How Modern Data Pipeline Tools Slash SIEM Costs and Storage Bills Without Sacrificing Logs

Security Data Pipeline Platforms

September 1, 2025

Abishek Ganesan

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Why are Legacy SIEMs a problem?

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.

Abishek Ganesan

Marketing Manager

At DataBahn.ai, Abishek leads the content marketing charter and helps technology, security, and data leaders worldwide understand how to unlock value from data through DataBahn's pioneering data fabric solution, which transforms enterprise data management. His diverse experience–spanning freelancing, agency work, an early-stage startup, and running a small business–sets me apart. This breadth has honed my ability to develop marketing strategies that balance immediate growth and long-term brand equity.

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Strengthening Compliance and Trust with Data Lineage in Financial Services

Discover how data lineage empowers financial institutions to meet rising regulatory demands with confidence. Learn what effective lineage looks like, why it’s so hard to achieve, and how modern data lineage tools are changing the game.

October 8, 2025

Financial data flows are some of the most complex in any industry. Trades, transactions, positions, valuations, and reference data all pass through ETL jobs, market feeds, and risk engines before surfacing in reports. Multiply that across desks, asset classes, and jurisdictions, and tracing a single figure back to its origin becomes nearly impossible. This is why data lineage has become essential in financial services, giving institutions the ability to show how data moved and transformed across systems. So, when regulators, auditors, or even your own board ask: “Where did this number come from?” too many teams still don’t have a clear answer.

The stakes couldn’t be higher. Across frameworks like BCBS-239, the Financial Data Transparency Act, and emerging supervisory guidelines in Europe, APAC, and the Middle East, regulators are raising the bar. Banks that have adopted modern data lineage tools report 57% faster audit prep and ~40% gains in engineering productivity, yet progress remains slow — surveys show that fewer than 10% of global banks are fully compliant with BCBS-239 principles. The result is delayed audits, costly manual investigations, and growing skepticism from regulators and stakeholders alike.

The takeaway is simple: data lineage is no longer optional. It has become the foundation for compliance, risk model validation, and trust. For financial services, what data lineage means is simple: without it, compliance is reactive and fragile; with it, auditability and transparency become operational strengths.

In the rest of this blog, we’ll explore why lineage is so hard to achieve in financial services, what “good” looks like, and how modern approaches are closing the gap.

Why data lineage is so hard to achieve in Financial Services

If lineage were just “draw arrows between systems,” we’d be done. In the real world it fails because of technical edge cases and organizational friction, the stuff that makes tracing a number feel like detective work.

Siloed ownership and messy handoffs
Trade, market, reference and risk systems are often owned by separate teams with different priorities. A single calculation can touch five teams and ten systems; tracing it requires stepping across those boundaries and reconciling different glossaries and operational practices. This isn’t just technical overhead but an ownership problem that breaks automated lineage capture.

Opaque, undocumented transforms in the middle
Lineage commonly breaks inside ETL jobs, bespoke SQL, or one-off spreadsheets. Those transformation steps encode business logic that rarely gets cataloged, and regulators want to know what logic ran, who changed it, and when. That gap is one of the recurring blockers to proving traceability.

Temporal and model lineage
Financial reporting and model validation require not just “where did this value come from?” but “what did it look like at time T?” Capturing temporal snapshots and ensuring you can reconstruct the exact input set for a historical run (with schema versions, parameter sets, and market snapshots) adds another layer of complexity most lineage tools don’t handle out of the box.

Scaling lineage without runaway costs
Lineage at scale is expensive. Streaming trades, tick data and high-cardinality reference tables generate huge volumes of metadata if you try to capture full, row-level lineage. Teams need to balance fidelity, cost, and query ability, and that trade-off is a frequent operational headache.

Organizational friction and change management
Technical fixes only work when governance, process and incentives change too. Lineage rollout touches risk, finance, engineering and compliance, aligning those stakeholders, enforcing cataloging discipline, and maintaining lineage over time is a people problem as much as a technology one.

The real challenge isn’t drawing arrows between systems but designing lineage that regulators can trust, engineers can maintain, and auditors can use in real time. That’s the standard the industry is now being measured against.

What good Data Lineage looks like in finance

Great lineage in financial services doesn’t look like a prettier diagram; it feels like control. The moment an auditor asks, “Where did this number come from?” the answer should take minutes, not weeks. That’s the benchmark.

It’s continuous, not reactive.
Lineage isn’t something you piece together after an audit request. It’s captured in real time as data flows — across trades, models, and reports — so the evidence is always ready.

It’s explainable to both engineers and auditors.
Engineers should see schema versions, transformations, and dependencies. Auditors should see clear traceability and business definitions. Good lineage bridges both worlds without translation exercises.

It scales with the business.
From millions of daily trades to real-time model recalculations, lineage must capture detail without exploding into unusable metadata. That means selective fidelity, efficient storage, and fast query ability built in.

It integrates governance, not adds it later.
Lineage should carry sensitivity tags, policy markers, and glossary links as data moves. Compliance is strongest when it’s embedded upstream, not enforced after the fact.

The point is simple: an effective data lineage makes defensibility the default. It doesn’t slow down data flows or burden teams with extra work. Instead, it builds confidence that every calculation, every report, and every disclosure can be traced and trusted.

Databahn in practice: Data Lineage as part of the flow

Databahn captures lineage as data moves, not after it lands. Rather than relying on manual cataloging, the platform instruments ingestion, parsing, transformation and routing layers so every change — schema update, join, enrichment or filter — is recorded as part of normal pipeline execution. That means auditors, risk teams and engineers can reconstruct a metric, replay a run, or trace a root cause without digging through ad-hoc scripts or spreadsheets.

In production, that capture is combined with selective fidelity controls, snapshotting for time-travel, and business-friendly lineage views so traceability is both precise for engineers and usable for non-technical stakeholders.

Here are a few of the key features in Databahn’s arsenal and how they enable practical lineage:

Seamless lineage with Highway
Every routing and transformation is tracked natively, giving a complete view from source to report without blind spots.
Real-time visibility and health monitoring
Continuous observability across pipelines detects lineage breaks, schema drift, or anomalies as they happen — not months later.
Governance with history recall and replay
Metadata tagging and audit trails preserve data history so any past report or model run can be reconstructed exactly as it appeared.
In-flight sensitive data handling
PII and regulated fields can be masked, quarantined, or tagged in motion, with those transformations recorded as part of the audit trail.
Schema drift detection and normalization
Automatic detection and normalization keep lineage consistent when upstream systems change, preventing gaps that undermine compliance.

The result is lineage that financial institutions can rely on, not just to pass regulatory checks, but to build lasting trust in their reporting and risk models. With Databahn, data lineage becomes a built-in capability, giving institutions confidence that every number can be traced, defended, and trusted.

The future of Data Lineage in finance

Lineage is moving from a compliance checkbox to a living capability. Regulators worldwide are raising expectations, from the Financial Data Transparency Act (FDTA) in the U.S., to ECB/EBA supervisory guidance in Europe, to data risk frameworks in APAC and the Middle East. Across markets, the signal is the same: traceability can’t be partial or reactive, it has to be continuous.

AI is at the center of this shift. Where teams once relied on static diagrams or manual cataloging, AI now powers:

Automated lineage capture – extracting flows directly from SQL, ETL code, and pipeline metadata.
Drift and anomaly detection – spotting schema changes or unusual transformations before they become audit findings.
Metadata enrichment – linking technical fields to business definitions, tagging sensitive data, and surfacing lineage in auditor-friendly terms.
Proactive remediation – recommending fixes, rerouting flows, or even self-healing pipelines when lineage breaks.

This is also where modern platforms like Databahn are heading. Rather than stop at automation, Databahn applies agentic AI that learns from pipelines, builds context, and acts, whether that’s updating lineage after a schema drift, tagging newly discovered sensitive fields, or ensuring audit trails stay complete.

Looking forward, financial institutions will also see exploration of immutable lineage records (using distributed ledger technologies) and standardized taxonomies to reduce cross-border compliance friction. But the trajectory is already clear: lineage is becoming real-time, AI-assisted, and regulator-ready by default, and platforms with agentic AI at their core are leading that evolution.

Conclusion: Lineage as the Foundation of Trust

Financial institutions can’t afford to treat lineage as a back-office detail. It’s become the foundation of compliance, the enabler of model validation, and the basis of trust in every reported number.

As regulators raise the bar and AI reshapes data management, the institutions that thrive will be the ones that make traceability a built-in capability, not an afterthought. That’s why modern platforms like DataBahn are designed with lineage at the core. By capturing data in motion, applying governance upstream, and leveraging agentic AI to keep pipelines audit-ready, they make defensibility the default.

If your institution is asking tougher questions about “where did this number come from?”, now is the time to strengthen your lineage strategy. Explore how Databahn can help make compliance, trust, and auditability a natural outcome of your data pipelines. Get in touch for a demo!

5 min read

Cybersecurity Awareness Month 2025: Why Broken Data Pipelines Are the Biggest Risk You’re Ignoring

This Cybersecurity Awareness Month, focus on resilient cybersecurity data pipelines. Learn why moving security data safely is the key to true defense.

October 9, 2025

Every October, Cybersecurity Awareness Month rolls around with the same checklist: patch your systems, rotate your passwords, remind employees not to click sketchy links. Important, yes – but let’s be real: those are table stakes. The real risks security teams wrestle with every day aren’t in a training poster. They’re buried in sprawling data pipelines, brittle integrations, and the blind spots attackers know how to exploit.

The uncomfortable reality is this: all the awareness in the world won’t save you if your cybersecurity data pipelines are broken.

Cybersecurity doesn’t fail because attackers are too brilliant. It fails because organizations can’t move their data safely, can’t access it when needed, and can’t escape vendor lock-in while dealing with data overload. For too long, we’ve built an industry obsessed with collecting more data instead of ensuring that data can flow freely and securely through pipelines we actually control.

It’s time to embrace what many CISOs, SOC leaders, and engineers quietly admit: your security posture is only as strong as your ability to move and control your data.

The Hidden Weakness: Cybersecurity Data Pipelines

Every security team depends on pipelines, the unseen channels that collect, normalize, and route security data across tools and teams. Logs, telemetry, events, and alerts move through complex pipelines connecting endpoints, networks, SIEMs, and analytics platforms.

And yet, pipelines are treated like plumbing. Invisible until they burst. Without resilient pipelines, visibility collapses, detections fail, and incident response slows to a crawl.

Security teams drowning in data yet starved for the right insights because their pipelines were never designed for flexibility or scale. Awareness campaigns should shine a light on this blind spot. Teams must not only know how phishing works but also how their cybersecurity data pipelines work — where they’re brittle, where data is locked up, and how quickly things can unravel when data can’t move.

Data Without Movement Is Useless

Here’s a hard truth: security data at rest is as dangerous as uncollected evidence.

Storing terabytes of logs in a single system doesn’t make you safer. What matters is whether you can move security data safely when incidents strike.

Can your SOC pivot logs into a different analytics platform when a breach unfolds?
Can compliance teams access historical data without waiting weeks for exports?
Can threat hunters correlate data across environments without being blocked by proprietary formats?

When data can’t move, it becomes a liability. Organizations have failed audits because they couldn’t produce accessible records. Breaches have escalated because critical logs were locked in a vendor’s silo. SOCs have burned out on alert fatigue because pipelines dumped raw, unfiltered data into their SIEM.

Movement is power. Databahn products are designed around the principle that data only has value if it’s accessible, portable, and secure in motion.

Moving Data Safely: The Real Security Priority

Everyone talks about securing endpoints, networks, and identities. But what about the routes your data travels on its way to analysts and detection systems?

The ability to move security data safely isn’t optional. It’s foundational. And “safe” doesn’t just mean encryption at rest. It means:

Encryption in motion to protect against interception
Role-based access control so only the right people and tools can touch sensitive data
Audit trails that prove how and where data flowed
Zero-trust principles applied to the pipeline itself

Think of it this way: you wouldn’t spend millions on vaults for your bank and then leave your armored trucks unguarded. Yet many organizations do exactly that, lock down storage, while neglecting the pipelines.

This is why Databahn emphasizes pipeline resilience. With solutions like Cruz, we’ve seen organizations regain control by treating data movement as a first-class security priority, not an afterthought.

A New Narrative: Control Your Data, Control Your Security

At the heart of modern cybersecurity is a simple truth: you control your narrative when you control your data.

Control means more than storage. It means knowing where your data lives, how it flows, and whether you can pivot it when threats emerge. It means refusing to accept vendor black boxes that limit visibility. It means architecting pipelines that give you freedom, not dependency.

This philosophy drives our work at Databahn. With Reef helping teams shape, access, and govern security data, and Cruz enabling flexible, resilient pipelines. Together, these approaches echo a broader industry need: break free from lock-in, reclaim control, and treat your pipeline as a strategic asset.

Security teams that control their pipelines control their destiny. Those that don’t remain one vendor outage or one pipeline failure away from disaster.

The Path Forward: Building Resilient Cybersecurity Data Pipelines

So how do we shift from fragile to resilient? It starts with mindset. Security leaders must see data pipelines not as IT plumbing but as strategic assets. That shift opens the door to several priorities:

Embrace open architectures – Avoid tying your fate to a single vendor. Design pipelines that can route data into multiple destinations.
Prioritize safe, audited movement – Treat data in motion with the same rigor you treat stored data. Every hop should be visible, secured, and controlled.
Test pipeline resilience – Run drills that simulate outages, tool changes, and rerouting. If your pipeline can’t adapt in hours, you’re vulnerable.
Balance cost with control – Sometimes the cheapest storage or analytics option comes with the highest long-term lock-in risk. Awareness must extend to financial and operational trade-offs.

We’ve seen organizations unlock resilience when they stop thinking of pipelines as background infrastructure and start thinking of them as the foundation of cybersecurity itself. This shift isn’t just about tools, it’s about mindset, architecture, and freedom.

The Real Awareness Shift We Need

As Cybersecurity Awareness Month 2025 unfolds, we’ll see the usual campaigns: don’t click suspicious links, don’t ignore updates, don’t recycle passwords. All valuable advice. But we must demand more from ourselves and from our industry.

The real awareness shift we need is this: don’t lose control of your data pipelines.

Because at the end of the day, security isn’t about awareness alone. It’s about the freedom to move, shape, and use your data whenever and wherever you need it.

Until organizations embrace that truth, attackers will always be one step ahead. But when we secure our pipelines, when we refuse lock-in, and when we prioritize safe movement of data, we turn awareness into resilience.

And that is the future cybersecurity needs.

5 min read

Recap | From Chaos to Clarity Webinar

This blog captures key takeaways from analysts and practitioners from Forrester, Becton Dickinson, and Databahn leaders on why pipeline independence is essential for resilience, visibility, and future-ready security operations.

October 3, 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.