumptions: data was static, analysis was retrospective, and human-powered querying was the bottleneck for making sense of it. Datasets became the backend, which meant an entire ecosystem of business applications was designed to work on this data as a static repository. But in the age of AI, these systems don’t make sense anymore.

As Satya Nadella, CEO of Microsoft, starkly put it to signal the end of the traditional backend, “business applications … are essentially CRUD databases with a bunch of business logic. All that business logic is moving to ADI agents, which will work across multiple repositories and CRUD operations.”

AI-ready data infrastructure breaks those assumptions. It is:

• Dynamic: Data is structured, enriched, and understood in flight.

• Contextual: Entities, relationships, and relevance are attached before data is stored.

• Governed: Lineage and compliance tagging are applied automatically.

• Conversational: Access is democratized; leaders and teams can interact with data directly, in natural language, without hunting dashboards, building charts, or memorizing query syntax.

The distinction isn’t about speed alone; it’s about intelligence at the foundation.  

Business Impact across Decisions

Why does modernizing legacy data infrastructure matter now? Because AI has shifted expectations. Leaders want time-to-insight measured in seconds, not days.

ERP and CRM

Legacy ERP/CRM systems provided dashboards of what happened. AI-ready data systems can use patterns and data to anticipate what’s likely to occur and explain why. They can cast a wider net and find anomalies and similarities across decades of data, unlike human analysts who are constrained by the dataset they have access to, and querying/computing limitations. AI-ready data systems will be able to surface insights from sales cycles, procurement, or supply chains before they become revenue-impact issues.

Observability

Traditional observability platforms were designed to provide visibility into the health, performance, and behavior of IT systems and applications, but they were limited by the technology of the time in their ability to detect outages and issues when and where they happen. They required manual adjustments to prevent normal data fluctuations from being misinterpreted. AI-ready infrastructure can detect drift, correlate and identify anomalies, and suggest fixes before downtime occurs. 

Security Telemetry

We’ve discussed legacy security systems many times before; they create an unmanageable tidal wave of alerts while being too expensive to manage, and nearly impossible to migrate away from. With the volume of logs and alerts continuing to expand, security teams can no longer rely on manual queries or post-hoc dashboards. AI-ready telemetry transforms raw signals into structured, contextual insights that drive faster, higher-fidelity decisions.

Across all these domains – and the dozens of others that encompass the data universe – the old question of how fast I can query is giving way to a better one: how close to zero can I drive time-to-insight?

Challenges & Common Pitfalls

Enterprises recognize the urgency, and according to a survey, 96% of global organizations have deployed AI models, but they encounter concerns and frustrations while trying to unlock their full potential. According to TechRadar, legacy methods and manual interventions are slowing down AI implementation when the infrastructure relies on time-consuming, error-prone manual steps. These include: –

1. Data Silos and Schema Drift: When multiple systems are connected using legacy pipelines and infrastructure, integrations are fragile, costly, and not AI-friendly. AI compute would be wasted on pulling data together across silos, making AI-powered querying wasteful rather than time-saving. When the data is not parsed and normalized, AI systems have to navigate formats and schemas to understand and analyze the data. Shifts in schema from upstream systems could confound and befuddle AI systems.

2. Dashboard Dependence: Static dashboards and KPIs have been the standard way for enterprises to track the data that matters, but they offer a limited perspective on essential data, limited by time, update frequency, and complexity. Experts were still required to run, update, and interpret these dashboards; and even then, they at best describe what happened, but are unable to adequately point leaders and decision-makers to what matters now.

3. Backend databases with AI overlays: To be analyzed in aggregate, legacy systems required pools of data. Cloud databases, data lakes, data warehouses, etc., became the storage platforms for the enterprise. Compliance, data localization norms, and ad-hoc building have led to enterprises relying on data resting in various silos. Storage platforms are adding AI layers to make querying easier or to stitch data across silos.  
   
   While this is useful, this is retrofitting. Data still enters as raw, unstructured exhaust from legacy pipelines. The AI must work harder, governance is weaker, and provenance is murky. Without structuring for AI at the pipeline level, data storage risks becoming an expensive exercise, as each AI-powered query results in compute to transform raw and unstructured data across silos into helpful information.

4. The Ol’ OLTP vs OLAP divide: For decades, enterprises have separated real-time transactions (OLTP) from analysis (OLAP) because systems couldn’t handle moving and dynamic data and running queries and analytics at the same time. The result? Leaders operate on lagging indicators. It’s like sending someone into a room to count how many people are inside, instead of tracking them as they walk in and out of the door.  

5. AI grafted onto bad data: As our Chief Security and Strategy officer, Preston Wood, said in a recent webinar –  
   “The problem isn’t that you have too much data – it’s that you can’t trust it, align it, or act on it fast enough.”

When AI is added on top of noisy data, poorly-governed pipelines magnify the problem. Instead of surfacing clarity, unstructured data automates confusion. If you expend effort to transform the data at rest with AI, you spend valuable AI compute resources doing so. AI on top of bad data is unreliable, and leaves enterprises second-guessing AI output and wiping out any gains from automation and Gen AI transformation.

These pitfalls illustrate why incremental fixes aren’t enough. AI needs an infrastructure that is designed for it from the ground up.

Solutions and Best Practices

Modernizing requires a shift in how leaders think about data: from passive storage to active, intelligent flow.

1. Treat the pipeline as the control plane.
   Don’t push everything into a lake, a warehouse, or a tool. You can structure, enrich, and normalize the data while it is in motion. You can also segment or drop repetitive and irrelevant data, ensuring that downstream systems consume signal, not noise.

2. Govern in flight.
   When the pipeline is intelligent, data is tagged with lineage, sensitivity, and relevance as it moves. This means you know not just what the data is, but where it came from and why it matters. This vastly improves compliance and governance – and most importantly, builds analytics and analysis-friendly structures, compared to post-facto cataloging.

3. Collapse OLTP and OLAP.
   With AI-ready pipelines, real-time transactions can be analyzed as they happen. You don’t need to shuttle data into a separate OLAP system for insight. The analysis layer lives within the data plane itself. Using the earlier analogy, you track people as they enter the room, not by re-counting periodically. And you also log their height, their weight, the clothes they wear, discern patterns, and prepare for threats instead of reacting to them.

4. Normalize once, reuse everywhere.
   Adopt and use open schemas and common standards so your data is usable across business systems, security platforms, and AI agents without constant rework. Use AI to cut past data silos and create a ready pool of data to put into analytics without needing to architect different systems and dashboards.

5. Conversation as the front door.
   Enable leaders and operators to interact with data through natural language. When the underlying pipeline is AI-powered, the answers are contextual, explainable, and immediate.

This is what separates data with AI features from truly AI-ready data infrastructure.

Telemetry and Security Data

Nowhere are these principles tested more severely than in telemetry. Security and observability teams ingest terabytes of logs, alerts, and metrics every day. Schema drift is constant, volumes are unpredictable, and the cost of delay is measured in breaches and outages.  

Telemetry proves the rule: if you can modernize here, you can modernize everywhere.

This is where DataBahn comes in. Our platform was purpose-built to make telemetry AI-ready:

• Smart Edge & Highway structure, filter, and enrich data in motion, ensuring only relevant, governed signal reaches storage or analysis systems

• Cruz automates data movement and transformation, ensuring AI-ready structured storage and tagging

• Reef transforms telemetry into a contextual insight layer, enabling natural language interaction and agent-driven analytics without queries or dashboards.

In other words, instead of retrofitting AI on top of raw data, DataBahn ensures that your telemetry arrives already structured, contextualized, and explainable. Analytics tools and dashboards can leverage a curated and rich data set; Gen AI tools can be built to make AI accessible and ensure analytics and visualization are a natural language query away.

Conclusion

Enterprise leaders face a choice. Continue patching legacy infrastructure with AI “features” in the hope of achieving AI-powered analytics, or modernize your foundations to be AI-ready and enabled for AI-powered insights.

Modernizing legacy data infrastructure for analytics requires converting raw data into usable and actionable, structured information that cuts across formats, schemas, and destinations. It requires treating pipelines as control planes, governing data in flight, and collapsing the gap between operations and analysis. It means not being focused on creating dashboards, but optimizing time-to-insight – and driving that number towards zero.

Telemetry shows us what’s possible. At DataBahn, we’ve built a foundation to enable enterprises to turn data from liability into their most strategic asset.

Ready to see it in action? Get an audit of your current data infrastructure to assess your readiness to build AI-ready analytics. Experience how our intelligent telemetry pipelines can unlock clarity, control, and competitive advantage.

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The SIEM Cost Spiral Security Leaders Face

Imagine if your email provider charged you for every message sent and received, even the junk, the duplicates, and the endless promotions. That’s effectively how SIEM billing works today. Every log ingested and stored is billed at premium rates, even though only a fraction is truly security relevant. For enterprises, initial license fees might seem manageable or actually give value – but that's before rising data volumes push them into license overages, inflicting punishing cost and budget overruns on already strained SOCs.

SIEM costs can be upwards of a million dollars annually for ingesting their entire volume, leaving analysts spending nearly 30% of their time chasing low-value alerts arising out of rising data volumes. Some SOCs deal with the cost dimension by switching off noisy sources such as firewalls or EDRs/XDRs, but this leaves them vulnerable.

The tension is simple: you cannot stop collecting telemetry without creating blind spots, and you cannot keep paying for every byte without draining the security budget. 

Our team, with decades of cybersecurity experience, has seen that pre-ingestion processing and tiering of data can significantly reduce volumes and save costs, while maintaining and even improving SOC security posture. 

Key Drivers Behind Rising SIEM Costs

SIEM platforms have become indispensable, but their pricing and operating models haven’t kept pace with today’s data realities. Several forces combine to push costs higher year after year:

1. Exploding telemetry growth
Cloud adoption, SaaS proliferation, and IoT/endpoint sprawl have multiplied the volume of security data. Yesterday’s manageable gigabytes quickly become today’s terabytes. 

2. Retention requirements
Regulations and internal policies force enterprises to keep logs for months or even years. Audit teams often require this data to stay in hot tiers, keeping storage costs high. Retrieval from archives adds another layer of expense.

3. Ingestion-based pricing
SIEM costs are still based on how much data you ingest and store. As log sources multiply across cloud, SaaS, IoT, and endpoints, every new gigabyte directly inflates the bill.

4. Low-value and noisy data
Heartbeats, debug traces, duplicates, and verbose fields consume budget without improving detections. Surveys suggest fewer than 40% of logs provide real investigative value, yet every log ingested is billed.

5. Search and rehydration costs
Investigating historical incidents often requires rehydrating archived data or scanning across large datasets. These searches are compute-intensive and can trigger additional fees, catching teams by surprise.

6. Hidden operational overhead
Beyond licensing, costs show up in infrastructure scaling, cross-cloud data movement, and wasted analyst hours chasing false positives. These indirect expenses compound the financial strain on security programs.

Why Traditional Fixes Fall Short

CISOs struggling to balance their budgets know that their SIEM costs add the most to the bill but have limited options to control it. They can tune retention policies, archive older data, or apply filters inside the SIEM. Each approach offers some relief, but none addresses the underlying problem.

Retention tuning
Shortening log retention from twelve months to six may lower license costs, but it creates other risks. Audit teams lose historical context, investigations become harder to complete, and compliance exposure grows. The savings often come at the expense of resilience.

Cold storage archiving
Moving logs out of hot tiers does reduce ingestion costs, but the trade-offs are real. When older data is needed for an investigation or audit, retrieval can be slow and often comes with additional compute or egress charges. What looked like savings up front can quickly be offset later.

Routing noisy sources away
Some teams attempt to save money by diverting particularly noisy telemetry, such as firewalls or DNS, away from the SIEM entirely. While this cuts ingestion, it also creates detection gaps. Critical events buried in that telemetry never reach the SOC, weakening security posture and increasing blind spots.

Native SIEM filters
Filtering noisy logs within the SIEM gives the impression of control, but by that stage the cost has already been incurred. Ingest-first, discard-later approaches simply mean paying premium rates for data you never use.

These measures chip away at SIEM costs but don’t solve the core issue: too much low-value, less-relevant data flows into the SIEM in the first place. Without controlling what enters the pipeline, security leaders are forced into trade-offs between cost, compliance, and visibility.

Data Pipeline Tools: The Missing Middle Layer

All the 'traditional fixes' sacrifice visibility for cost; but the real logical solution is to solve for relevance before ingestion. Not at a source level, and not static like a rule, but dynamically and in real-time. That is where a data pipeline tool comes in.

Data pipeline tools sit between log sources and destinations as an intelligent middle layer. Instead of pushing every event straight into the SIEM, data first passes through a pipeline that can filter, shape, enrich, and route it based on its value to detection, compliance, or investigation.

This model changes the economics of security data. High-value events stream into the SIEM where they drive real-time detections. Logs with lower investigative relevance are moved into low-cost storage, still available for audits or forensics. Sensitive records can be masked or enriched at ingestion to reduce compliance exposure and accelerate investigations.

In this way, data pipeline tools don’t eliminate data; it ensures each log goes to the right place at the right cost. Security leaders maintain full visibility while avoiding premium SIEM rcosts for telemetry that adds little detection value.

How Data Pipeline Tools Deliver SIEM Cost Reduction

Data pipeline tools lower SIEM costs and storage bills by aligning cost with value. Instead of paying premium rates to ingest every log, pipelines ensure each event goes to the right place at the right cost. The impact comes from a few key capabilities:

Pre-ingest filtering
Heartbeat messages, duplicate events, and verbose debug logs are removed before ingestion. Cutting noise at the edge reduces volume without losing investigative coverage.

Smart routing
High-value logs stream into the SIEM for real-time detection, while less relevant telemetry is archived in low-cost, compliant storage. Everything is retained, but only what matters consumes SIEM resources.

Enrichment at collection
Logs are enriched with context — such as user, asset, or location — before reaching the SIEM. This reduces downstream processing costs and accelerates investigations, since fewer raw events can still provide more insight.

Normalization and transformation
Standardizing logs into open schemas reduces parsing overhead, avoids vendor lock-in, and simplifies investigations across multiple tools.

Flexible retention
Critical data remains hot and searchable, while long-tail records are moved into cheaper storage tiers. Compliance is maintained without overspending.

Together, these practices make SIEM cost reduction achievable without sacrificing visibility. Every log is retained, but only the data that truly adds value consumes expensive SIEM resources.

The Business Impact of Modern Data Pipeline Tools

The financial savings from data pipeline tools are immediate, but the strategic impact is more important. Predictable budgets replace unpredictable cost spikes. Security teams regain control over where money is spent, ensuring that value rather than volume drives licensing decisions.

Operations also change. Analysts no longer burn hours triaging low-value alerts or stitching context from raw logs. With cleaner, enriched telemetry, investigations move faster, and teams can focus their energy on meaningful threats instead of noise.

Compliance obligations become easier to meet. Instead of keeping every log in costly hot tiers, organizations retain everything in the right place at the right cost — searchable when required, affordable at scale.

Perhaps most importantly, data pipeline tools create room to maneuver. By decoupling data pipelines from the SIEM itself, enterprises gain the flexibility to change vendors, add destinations, or scale to new environments without starting over. This agility becomes a competitive advantage in a market where security and data platforms evolve rapidly.

In this way, a data pipeline tool are more than a cost-saving measure. It is a foundation for operational resilience and strategic flexibility.

Future-Proofing the SOC with AI-Powered Data Pipeline Tools

Reducing SIEM costs is the immediate outcome of data pipeline tools, but its real value is in preparing security teams for the future. Telemetry will keep expanding, regulations will grow stricter, and AI will become central to detection and response. Without modern pipelines, these pressures only magnify existing challenges.

DataBahn was built with this future in mind. Its components ensure that security data isn’t just cheaper to manage, but structured, contextual, and ready for both human analysts and machine intelligence.

• Smart Edge acts as the collection layer, supporting both agent and agentless methods depending on the environment. This flexibility means enterprises can capture telemetry across cloud, on-prem, and OT systems without the sprawl of multiple collectors.
• Highway processes and routes data in motion, applying enrichment and normalization so downstream systems — SIEMs, data lakes, or storage — receive logs in the right format with the right context.
• Cruz automates data movement and transformation, tagging logs and ensuring they arrive in structured formats. For security teams, this means schema drift is managed seamlessly and AI systems receive consistent inputs without manual intervention.
• Reef, a contextual insight layer, turns telemetry into data that can be queried in natural language or analyzed by AI agents. This accelerates investigations and reduces reliance on dashboards or complex queries.

Together, these capabilities move security operations beyond cost control. They give enterprises the agility to scale, adopt AI, and stay compliant without being locked into a single tool or architecture. In this sense, a data pipeline management tool is not just about cutting SIEM costs; it’s about building an SOC that’s resilient and future-ready.

Cut SIEM Costs, Keep Visibility

For too long, security leaders have faced a frustrating paradox: cut SIEM ingestion to control costs and risk blind spots, or keep everything and pay rising bills to preserve visibility.

Data pipeline tools eliminate that trade-off by moving decisions upstream. You still collect every log, but relevance is decided before ingestion: high-value events flow into the SIEM, the rest land in low-cost, compliant stores. The same normalization and enrichment that lower licensing and storage also produce structured, contextual telemetry that speeds investigations and readies the SOC for AI-driven workflows. The outcome is simple: predictable spend, full visibility, and a pipeline built for what’s next.

The takeaway is clear: SIEM cost reduction and complete visibility are no longer at odds. With a data pipeline management tool, you can achieve both.

Ready to see how? Book a personalized demo with DataBahn and start reducing SIEM and storage costs without compromise.

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.