The latest articles, news, blogs and learnings from Databahn
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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.
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.
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.
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.
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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.
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.
These outcomes are not just migration wins, they set up your SOC for long-term agility in a fast-changing security technology landscape.
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.
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The air outside is a wall of heat. Inside, the Mandalay Bay convention floor hums with thousands of conversations, security researchers debating zero-days, vendors unveiling new tools, and old friends spotting each other across the crowd. It’s loud, chaotic, and absolutely electric!
For us at Databahn, Black Hat isn’t just a showcase of cutting-edge research and product launches. It’s where the heartbeat of the cybersecurity community comes alive in the handshakes, the hallway chats, and the unexpected reunions that remind us why we’re here in the first place.
From security engineers and researchers to marketers, event organizers, and community builders, every role plays a part in making this event what it is. Like RSAC, Black Hat feels less like a trade show and more like a reunion, one where we share new ideas, catch up with longtime peers, and recognize the often-unsung contributors who quietly keep the cybersecurity world moving.
While the people are the soul of Black Hat, the conversations this year reflected a major shift in technology priorities: the role of telemetry in the AI era.
AI, autonomous agents, and APIs are transforming security operations faster than ever before. But their effectiveness hinges on the quality of the data they consume. Modern detection, response, analytics, and compliance workflows all depend on telemetry that is:
We took this challenge head-on at Black Hat with the launch of our Smart Agent: a lightweight, programmable collection layer designed to bring policy, precision, and platform awareness to endpoint telemetry.
Think of it as a precision instrument for your security data that turns telemetry from a bottleneck into a force multiplier.
For years, the industry has debated: agent or agentless? At Databahn.ai, we see this as a false binary. Real-world environments require both approaches, deployed intelligently based on:
The Smart Agent gives security teams that flexibility without forcing trade-offs. Learn more about our approach here.
As we push the envelope in telemetry, our goal remains the same: build the platform that enables people to do their best work. Because in cybersecurity, the human element isn’t just important; it’s irreplaceable. If you missed us at Black Hat, let’s talk about how the Smart Agents can help your team cut data waste, improve precision, and stay ahead of evolving threats.
This blog is based on a CXO Insight Series conversation between Preston Wood and Aditya Sundararam on LinkedIn Live. Watch the full episode here.
In today’s cybersecurity landscape, it’s no longer enough to ingest more logs. CISOs face deeper, more systemic challenges as the foundational architecture of the modern enterprise SOC relies on antiquated SIEMs, siloed data landscapes, and brittle data pipelines which have reached their limit.
The main takeaway? If CISOs don’t rethink their approach to telemetry and pipelines, they’ll continue to fall behind. Not because they lack the tools, but because data strategies don’t work on a broken data foundation.
Preston opened the session by recounting a familiar story for many security leaders: SOCs using SIEMs that are drowning in irrelevant data, security analysts overwhelmed by a noisy tsunami of alerts, and struggling to investigate and manage their security posture effectively.
“You’ve got a 24/7 SOC and a dozen tools throwing off logs, but your team is still asking the same questions: what’s actually going on here?”
Preston
The issue isn’t visibility, it’s clarity. As Preston noted, enterprise SOCs don’t just suffer from managing volume; they suffer from lack of trust in their data. When logs are duplicated, out of order, lack context, and come in formats that were invented long after the tools meant to make sense of them, analysts spend more time normalizing and querying than detecting and responding.
The session detailed serious limitations of the SIEM-centric model:
“When your security ROI is gated by how many terabytes you can afford to ingest, you’re already behind.”
Preston
Preston argued that SIEMs still have a role - but not as the data movement engine. That role now belongs to something else: the security data pipeline.
For security leaders wrestling with log sprawl, cloud complexity, and regulatory risk, the answer isn’t going to come from continuing to overload the SIEM; that has led to overwhelming telemetry sprawl, increasingly fragmented environment, and mounting telemetry pressure. To elevate enterprise SOC operations, security leaders should focus on treating the data pipeline as the part of their security architecture that can unlock the future for them.
The shift means rethinking how data flows through the stack. Instead of sending everything to the SIEM and dealing with the noise later, security teams should be routing and filtering telemetry at the edge, well before it even reaches their analytics tools. Enrichment can happen upstream as well, happening left-of-SIEM and ensuring contextual signals from the enterprise environment, and reducing dependency on external threat feeds. Normalization can occur just once as the data is collected and aggregated, ideally using open schemas like OCSF, so that data can be reused for different use cases – detection, investigation, and compliance. By placing intelligent data pipelines before the SIEM, teams can significantly cut egress, compute, and SIEM licensing costs while improving the signal-to-noise ratio.
Ultimately, this isn’t about adding a new tool into an already complex toolchain. It’s about building an intelligent and foundational data fabric layer which understands the environment, aligns with business and risk priorities, and prepares the organization for an AI-driven future. This is essential for SOCs looking to lean into AI use cases, because without AI-ready data, security tools leveraging AI are just window dressing.
Preston warned his fellow CISOs about how most security vendors are racing to bolt AI onto their dashboards to leverage the current hype cycle. True AI-driven security begins at the pipeline layer, which delivers structured, enriched, clean telemetry that is collected and governed in real-time. This is the input LLM or reasoning engines can build on for future SOC use cases.
DataBahn’s platform was purpose-built for this future: using Agentic AI to automate parsing, schema detection, enrichment, and routing. With products like Cruz and Reef operating as intelligent assistants embedded in the data plane - leveraging Agentic AI to learn, adopt, evolve, and grow into helping security teams - security decision makers can begin to empower their teams away from the manual drudgery of managing data movement and focus them on strategic goals. Agentic AI pipelines also create a foundational data layer to ensure that your AI-powered security tools are equipped with the data, context, policy, and understanding required to deliver value.
"Agentic AI doesn't start in the UI. It starts with the data fabric. It starts with being able to reason over telemetry that actually makes sense."
Preston
The session closed with a call to action for CISOs navigating their next big data decision: whether that’s SIEM migration, XDR adoption, or cloud expansion.
The future belongs to organizations that control their telemetry, set up a streamlined data fabric, and prepare their stack for AI – not just in theory, and not for tomorrow, but put into practice today.
This blog is based on a CXO Insight Series conversation between Preston Wood and Aditya Sundararam on LinkedIn Live. Watch the full episode here.
Why is DataBahn building agents? Why now?
Agents are not new. But the problem they were created to solve has evolved. What’s changed is not just the technology landscape, but the role of telemetry in powering modern detection, response, AI analytics, and compliance.
Most endpoint agents were designed for a narrow task: collect logs, ship them somewhere, and stay out of the way. But today’s security pipelines demand more. They need selective, low-latency, structured data that feeds not just a SIEM, but an entire ecosystem, from detection engines and data lakes to streaming analytics and AI models.
Our mission has always been to eliminate data waste and simplify how enterprises move, manage, and monitor security data. That’s why we built the Smart Agent: a lightweight, programmable collection layer that brings policy, precision, and platform awareness to endpoint telemetry – without the sprawl, bloat, and hidden costs of traditional agents.
Traditional agents are often built as isolated tools – one for log forwarding, another for EDR, a third for metrics. This results in resource contention, redundant data, and operational sprawl.
DataBahn's Smart Agent takes a fundamentally different approach. It’s built as a platform-native component, not a point solution. That means collect once from the endpoint, normalize once, and route anywhere, breaking the cycle of duplication.
Here’s what sets it apart:
- Modular, Policy-Driven Control: Enterprise teams can now define exactly what to collect, how to filter or enrich it, and where to send it – with full version control, change monitoring, and audit trails.
- Performance Without Sprawl: Replace 3–5 overlapping agents per endpoint with a single lightweight Smart Edge agent that serves security, observability, and compliance workflows simultaneously.
- Built for High-Value Telemetry: Our agents are optimized to selectively capture only high-signal events, reducing compute strain and downstream ingestion costs.
- AI-Ready, Future-Proof Architecture: These agents are telemetry-aware and natively integrated into our AI pipeline. Whether it’s streaming inference, schema awareness, or tagging sensitive data for compliance – they’re ready for the next generation of intelligent data pipelines.
This isn’t just about replacing old agents. It’s about rethinking the endpoint as the first intelligent node in your data pipeline.
We’ve spent years embedded in complex environments – from highly regulated banks to fast-moving cloud-native tech firms. And across the board, one pattern kept surfacing: traditional approaches to endpoint telemetry don’t scale.
What that enables:
The conversation around data collection has been stuck in a binary: agent or agentless. But in real-world environments, that framing doesn’t hold.
What enterprises need isn’t one or the other but the ability to deploy the right mechanism based on asset type, risk, latency sensitivity, and the downstream use case.
The future isn’t agent or agentless – it’s context-aware, modular, and unified. Data collection that adapts to where it’s running, integrates cleanly into existing pipelines, and remains extensible for what comes next, whether that’s AI-driven security operations, privacy-focused compliance, or cross-cloud observability.
That’s the shift we’re enabling with the DataBahn Smart Agent. Not just a product – but a programmable foundation for secure, scalable, and future-ready telemetry.
In their article about how banks can extract value from a new generation of AI technology, notable strategy and management consulting firm McKinsey identified AI-enabled data pipelines as an essential part of the ‘Core Technology and Data Layer’. They found this infrastructure to be necessary for AI transformation, as an important intermediary step in the evolution banks and financial institutions will have to make for them to see tangible results from their investments in AI.
The technology stack for the AI-powered banking of the future relies greatly on an increased focus on managing enterprise data better. McKinsey’s Financial Services Practice forecasts that with these tools, banks will have the capacity to harness AI and “… become more intelligent, efficient, and better able to achieve stronger financial performance.”
The authors point to increased adoption of AI across industries and organizations, but the depth of the adoption remains low and experimental. They express their vision of an AI-first bank, which -
They recommend that banks and financial service enterprises set a bold vision for AI-powered transformation, and root the transformation in business value.
The true potential of AI will require banks of the future to tread beyond just AI models, the authors claim. With embedding AI into four capability layers as the goal, they identify ‘data and core tech’ as one of those four critical components. They have augmented an earlier AI capability stack, specifically adding data preprocessing, vector databases, and data post-processing to create an ‘enterprise data’ part of the ‘core technology and data layer’. They indicate that this layer would build a data-driven foundation for multiple AI agents to deliver customer engagement and enable AI-powered decision-making across various facets of a bank’s functioning.
Data quality is the single greatest predictor of LLM effectiveness today, and our current generation of AI tools are fundamentally wired to convert large volumes of data into patterns, insights, and intelligence. We believe the true value of enterprise AI lies in depth, where Agentic AI modules can speak and interact with each other while automating repetitive tasks and completing specific and niche workstreams and workflows. This is only possible when the AI modules have access to purposeful, meaningful, and contextual data to rely on.
We are already working with multiple banks and financial services institutions to enable data processing (pre and post), and our Cruz and Reef products are deployed in many financial institutions to become the backbone of their transformation into AI-first organizations.
Are you curious to see how you can come closer to building the data infrastructure of the future? Set up a call with our experts to see what’s possible when data is managed with intelligence.
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Two years ago, our DataBahn journey began with a simple yet urgent realization: security data management is fundamentally flawed. Enterprises are overwhelmed by security and telemetry, struggling to collect, store, and process it, while finding it harder and harder to gain timely insights from it. As leaders and practitioners in cybersecurity, data engineering, and data infrastructure, we saw this pattern everywhere: spiraling SIEM costs, tool sprawl, noisy data, tech debt, brittle pipelines, and AI initiatives blocked by legacy systems and architectures.
We founded DataBahn to break this cycle. Our platform is specifically designed to help enterprises regain control: disconnecting data pipelines from outdated tools, applying AI to automate data engineering, and constructing systems that empower security, data, and IT teams. We believe data infrastructure should be dynamic, resilient, and scalable, and we are creating systems that leverage these core principles to enhance efficiency, insight, and reliability.
Today, we’re announcing a significant milestone in this journey: a $17M Series A funding round led by Forgepoint Capital, with participation from S3 Ventures and returning investor GTM Capital. Since coming out of stealth, our trajectory has been remarkable – we’ve secured a Fortune 10 customer and have already helped several Fortune 500 and Global 200 companies cut over 50% of their telemetry processing costs and automate most of their data engineering workloads. We're excited by this opportunity to partner with these incredible customers and investors to reimagine how telemetry data is managed.
As operators, consultants, and builders, we worked with and interacted with CISOs across continents who complained about how they had gone from managing gigabytes of data every month to being drowned by terabytes of data daily, while using the same pipelines as before. Layers and levels of complexity were added by proprietary formats, growing disparity in sources and devices, and an evolving threat landscape. With the advent of Generative AI, CISOs and CIOs found themselves facing an incredible opportunity wrapped in an existential threat, and without the right tools to prepare for it.
DataBahn is setting a new benchmark for how modern enterprises and their CISO/CIOs can manage and operationalize their telemetry across security, observability, and IOT/OT systems and AI ecosystems. Built on a revolutionary AI-driven architecture, DataBahn parses, enriches, and suppresses noise at scale, all while minimizing egress costs. This is the approach our current customers are excited about, because it addresses key pain points they have been unable to solve with other solutions.
Our two new Agentic AI products are solving problems for enterprise data engineering and analytics teams. Cruz automates complex data engineering tasks from log discovery, pipeline creation, tracking and maintaining telemetry health, to providing insights on data quality. Reef surfaces context-aware and enriched insights from streaming telemetry data, turning hours of complex querying across silos into seconds of natural-language queries.
We’re incredibly grateful to our early customers; their trust, feedback, and high expectations have shaped who we are. Their belief drives us every day to deliver meaningful outcomes. We’re not just solving problems with them, we’re building long-term partnerships to help enterprise security and IT teams take control of their data, and design systems that are flexible, resilient, and built to last. There’s more to do, and we’re excited to keep building together.
We’re also deeply thankful for the guidance and belief of our advisors, and now our investors. Their support has not only helped us get here but also sharpened our understanding of the opportunity ahead. Ernie, Aaron, and Saqib’s support has made this moment more meaningful than the funding; it’s the shared conviction that the way enterprises manage and use data must fundamentally change. Their backing gives us the momentum tomove faster, and the guidance to keep building towards that mission.
Above all, we want to thank our team. Your passion, resilience, and belief in what we’re building together are what got us here. Every challenge you’ve tackled, every idea you’ve contributed, every late night and early morning has laid the foundation for what we have done so far and for what comes next. We’re excited about this next chapter and are grateful to have been on this journey with all of you.
The complexity of enterprise data management is growing exponentially. But we believe that with the right foundation, enterprises can turn that complexity into clarity, efficiency, and competitive advantage.
If you’re facing challenges with your security or observability data, and you’re ready to make your data work smarter for AI, we’d love to show you what DataBahn can do. Request a demo and see how we can help.
Onwards and upwards!
Nanda and Nithya
Cofounders, DataBahn
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In September 2022, cybercriminals accessed, encrypted, and stole a substantial amount of data from Suffolk County’s IT systems, which included personally identifiable information (PII) of county residents, employees, and retirees. Although Suffolk County did not pay the ransom demand of $2.5 million, it ultimately spent $25 million to address and remediate the impact of the attack.
Members of the county’s IT team reported receiving hundreds of alerts every day in the weeks leading up to the attack. Several months earlier, frustrated by the excessive number of unnecessary alerts, the team redirected the notifications from their tools to a Slack channel. Although the frequency and severity of the alerts increased leading up to the September breach, the constant stream of alerts wore the small team down, leaving them too exhausted to respond and distinguish false positives from relevant alerts. This situation created an opportunity for malicious actors to successfully circumvent security systems.
Today, cybersecurity teams are continually bombarded by alerts from security tools throughout the data lifecycle. Firewalls, XDRs/EDRs, and SIEMs are among the common tools that trigger these alerts. In 2020, Forrester reported that SOC teams received 11,000 alerts daily, and 55% of cloud security professionals admitted to missing critical alerts. Organizations cannot afford to ignore a single alert, yet alert fatigue (and an overwhelming number of unnecessary alerts) causes SOCs to miss up to 30% of security alerts that go uninvestigated or are completely overlooked.
While this creates a clear cybersecurity and business continuity problem, it also presents a pressing human issue. Alert fatigue leads to cognitive overload, emotional exhaustion, and disengagement, resulting in stress, mental health concerns, and attrition. More than half of cybersecurity professionals cite their workload as the primary source of stress, two-thirds reported experiencing burnout, and over 60% of cybersecurity professionals surveyed stated it contributed to staff turnover and talent loss.
Alert fatigue poses operational challenges, represents a critical security risk, and truly becomes an adversary of the most vital resource that enterprises rely on for their security — SOC professionals doing their utmost to combat cybercriminals. SOCs are spending so much time and effort triaging alerts and filtering false positives that there’s little room for creative threat hunting.
Alert fatigue is a result, not a root cause. When these security tools were initially developed, cybersecurity teams managed gigabytes of data each month from a limited number of computers on physically connected sites. Today, Security Operations Centers (SOCs) are tasked with handling security data from thousands of sources and devices worldwide, which arrive through numerous distinct devices in various formats. The developers of these devices did not intend to simplify the lives of security teams, and the tools they designed to identify patterns often resemble a fire alarm in a volcano. The more data that is sent as an input to these machines, the more likely they are to malfunction – further exhausting and overwhelming already stretched security teams.
Well-intentioned leaders advocate for improved triaging, the use of automation, refined rules to reduce false-positive rates, and the application of popular technologies like AI and ML. Until we can stop security tools from being overwhelmed by large volumes of unstructured, unrefined, and chaotic data from diverse sources and formats, these fixes will be band aids on a gaping wound.
The best way to address alert fatigue is to filter out the data being ingested into downstream security tools. Consolidate, correlate, parse, and normalize data before it enters your SIEM or UEBA. If it isn’t necessary, store it in blob storage. If it’s duplicated or irrelevant, discard it. Don’t clutter your SIEM with poor data so it doesn’t overwhelm your SOC with alerts no one requested.
At DataBahn, we help enterprises cut through cybersecurity noise with our security data pipeline solution, which works around the clock to:
1. Aggregates and normalizes data across tools and environments automatically
2. Uses AI-driven correlation and prioritization
3. Denoises the data going into the SIEM, ensuring more actionable alerts with full context
SOCs using DataBahn aren’t overwhelmed with alerts; they only see what’s relevant, allowing them to respond more quickly and effectively to threats. They are empowered to take a more strategic approach in managing operations, as their time isn’t wasted triaging and filtering out unnecessary alerts.
Organizations looking to safeguard their systems – and protect their SOC members – should shift from raw alert processing to smarter alert management, driven by an intelligent pipeline which combines automation, correlation, and transformation that filters out the noise and combats alert fatigue.
Interested in saving your SOC from alert fatigue? Contact DataBahn
In the past, we've written about how we solve this problem for Sentinel. You can read more here: AI-powered Sentinel Log Optimization
I see a lot more organizations head towards Headless Cyber Architecture. Traditionally, cybersecurity teams relied on one massive tool: the SIEM. For years, Cyber security orgs funneled all their cyber data into it; not because it was optimal, but because it was the compliance checkbox.
That’s how SIEMs earned their core seat at the table. Over time, SIEMs evolved from a log aggregator into something more sophisticated: UEBA ->Security Analytics -> and now, increasingly, SaaS-based platforms to more AI SOC. But there’s a catch—in this model, you don’t truly own your data. It lives in the vendor’s ecosystem, locked into their proprietary format rather than an open standard.
You end up paying for storage, analytics, and access to your own telemetry—creating a cycle of dependency and vendor lock-in.
SIEMs are not going away; they remain mission-critical. But they’re no longer the sole destination for all cyber data. Instead, they are being refocused: They now consume only Security-Relevant Data (SRDs)—purposefully curated feeds for advanced threat detection, correlation, and threat chaining. Nearly 80% of organizations have only integrated baseline telemetry—firewalls, endpoints, XDRs, and the like. But where’s the visibility into mission-critical apps? Your plant data? Manufacturing systems? The rest of your telemetry often remains siloed, unparsed, and not in open, interoperable formats like OTEL or OCSF.
The shift is this : It’s now flowing into your Security Data Lake (SDL)—parsed, normalized, enriched with context like threat intel, HR systems, identity, and geo signals. This data increasingly lives in your environment: Databricks. Snowflake. Amazon Web Services (AWS), Microsoft Azure, Google Cloud, Hydrolix.
With this shift, a new category is exploding: headless cybersecurity products—tools that sit on top of your data rather than ingesting it.
· Headless SIEMs: Built for detection, not data hoarding.
· Headless Vulnerability Analytics: Operating directly on vuln data inside your SDL.
· Headless Data Science: ML models run atop your lake, no extraction needed.
· Soon: Headless IAM & Access Analytics: Compliance and reporting directly from where access logs reside.
These solutions don’t route your data out—they bring their algorithm to your lake. This flips the control model.
What’s needed is an independent platform purpose-built for streaming ETL and pipeline management, the connective tissue that moves, filters, and enriches your telemetry in real time. A platform that’s-
· Lightweight and modular—drop a node anywhere to start collecting from a new business unit or acquisition.
· Broadly integrated—connecting with thousands of systems to maximize visibility.
· Smart at filtering—removing up to 60%-80% of Non-Security Data (NSDs) that bloats your SIEM
· Enrichment-first—applying threat intel, identity, geo, and other contextual data before forwarding to your Security Data Lake (SDL) and SIEM. Remember, analysts spend valuable time manually stitching together context during investigations. Pre-enriched data dramatically reduces that effort—cutting investigation time, improving accuracy, and accelerating response.
· AI-ready—feeding clean, contextualized data into your models, reducing noise and improving MTTD/MTTR. Also helps desanitize sensitive information leaving your environment.
· Insightful in motion—offering real-time observability as data flows through the pipeline.
In short, the pipeline becomes the foundation for modern security architecture and the fuel for your AI-driven transformation.
With this shift, a new category is exploding: headless cybersecurity products—tools that sit on top of your data rather than ingesting it.
· SIEMs do less ingestion, more detection
· Data lakes become the source of truth, enriched, stored in your format
· Vendors no longer take your data—they work on top of it
· Security teams get flexibility, visibility, and control—without the lock-in
This is the rise of modular, headless cybersecurity—
This is the rise of modular, headless cybersecurity, where your data stays yours. Their analytics run where you want and computing happens on your terms, all while you have complete control over your data.
As security operations become more complex and SOCs face increasingly sophisticated threats, the data layer has emerged as the critical foundation. SOC effectiveness now depends on the quality, relevance, and timeliness of data it processes; without a robust data layer, SIEM-based analytics, detection, and response automation crumble under the deluge of irrelevant data and unreliable insights.
Recognizing the need to engage with current SIEM problems, security leaders are adopting a new breed of security data tools known as Security Data Pipeline Platforms. These platforms sit beneath the SIEM, acting as a control plane for ingesting, enriching, and routing security data in real time. In its 2025 Market Guide, SACR explores this fast-emerging category and names DataBahn among the vendors leading this shift.
The SACR report highlights this breaking point: organizations typically collect data from 40+ security tools, generating terabytes daily. This volume overwhelms legacy systems, creating three critical problems:
First, prohibitive costs force painful tradeoffs between security visibility and budget constraints. Second, analytics performance degrades as data volumes increase. Finally, security teams waste precious time managing infrastructure rather than investigating threats.
Fundamentally, security data pipeline platforms partially or fully resolve the data volume problems with differing outcomes and performance. DataBahn decouples collection from storage and analysis, automates and simplifies data collection, transformation, and routing. This architecture reduces costs while improving visibility and analytic capabilities—the exact opposite of the traditional, SIEM-based approach.
The report examines how AI is reshaping security data pipelines. While many vendors claim AI capabilities, few have integrated intelligence throughout the entire data lifecycle.
DataBahn's approach embeds intelligence at every layer of the security data pipeline. Rather than simply automating existing processes, our AI continually optimizes the entire data journey—from collection to transformation to insight generation.
This intelligence layer represents a paradigm shift from reactive to proactive security, moving beyond "what happened?" to answering "what's happening now, and what should I do about it?"
Take threat detection as an example: traditional systems require analysts to create detection rules based on known patterns. DataBahn's AI continually learns from your environment, identifying anomalies and potential threats without predefined rules.
In an era where security data is both abundant and complex, DataBahn's platform stands out by offering intelligent, adaptable solutions that cater to the evolving needs of security teams.
Agentic AI for Security Data Engineering: Our agentic AI, Cruz, automates the heavy lifting across your data pipeline—from building connectors to orchestrating transformations. Its self-healing capabilities detect and resolve pipeline issues in real-time, minimizing downtime and maintaining operational efficiency.
Intelligent Data Routing and Cost Optimization: The platform evaluates telemetry data in real-time, directing only high-value data to cost-intensive destinations like SIEMs or data lakes. This targeted approach reduces storage and processing costs while preserving essential security insights.
Flexible SIEM Integration and Migration: DataBahn's decoupled architecture facilitates seamless integration with various SIEM solutions. This flexibility allows organizations to migrate between SIEM platforms without disrupting existing workflows or compromising data integrity.
Enterprise-Wide Coverage: Security, Observability, and IoT/OT: Beyond security data, DataBahn's platform supports observability, application, and IoT/OT telemetry, providing a unified solution for diverse data sources. With 400+ prebuilt connectors and a modular architecture, it meets the needs of global enterprises managing hybrid, cloud-native, and edge environments.
One of DataBahn’s standout features highlighted by SACR is our newly launched "insights layer”—Reef. Reef transforms how security professionals interact with data through conversational AI. Instead of writing complex queries or building dashboards, analysts simply ask questions in natural language: "Show me failed login attempts for privileged users in the last 24 hours" or "Show me all suspicious logins in the last 7 days"
Critically, Reef decouples insight generation from traditional ingestion models, allowing Security analysts to interact directly with their data, gain context-rich insights without cumbersome queries or manual analysis. This significantly reduces the mean time to detection (MTTD) and response (MTTR), allowing teams to prioritize genuine threats quickly.
DataBahn's inclusion in the SACR 2025 Market Guide affirms our position at the forefront of security data innovation. As threat environments grow more complex, the difference between security success and failure increasingly depends on how effectively organizations manage their data.
We invite you to download the complete SACR 2025 Market Guide to understand how security data pipeline platforms are reshaping the industry landscape. For a personalized discussion about transforming your security operations, schedule a demo with our team. Click here
In today's environment, your security data should work for you, not against you.
For effective data-driven decision-making, decision-makers must access accurate and relevant data at the right time. Security, sales, manufacturing, resource, inventory, supply chain, and other business-critical data help inform critical decisions. Today’s enterprises need to aggregate relevant data from around the world and various systems into a single location for analysis and presentation to leaders in a digestible format in real time for them to make these decisions effectively.
Today, businesses of all sizes need to collect information from various sources to ensure smooth operations. For instance, a modern retail brand must gather sales data from multiple storefronts across different locations, its website, and third-party sellers like e-commerce and social media platforms to understand how their products performed. It also helps inform decisions such as inventory, stocking, pricing, and marketing.
For large multi-national commercial enterprises, this data and its importance get magnified. Executives have to make critical decisions with millions of dollars at stake and in an accelerated timeline. They also have more complex and sophisticated systems with different applications and digital infrastructures that generate large amounts of data. Both old and new-age companies must build elaborate systems to connect, collect, aggregate, make sense of, and derive insights from this data.
Telemetry data encompasses various types of information captured and collected from remote and hard-to-reach sources. The term ‘telemetry’ originates from the French word ‘télémètre’, which means a device for measuring (“mètre”) data from afar (“télé”). In the context of modern enterprise businesses, telemetry data includes application logs, events, metrics, and performance indicators which provide essential information that helps run, maintain, and optimize systems and operations.
A telemetry pipeline, as the name implies, is the infrastructure that collects and moves the data from the source to the destination. But a telemetry data pipeline doesn’t just move data; it also aggregates and processes this data to make it usable, and routes it to the necessary analytics or security destinations where it can be used by leaders to make important decisions.
Telemetry data pipelines have 3 core functions:
The first phase of a data pipeline is collecting data from various sources. These sources can include products, applications, servers, datasets, devices, and sensors, and they can be spread across different networks and locations. The collection of this data from these different sources and moving them towards a central repository is the first part of the data lifecycle.
Challenges: With the growing number of sources, IT and data teams find it difficult to integrate new ones. API-based integrations can take between four to eight weeks for an enterprise data engineering team, placing significant demands on technical engineering bandwidth. Monitoring and tracking sources for anomalous behavior, identifying blocked data pipelines, and ensuring the seamless flow of telemetry data are major pain points for enterprises. With data volumes growing at ~30% Y-o-Y, being able to scale data collection to manage spikes in data flow is an important problem for engineering teams to solve, but they don’t always have the time and effort to invest in such a project.
The second phase of a data pipeline is aggregating the data, which requires multiple data operations such as cleansing, de-duplication, parsing, and normalization. Raw data is not suitable for leaders to make decisions, and it needs to be aggregated from different sources. Data from different sources have to be turned into the same format, stitched together for correlation and enrichment, and prepared to be further refined for further insights and decision-making.
Challenges: Managing the different formats and parsing it can get complicated; and with many enterprises building or having built custom applications, parsing and normalizing that data is challenging. Changing log and data schemas can create cascading failures in your data pipeline. Then there are challenges such as identifying and masking sensitive data and quarantining it to protect PII from being leaked.
The final stage is taking the data to its intended destination – a data lake or lakehouse, a cloud storage service, or an observability or security tool. For this, data has to be put into a specific format and has to be optimally segregated to avoid the high cost of the real-time analysis tools.
Challenges: Different types of telemetry data have different values, and segregating the data optimally to manage and reduce the cost of expensive SIEM and observability tools is high priority for most enterprise data teams. The ‘noise’ in the data also causes an increase in alerts and makes it harder for teams to find relevant data in the stream coming their way. Unfortunately, segregating and filtering the data optimally is difficult as engineers can't predict what data is useful and what data isn’t. Additionally, the increasing volume of data with the stagnant IT budget means that many teams are making sub-optimal choices of routing all data from some noisy sources into long-term storage, meaning that some insights are lost.
Organizations today generate terabytes of data daily and use telemetry data pipelines to move the data in real-time to derive actionable insights that inform important business decisions. However, there are major challenges in building and managing telemetry data pipelines, even if they are indispensable.
Agentic AI solves for all these challenges and is capable of delivering greater efficiency in managing and optimizing telemetry data pipeline health. An agentic AI can –
Curious about how agentic AI can solve your data problems? Get in touch with us to explore Cruz, our agentic AI data-engineer-in-a-box to solve your telemetry data challenges.
