Hybrid Data Pipeline Security: Best Practices for Telemetry in 2025

Ask any security practitioner what keeps them up at night, and it rarely comes down to a specific tool. It's usually the data itself – is it complete, trustworthy, and reaching the right place at the right time?

Pipelines are the arteries of modern security operations. They carry logs, metrics, traces, and events from every layer of the enterprise. Yet in too many organizations, those arteries are clogged, fragmented, or worse, controlled by someone else.

That was the central theme of our webinar, From Chaos to Clarity, where Allie Mellen, Principal Analyst at Forrester, and Mark Ruiz, Sr. Director of Cyber Risk and Defense at BD, joined our CPO Aditya Sundararam and our CISO Preston Wood.

Together, their perspectives cut through the noise: analysts see a market increasingly pulling practitioners into vendor-controlled ecosystems, while practitioners on the ground are fighting to regain independence and resilience.

The Analyst's Lens: Why Neutral, Open Pipelines Matter

Allie Mellen spends her days tracking how enterprises buy, deploy, and run security technologies. Her warning to practitioners is direct: control of the pipeline is slipping away.

The last five years have seen unprecedented consolidation of security tooling. SIEM vendors offer their own ingestion pipelines. Cloud hyperscalers push their monitoring and telemetry services as defaults. Endpoint and network vendors bolt on log shippers designed to funnel telemetry back into their ecosystems.

It all looks convenient at first. Why not let your SIEM vendor handle ingestion, parsing, and routing? Why not let your EDR vendor auto-forward logs into its own analytics console?

Allie's answer: because convenience is control and you're not the one holding it.

" Practitioners are looking for a tool much like with their SIEM tool where they want something that is independent or that’s kind of how they prioritize this "

— Allie Mellen, Principal Analyst, Forrester

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This erosion of control has real consequences:

• Vendor lock-in: Once you're locked into a vendor's pipeline, swapping tools downstream becomes nearly impossible. Want to try a new analytics platform? Your data is tied up in proprietary formats and routing rules.
• Blind spots: Vendor-native pipelines often favor data that benefits the vendor's use cases, not the practitioners’. This creates gaps that adversaries can exploit.
• AI limitations: Every vendor now advertises "AI-driven security." But as Allie points out, AI is only as good as the data it ingests. If your pipeline is biased toward one vendor's ecosystem, you'll get AI outcomes that reflect their blind spots, not your real risk.

For Allie, the lesson is simple: net-neutral pipelines are the only way forward. Practitioners must own routing, filtering, enrichment, and forwarding decisions. They must have the ability to send data anywhere, not just where one vendor prefers.

That independence is what preserves agility, the ability to test new tools, feed new AI models, and respond to business shifts without ripping out infrastructure.

The Practitioner's Challenge: BD's Story of Data Chaos

Theory is one thing, but what happens when practitioners actually lose control of their pipelines? For Becton Dickinson (BD), a global leader in medical technology, the consequences were very real.

BD's environment spanned hospitals, labs, cloud workloads, and thousands of endpoints. Each vendor wanted to handle telemetry in its own way. SIEM agents captured one slice, endpoint tools shipped another, and cloud-native services collected the rest.

The result was unsustainable:

• Duplication: Multiple vendors forwarding the same data streams, inflating both storage and licensing costs.
• Blind spots: Medical device telemetry and custom application logs didn't fit neatly into vendor-native pipelines, leaving dangerous gaps.
• Operational friction: Pipeline management was spread across several vendor consoles, each with its own quirks and limitations.

For BD's security team, this wasn't just frustrating, it was a barrier to resilience. Analysts wasted hours chasing duplicates while important alerts slipped through unseen. Costs skyrocketed, and experimentation with new analytics tools or AI models became impossible.

Mark Ruiz, Sr. Director of Cyber Risk and Defense at BD, knew something had to change.

With Databahn, BD rebuilt its pipeline on neutral ground:

• Universal ingestion: Any source from medical device logs to SaaS APIs could be onboarded.
• Scalable filtering and enrichment: Data was cleaned and streamlined before hitting downstream systems, reducing noise and cost.
• Flexible routing: The same telemetry could be sent simultaneously to Splunk, a data lake, and an AI model without duplication.
• Practitioner ownership: BD controlled the pipeline itself, free from vendor-imposed limits.

The benefits were immediate. SIEM ingestion costs dropped sharply, blind spots were closed, and the team finally had room to innovate without re-architecting infrastructure every time.

" We were able within about eight, maybe ten weeks consolidate all of those instances into one Sentinel instance in this case, and it allowed us to just unify kind of our visibility across our organization."

‍ — Mark Ruiz, Sr. Director, Cyber Risk and Defense, BD

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Where Analysts and Practitioners Agree

What's striking about Allie's analyst perspective and Mark's practitioner experience is how closely they align.

Both argue that convenience isn't resilience. Vendor-native pipelines may be easy up front, but they lock teams into rigid, high-cost, and blind-spot-heavy futures.

Both stress that pipeline independence is fundamental. Whether you're defending against advanced threats, piloting AI-driven detection, or consolidating tools, success depends on owning your telemetry flow.

And both highlight that resilience doesn't live in downstream tools. A world-class SIEM or an advanced AI model can only be as good as the data pipeline feeding it.

This alignment between market analysis and hands-on reality underscores a critical shift: pipelines aren't plumbing anymore. They're infrastructure.

The Databahn Perspective

For Databahn, this principle of independence isn't an afterthought—it's the foundation of the approach.

Preston Wood, CSO at Databahn, frames it this way:

"We don't see pipelines as just tools. We see them as infrastructure. The same way your network fabric is neutral, your data pipeline should be neutral. That's what gives practitioners control of their narrative."

— Preston Wood, CSO, Databahn

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This neutrality is what allows pipelines to stay future-proof. As AI becomes embedded in security operations, pipelines must be capable of enriching, labeling, and distributing telemetry in ways that maximize model performance. That means staying independent of vendor constraints.

Aditya Sundararam, CPO at Databahn, emphasizes this future orientation: building pipelines today that are AI-ready by design, so practitioners can plug in new models, test new approaches, and adapt without disruption.

Own the Pipeline, Own the Outcome

For security practitioners, the lesson couldn't be clearer: the pipeline is no longer just background infrastructure. It's the control point for your entire security program.

Analysts like Allie warn that vendor lock-in erodes practitioner control. Practitioners like Mark show how independence restores visibility, reduces costs, and builds resilience. And Databahn's vision underscores that independence isn't just tactical, it's strategic.

So the question for every practitioner is this: who controls your pipeline today?

If the answer is your vendor, you've already lost ground. If the answer is you, then you have the agility to adapt, the visibility to defend, and the resilience to thrive.

In security, tools will come and go. But the pipeline is forever. Own it, or be owned by it.

The MITRE ATT&CK Evaluations have entered unexpected choppy waters. Several of the cybersecurity industry’s largest platform vendors have opted out this year, each using the same language about “resource prioritization” and “customer focus”. When multiple leaders step back at once, it raises some hard questions. Is this really about resourcing, or about avoiding scrutiny? Or is it the slow unraveling of a bellwether and much-loved institution?

Speculation is rife; some suggest these giants are wary of being outshone by newer challengers; other believe it reflects uncertainty inside MITRE itself. Whatever the case, the exits have forced a reckoning: does ATT&CK still matter? At Databahn, we believe it does – but only if it evolves into something greater than it is today.

What is MITRE ATT&CK and why it matters

MITRE ATT&CK was born from a simple idea: if we could catalog the real tactics and techniques adversaries use in the wild, defenders everywhere could share a common language and learn from each other. Over time, ATT&CK became more than a knowledge base – it became the Rosetta Stone of modern cybersecurity.

The Evaluations program extended that vision. Instead of relying on vendor claims or glossy datasheets, enterprises could see how different tools performed against emulated threat actors, step by step. MITRE never crowned winners or losers; it simply published raw results, offering a level playing field for interpretation.

That transparency mattered. In an industry awash with noise and marketing spin, ATT&CK Evaluations became one of the few neutral signals that CISOs, SOC leaders, and practitioners could trust. For many, it was less about perfect scores and more about seeing how a tool behaved under pressure – and whether it aligned with their own threat model.

The Misuse and the Criticisms

For years, ATT&CK Evaluations were one of the few bright spots in an industry crowded with vendor claims. CISOs could point to them as neutral, transparent – and at least in theory – immune from spin. In a market that rarely offers apples-to-apples comparisons, ATT&CK stood out as a genuine attempt at objectivity. In defiance of the tragedy of the commons, it remained neutral, with all revenues routed towards doing more research to improve public safety.

The absences of some of the industry’s largest vendors have sparked a firestorm of commentary. While their detractors are skeptical about their near-identical statements and suggest that this was strategic, it raises questions at a time when criticisms of MITRE ATT&CK Evaluations were also growing more strident, pointing to how results were interpreted – or rather, misinterpreted. While MITRE doesn’t crown champions, hand out trophies, or assign grades, vendors have been quick to award themselves with imagined laurels. Raw detection logs are taken and twisted into “best-in-class" coverage, missing the nuance that matters most: whether detections were actionable, whether alerts drowned analysts in noise, and whether the configuration mirrored a real production environment.

The gap became even more stark when evaluation results didn’t line up with enterprise reality. CISOs would see a tool perform flawlessly on paper, only to watch it miss basic detections or drown SOCs with false positives. The disconnect wasn’t the fault of the ATT&CK framework itself, which didn’t intend to simulate the full messiness of a live environment. But this gave critics the ammunition to question whether the program had lost its value.

And of course, there is the Damocles’ sword of AI. In a time of dynamic threats being spun up and vulnerabilities exploited in days, do one-time evaluations of solutions really have the same effectiveness? In short, what was designed to be a transparent reference point too often CISOs and SOC teams were left to sift through competing storylines–especially in an ecosystem where AI-powered speed rendered static frameworks less effective.

Making the gold standard shine again

For all its flaws and frustrations, ATT&CK remains the closest thing cybersecurity has to a gold standard. No other program managed to establish such a widely accepted, openly accessible benchmark for adversary behavior. For CISOs and SOC leaders, it has become the shared map that allows them to compare tools, align on tactics, and measure their own defenses against a common framework.

Critics are right to point out the imperfections in MITRE Evaluations. But in a non-deterministic security landscape – where two identical attacks can play out in wildly different ways – imperfection is inevitable. What makes ATT&CK different is that it provides something few others do: neutrality. Unlike vendor-run bakeoffs, pay-to-play analyst reports, or carefully curated customer case studies, ATT&CK offers a transparent record of what happened, when, and how. No trophies, no hidden methodology, no commercial bias. Just data.

That’s why, even as some major players step away, ATT&CK still matters. It is not a scoreboard and never should have been treated as one. It is a mirror that shows us where we stand, warts and all. And when that mirror is held up regularly, it keeps vendors honest, challengers motivated, and buyers better informed. And most importantly, it keeps us all safer and better prepared for the threats we face today.

Yet, holding up a mirror once a year is no longer enough. The pace of attacks has accelerated, AI is transforming both offense and defense, and enterprises can’t afford to wait for annual snapshots. If ATT&CK is to remain the industry’s north star, it must evolve into something more dynamic – capable of keeping pace with today’s threats and tomorrow’s innovations.

From annual tests to constant vigilance

If ATT&CK is to remain the north star of cybersecurity, it cannot stay frozen in its current form. Annual, one-off evaluations feel outdated in today’s fast-paced threat landscape. The need is to test enterprise deployments, not security tools in sterilized conditions.  

In one large-scale study, researchers mapped enterprise deployments against the same MITRE ATT&CK techniques used in evaluations. The results were stark: despite high vendor scores in controlled settings, only 2% of adversary behaviors were consistently detected in product. That kind of drop-off exposes a fundamental gap – not in MITRE’s framework itself, but in how it is being used.

The future of ATT&CK must be continuous. Enterprises should be leveraging the framework to test their systems, because that is what is being attacked and under threat. These tests should be a consistent process of stress-testing, learning, and improving. Organizations should be able to validate their security posture against MITRE techniques regularly – with results that reflect live data, not just laboratory conditions.

This vision is no longer theoretical. Advances in data pipeline management and automation now make it possible to run constant, low friction checks on how telemetry maps to ATT&CK. At Databahn, we’ve designed our platform to enable exactly this: continuous visibility into coverage, blind spots, and gaps in real-world environments. By aligning security data flows directly with ATT&CK, we help enterprises move from static validation to dynamic, always-on confidence.

Vendors shouldn’t abandon MITRE ATT&CK Evaluations; they should make it a module in their products, to enable enterprises to consistently evaluate their security posture. This will ensure that enterprises can keep better pace with an era of relentless attack and rapid innovation. The value of ATT&CK was never in a single set of results – but in the discipline of testing, interpreting, and improving, again and again.

In cybersecurity today, the most precious resource is not the latest tool or threat feed – it is intelligence. And this intelligence is only as strong as the data foundation that creates it from the petabytes of security telemetry drowning enterprises today. Security operation centers (SOCs) worldwide are being asked to defend at AI speed, while still struggling to navigate a tidal wave of logs, redundant alerts, and fragmented systems.

This is less about a product release and more about a movement​​—a movement that​​​​​​ places data at the foundation for agentic, AI-powered cybersecurity. It signals a shift in how the industry must think about security data: not as exhaust to be stored or queried, but as a living fabric that can be structured, enriched, and made ready for AI-native defense.

At DataBahn, we are proud to ​​partner with Databricks and fully integrate with their technology. Together, we are helping enterprises transition from reactive log management to proactive security intelligence,​​​​​​ transforming fragmented telemetry into trusted, actionable insights at scale.

From Data Overload to Data Intelligence

For decades, the industry’s instinct has been to capture more data. Every sensor, every cloud workload, and every application heartbeat is shipped to a SIEM or stored in a data lake for later investigation. The assumption was simple: more data equals better defense. But in practice, this approach has created more problems for enterprises.

Enterprises now face terabytes of daily data ingestion, much of which is repetitive, irrelevant, or misaligned with actual detection needs. This data also comes in different formats from hundreds and thousands of devices, and security tools and systems are overwhelmed by noise. Analysts are left searching for needles in haystacks, while adversaries increasingly leverage AI to strike more quickly and precisely.

What’s needed is not just scale, but intelligence: the ability to collect vast volumes of security data and to understand, prioritize, analyze, and act on it while it is in motion. Databricks provides the scale and flexibility to unify massive volumes of telemetry. DataBahn brings the data collection, in-motion enrichment, and AI-powered tiering and segmenting that transform raw telemetry into actionable insights.

Next-Gen Security Data Infrastructure Platform

Databricks is the foundation for operationalizing AI at scale in modern cyber defense, enabling faster threat detection, investigation, and response. It enables the consolidation of all security, IT, and business data into a single, governed ​Data Intelligence Platform​​​ – which becomes a ready ​​dataset​​​​​​ for AI to operate on. When you combine this with DataBahn, you create an AI-ready data ecosystem that spans from source to destination and across the data lifecycle.

DataBahn sits on the left of Databricks, ensuring decoupled and flexible log and data ingestion into downstream SIEM solutions and ​​Databricks. It leverages Agentic AI for data flows, automating the ingestion, parsing, normalization, enrichment, and schema drift handling of security telemetry across hundreds of formats. No more brittle connectors, no more manual rework when schemas drift. With AI-powered tagging, tracking, and tiering, you ensure that the ​​correct​​​​​​ data goes to the right place and optimize your SIEM license costs.

​​​Agentic AI​ is leveraged​ to deliver insights and intelligence not just to data at rest, stored in Databricks​,​​​ but also in flight via a persistent knowledge layer. Analysts can ask real questions in natural language and get contextual answers instantly, without writing queries or waiting on downstream indexes. Security tools and AI applications can access this layer to reduce time-to-insight and MTTR even more.

The solution brings the data intelligence vision tangible for security​​ and is in sync with DataBahn’s vision for Headless Cyber Architecture. This is an ecosystem where enterprises control their own data in Databricks, and security tools (such as the SIEM) do less ingestion and more detection. Your Databricks security data storage becomes the source of truth.

Making the Vision Real for Enterprises

Security leaders don’t need another dashboard or more security tools. They need their teams to move faster​​ and with confidence. For that, they need their data to be reliable, contextual, and usable – whether the task is threat hunting, compliance, or powering a new generation of AI-powered workflows.

By combining Databricks’ unified platform with DataBahn’s agentic AI pipeline, enterprises can:

• Cut through noise at the source: Filter out low-value telemetry before it ever clogs storage or analytics pipelines, preserving only what matters for detection and investigation.

• Enrich with context automatically: Map events against frameworks such as MITRE ATT&CK, tag sensitive data for governance, and unify signals across IT, cloud, and OT environments.

• Accelerate time to insight: Move away from waiting hours for query results to getting contextual answers in seconds, through natural language interaction with the data itself. Get insights from data in motion or stored/retained data, kept in AI-friendly structures for investigation.

• Power AI-native security apps: Feed consistent, high-fidelity telemetry into Databricks models and downstream security tools, enabling generative AI to act with confidence and explainability. Leverage Reef for insight-rich data to reduce compute costs and improve response times.

For SOC teams, this means less time spent triaging irrelevant alerts and more time preventing breaches. For CISOs, this means greater visibility and control across the entire enterprise, while empowering their teams to achieve more at lower costs. For the business, it means security and data ownership that scale with innovation.

A Partnership Built for the Future

Databricks’ Data Intelligence for ​Cybersecurity ​​​brings the scale and governance enterprises need to unify their data at rest as a central destination. With DataBahn, data arrives in Databricks already optimized – AI-powered pipelines make it usable, insightful, and actionable in real time.

This partnership goes beyond integration – it lays the foundation for a new era of cybersecurity, where data shifts from liability to advantage in unlocking generative AI for defense. Together, Databricks’ platform and DataBahn’s intelligence layer give security teams the clarity, speed, and agility they need against today’s evolving threats.

What Comes Next

The launch of Data Intelligence for ​Cybersecurity ​​​is only the beginning. Together, Databricks and DataBahn are helping enterprises reimagine how they collect, manage, secure, and leverage data.

The vision is clear – a platform that is:

• Lightweight and modular – collect data from any source effortlessly, including AI-powered integration for custom applications and microservices.

• Broadly integrated – DataBahn comes with a library of collectors for aggregating and transforming telemetry, while Databricks creates a unified data storage for the telemetry.

• Intelligently optimized – remove 60-80% of non-security-relevant data and keep it out of your SIEM to save on costs; eventually, make your SIEM work as a detection engine on top of Databricks as a storage layer for all security telemetry.
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• Enrichment-first – apply threat intel, identify, geospatial data, and other contextual information before forwarding data into Databricks and your SIEM to make analysis and investigations faster and smarter.

• AI-ready – feeding clean, contextualized, and enriched data into Databricks to be fed into your models and your AI applications – for metrics and richer insights, they can also leverage Reef to save on compute.

This is the next era of security – and it starts with ​​​data​​​. ​Together, Databricks and DataBahn provide a​​​n AI-native foundation in which telemetry is self-optimized and stored in a way to make insights instantly accessible. Data is turned into intelligence, and intelligence is turned into action.