Adding Context to Security Event Logs Without Exploding Volume

ROI is the metric that shows up in dashboards, budget reviews, and architecture discussions because it’s easy to measure and easy to attribute. Lower GB/day. Fewer logs. Reduced SIEM bills. Tighter retention.

But this is only the cost side of the equation — not the value side.

This mindset didn’t emerge because teams lack ambition. It emerged because cloud storage, SIEM licensing, and telemetry sprawl pushed everyone toward quick, measurable optimizations. Cutting volume became the universal lever, and over time, it began to masquerade as ROI itself.

The problem is simple: volume reduction says nothing about whether the remaining data is useful, trusted, high-quality, or capable of driving outcomes. It doesn’t tell you whether analysts can investigate faster, whether advanced analytics or automation can operate reliably, whether compliance risk is dropping, or whether teams across the business can make better decisions.

And that’s exactly where the real return lies.

Modern Data ROI must account for value extracted, not just volume avoided — and that value is created upstream, inside the pipeline, long before data lands in any system.

To move forward, we need to expand how organizations think about Data ROI from a narrow cost metric into a strategic value framework.

When Saving on Ingestion Cost Ends Up Costing You More

For most teams, reducing telemetry volume feels like the responsible thing to do. SIEM bills are rising, cloud storage is growing unchecked, and observability platforms charge by the event. Cutting data seems like the obvious way to protect the budget.

But here’s the problem: Volume is a terrible proxy for value.

When reductions are driven purely by cost, teams often remove the very signals that matter most — authentication context, enriched DNS fields, deep endpoint visibility, VPC flow attributes, or verbose application logs that power correlation. These tend to be high-volume, and therefore the first to get cut, even though they carry disproportionately high investigative and operational value.

And once those signals disappear, things break quietly:

• Detections lose precision
• Alert triage slows down
• investigations take longer
• root cause analysis becomes guesswork
• Incident timelines get fuzzy
• Reliability engineering loses context

All because the reduction was based on size, not importance.

Teams don’t cut the wrong data intentionally — they do it because they’ve never had a structured way to measure what each dataset contributes to security, reliability, or business outcomes. Without a value framework, cost becomes the default sorting mechanism.

This is where the ROI conversation goes off the rails. When decisions are made by volume instead of value, “saving” money often creates larger downstream costs in investigations, outages, compliance exposure, and operational inefficiency.

To fix this, organizations need a broader definition of ROI — one that captures what data enables, not just what it costs.

From Cost Control to Value Creation: Redefining Data ROI  

Many organizations succeed at reducing ingestion volume. SIEM bills come down. Storage growth slows. On paper, the cost problem looks addressed. Yet meaningful ROI often remains elusive.

The reason is simple: cutting volume manages cost, but it doesn’t manage value.

When reductions are applied without understanding how data is used, high-value context is often removed alongside low-signal noise. Detections become harder to validate. Investigations slow down. Pipelines remain fragmented, governance stays inconsistent, and engineering effort shifts toward maintaining brittle flows instead of improving outcomes. The bill improves, but the return does not.

To move forward, organizations need a broader definition of Data ROI, one that aligns more closely with FinOps principles. FinOps isn’t about minimizing spend in isolation. It’s about evaluating spend in the context of the value it creates.  

Data ROI shows up in:

• Signal quality and context, where complete, normalized data supports accurate detections and faster investigations.
• Timeliness, where data arrives quickly enough to drive action.
• Governance and confidence, where teams know how data was handled and can trust it during audits or incidents.
• Cross-team reuse, where the same governed data supports security, reliability, analytics, and compliance without duplication.
• Cost efficiency as an outcome, where volume reduction preserves the signals that actually drive results.

When these dimensions are considered together, the ROI question shifts from how much data was cut to how effectively data drives outcomes.

This shift from cost control to value creation is what sets the stage for a different approach to pipelines, one designed to protect, amplify, and sustain returns.

What Value Looks Like in Practice

The impact of a value-driven pipeline becomes most visible when you look at how it changes day-to-day outcomes.

Consider a security team struggling with rising SIEM costs. Instead of cutting volume across the board, they rework ingestion to preserve high-value authentication, network, and endpoint context while trimming redundant fields and low-signal noise. Ingest costs drop, but more importantly, detections improve. Alerts become easier to validate; investigations move faster, and analysts spend less time chasing incomplete events.

In observability environments, the shift is similar. Application and infrastructure logs are routed with intent. High-resolution data stays available during incidents, while routine operational exhaust is summarized or routed to lower-cost storage. Reliability teams retain the context they need during outages without paying premium rates for data they rarely touch. Mean time to resolution improves even as overall spend stabilizes.

The same pattern applies to compliance and audit workflows. When privacy controls, lineage, and routing rules are enforced in the pipeline, teams no longer scramble to reconstruct how data moved or where sensitive fields were handled. Audit preparation becomes predictable, repeatable, and far less disruptive.

Across these scenarios, ROI doesn’t show up as a single savings number. It shows up as faster investigations, clearer signals, reduced operational drag, and confidence that critical data is available when it matters.

That is the difference between cutting data and managing it for value.  

Measuring Success by Value, Not Volume

Data volumes will continue to grow. Telemetry, logs, and events are becoming richer, more frequent, and more distributed across systems. Cost pressure is not going away, and neither is the need to control it.

But focusing solely on how much data is cut misses the larger opportunity. Real ROI comes from what data enables: faster investigations, better operational decisions, predictable compliance, and systems that teams can trust when it matters most.

Modern Data Pipeline Management reframes the role of pipelines from passive transport to active value creation. When data is shaped with intent, governed in motion, and reused across teams, every downstream system benefits. Cost efficiency follows naturally, but it is a byproduct, not the goal.

The organizations that succeed in the FinOps era will be those that treat data as an investment, not an expense. They will measure ROI not by the terabytes they avoided ingesting, but by the outcomes their data consistently delivers.

“We need to add 100+ more applications to our SIEM, but we have no room in our license. We have to migrate to a cheaper SIEM,” said every enterprise CISO. With 95%+ usage of their existing license – and the new sources projected to add 60% to their log volume – they had to migrate. But the reluctance was so obvious; they had spent years making this SIEM work for them. “It understands us now, and we’ve spent years to make it work that way,” said that Director for Security Operations.

They had spent years compensating for the complexity of the old system, and turned it into a skillset.

Their threat detection and investigation team had mastered its query language. The data engineering team had built configuration rules, created complex parsers, and managed the SIEM’s field extraction quirks and fragmented configuration model. They were proud of what they had built, and rightfully so. But today, that expertise had become a barrier. Security teams today are still investing their best talent and millions of dollars in mastering complexity because their tools never invested enough in making things simple.

Operators are expected to learn a vendor’s language, a vendor’s model, a vendor’s processing pipeline, and a vendor’s worldview. They are expected to stay updated with the vendor’s latest certifications and features. And over time, that mastery becomes a requirement to do the job. And at an enterprise level, it becomes a cage.

This is the heart of the problem. Ease of use is a burden security teams are taking upon themselves, because vendors are not.

How we normalized the burden of complexity

In enterprise security, complexity often becomes a proxy for capability. If a tool is difficult to configure, we assume it must be powerful. If a platform requires certifications, we assume it must be deep. If a pipeline requires custom scripting, we assume that is what serious engineering looks like.

This slow, cultural drift has shaped the entire landscape.

Security platforms leaned on specialized query languages that require months of practice. SIEMs demanded custom transformation and parsing logic that must be rebuilt for every new source. Cloud security tools introduced their own rule engines and ingestion constraints. Observability platforms added configuration models that required bespoke tuning. Tools were not built to work in the way teams did; teams had to be built in a way to make the tool work.

Over time, teams normalized this expectation. They learned to code around missing features. They glued systems together through duct-tape pipelines. They designed workarounds when vendor interfaces fell short. They memorized exceptions, edge cases, and undocumented behaviors. Large enterprises built complex workflows and systems, customized and personalized software that cost millions to operate out of the box, and invested millions more of their talent and expertise to make it usable.

Not because it was the best way to operate. But because the industry never offered alternatives.

The result is an ecosystem where talent is measured by the depth of tool-specific knowledge, not by architectural ability or strategic judgment. A practitioner who has mastered a single platform can feel trapped inside it. A CISO who wants modernization hesitates because the existing system reflects years of accumulated operator knowledge. A detection engineer becomes the bottleneck because they are the only one who can make sense of a particular piece of the stack.

This is not the fault of the people. This is the cost of tools that never prioritized usability.

The consequences of tool-defined expertise

When a team is forced to become experts in tool complexity, several hidden problems emerge.

First, tool dependence becomes talent dependence. If only a few people can maintain the environment, then the environment cannot evolve. This limits the organization’s ability to adopt new architectures, onboard new data sources, or adjust to changing business requirements.

Second, vendor lock-in becomes psychological, not just contractual. The fear of losing team expertise becomes a bigger deterrent than licensing or performance issues.

Third, practitioners spend more time repairing the system than improving it. Much of their effort goes into maintaining the rituals the tool requires rather than advancing detection coverage, improving threat response, or designing scalable data architectures.

Fourth, data ownership becomes fragmented. Teams rely heavily on vendor-native collectors, parsers, rules, and models, which limits how and where data can move. This reduces flexibility and increases the long-term cost of security analytics.

These patterns restrict growth. They turn security operations into a series of compensations. They push practitioners to specialize in tool mechanics instead of the broader discipline of security engineering.

Why ease of use needs to be a strategic priority

There is a misconception that making a platform simpler somehow reduces its capability or seriousness. But in every other field, from development operations to data engineering, ease of use is recognized as a strategic accelerator.

Security has been slow to adopt this view because complexity has been normalized for so long. But ease of use is not a compromise. It is a requirement for adaptability, resilience, and scale.

A platform that is easy to use enables more people to participate in the architecture. It allows senior engineers to focus on high-impact design instead of low-level maintenance. It ensures that talent is portable and not trapped inside one tool’s ecosystem. It reduces onboarding friction. It accelerates modernization. It reduces burnout.

And most importantly, it allows teams to focus on the job to be done rather than the tool to be mastered. At a time when experienced security personnel are needed, when burnout is an acknowledged and significant challenge in the security industry, and while security budgets continue to fall short of where they need to be, removing tool-based filters and limitations would be extremely useful.

How AI helps without becoming the story

This is an instance where AI doesn’t hog the headline, but plays an important role nonetheless. AI can automate a lot of the high-effort, low-value work that we’re referring to. It can help automate parsing, data engineering, quality checks, and other manual flows that created knowledge barriers and necessitated certifications in the first place.  

At Databahn, AI has already simplified the process of detecting data, building pipelines, creating parsers, tracking data quality, managing telemetry health, fixing schema drift, and quarantining PII. But AI is not the point – it’s a demonstration of what the industry has been missing. AI helps show that years of accumulated tool complexity – particularly in bridging the gap between systems, data streams, and data silos – were not inevitable. They were simply unmet customer needs, where the gaps were filled by extremely talented technical talent, which was forced to expend their effort doing this instead of strategic work.

Bigger platforms and the illusion of simplicity

In response to these pressures, several large security vendors have taken a different approach. Instead of rethinking complexity, they have begun consolidating tools through acquisition, bundling SIEM, SOAR, EDR, cloud security, data lakes, observability, and threat analytics into a single ecosystem. On the surface, this appears to solve the usability problem. One login. One workflow. One vendor relationship. One neatly integrated stack.

But this model rarely delivers the simplicity it promises.  

Each acquired component carries its own legacy. Each tool inside the stack has its own schema, its own integration style, its own operational boundaries, and its own quirks. Teams still need to learn the languages and mechanics of the ecosystem; now there are simply more of them tucked under a single logo. The complexity has not disappeared. It has been centralized.

For some enterprises, this consolidation may create incremental improvements, especially for teams with limited engineering resources. But in the long term, it creates a deeper problem. The dependency becomes stronger. The lock-in becomes tighter. And the cost of leaving grows exponentially.

The more teams build inside these ecosystems, the more their processes, content, and institutional knowledge become inseparable from a vendor’s architecture. Every new project, every new parser, every new detection rule becomes another thread binding the organization to a specific way of operating. Instead of evolving toward data ownership and architectural flexibility, teams evolve within the constraints of a platform. Progress becomes defined by what the vendor offers, not by what the organization needs.

This is the opposite direction of where security must go. The future is not platform dependence. It is data independence. It is the ability to own, govern, transform, and route telemetry on your terms. It is the freedom to adapt tools to architecture, not architecture to tools. Consolidated ecosystems do not offer this freedom. They make it harder to achieve. And the longer an organization stays inside these consolidated stacks, the more difficult it becomes to reclaim that independence.

The CISO whose team changed its mind

The CISO from the beginning of this piece evaluated Databahn in a POC. They were initially skeptical; their operators believed that no-code systems were shortcuts, and expected there to be strong trade-offs in capability, precision, and flexibility. They expected to outgrow the tool immediately.

When the Director of Security Operations logged into the tool and realized they could make a pipeline in a few minutes by themselves, they realized that they didn’t need to allocate the bandwidth of two full data engineers to operate Databahn and manage the pipeline. They also saw approximately 70% volume reduction, and could add those 100+ sources in 2 weeks, instead of a few months.

The SOC chose Databahn at the end of the POC. Surprisingly, they also chose to retain their old SIEM. They could more easily export their configurations, rules, systems, and customizations into Databahn and since license costs were low, the underlying reason to migrate disappeared. But now, they are not spending cycles building pipelines, connecting sources, applying transformations, and building complex queries or writing complex code. They have found that Databahn’s ease of use has not removed their expertise; it’s elevated it. The same operators who resisted Databahn are now advocates for it.  

The team is now taking their time to design and build a completely new data architecture. They are now focused on using their years of expertise to build a future-proof security data system and architecture that meets their use case and is not constrained by the old barriers of tool-specific knowledge.

The future belongs to teams, not tools

Security does not need more dependence on niche skills. It does not need more platforms that require specialized certifications. It does not need more pipelines that can only be understood by one or two experts.

Security needs tools that make expertise more valuable, not less. Tools that adapt to people and teams, not the other way around. Tools that treat ease of use as a core requirement, not a principle to be condescendingly ignored or selectively focused on people who already know how to use their tool.  

Teams should not have to invest in mastering complexity. Tools should invest in removing it.

And when that happens, security becomes stronger, faster, and more adaptable. Talent becomes more portable and more empowered. Architecture becomes more scalable. And organizations regain their own control over their telemetry.

This shift is long overdue. But it is happening now, and the teams that embrace it will define the next decade of security operations.

Security teams today are drowning in data. Legacy SIEMs and monolithic SOC platforms choke on ever-growing log volumes, giving analysts too many alerts and too little signal. In practice, some organizations ingest terabytes of telemetry per day and see hundreds of thousands of alerts daily, yet roughly two-thirds of alerts go uninvestigated without security data fabrics. Traditional SIEM pricing (by gigabyte or event rate) and static collectors mean escalating bills and blind spots. The result is analyst fatigue, sluggish response, and “data silos” where tools don’t share a common context.

The Legacy SOC Dilemma

Monolithic SOC architectures were built for simpler times. They assume log volume = security, so every source is dumped into one big platform. This “collect-it-all” approach can’t keep up with modern environments. Cloud workloads, IoT/OT networks, and dynamic services churn out exponentially more telemetry, much of it redundant or low-value. Analysts get buried under noise. For example, up to 30% of a SOC analyst’s time can be wasted chasing false positives from undifferentiated data. Meanwhile, scaling a SIEM or XDR to handle that load triggers massive licensing and storage costs.

This architectural stress shows up in real ways: delayed onboarding of new data feeds, rules that can’t keep pace with cloud changes, gaps in compliance data, and “reactive” troubleshooting whenever ingestion spikes. In short, agility and scalability suffer. Security teams are increasingly asked to do more with less – deeper analytics, AI-driven hunting, and 24/7 monitoring – but are hamstrung by rigid, centralized tooling.

Industry Shift: Embracing Composable Architectures

The broader IT world has already swung toward modular, API-driven design, and security is following suit. Analysts note that “the future SOC will not be one large, inflexible platform. It will be a modular architecture built from pipelines, intelligence, analytics, detection, and storage that can be deployed independently and scale as needed”. In other words, SOC stacks are decomposing: SIEM, XDR, SOAR and other components become interchangeable services instead of a single black box. This composable mindset – familiar from microservices and cloud-native design – enables teams to mix best-of-breed tools, swap vendors, and evolve one piece without gutting the entire system.

For example, enterprise apps are moving to cloud-native, service-based platforms (IDC reports ~80% of new apps on microservices.) because monoliths can’t scale. Security is on the same path. By decoupling data collection from analytics, and using standardized data contracts (schemas, APIs), organizations gain flexibility and resilience. A composable SOC can ingest new telemetry streams or adopt advanced AI models without forklift upgrades. It also avoids vendor lock-in: teams “want the freedom to route, store, enrich, analyze, and search without being forced into a single vendor’s path”.

Security Data Fabrics: The Integration Layer

This is where a security data fabric comes in. A data fabric is essentially a unified, virtualized pipeline that connects all parts of the SOC stack. As one expert puts it, a “security data fabric” is an architectural layer for collecting, correlating, and sharing security intelligence across disparate tools and sources in real time. In practice, the security datafabric ingests raw logs and telemetry from every source, applies intelligence and policies, and then forwards the curated streams to SIEMs, XDR platforms, SOAR engines or data lakes as needed. The goal is to ensure every tool has just the right data in the right form.

For example, a data fabric can normalize and enrich events at ingest time (adding consistent tags, schemas or asset info), so downstream tools all operate on the same language. It can also compress and filter data to lower volumes: many teams report cutting 40–70% of their SIEM ingestion by eliminating redundant or low-value. A data fabric typically provides:

• Centralized data bus: All security streams (network flows, endpoint logs, cloud events, etc.) flow through a governed pipeline. This single source of truth prevents silos.

• On-the-fly enrichment and correlation: The fabric can attach context (user IDs, geolocation, threat intel tags) to each event as it arrives, so that SIEM, XDR and SOAR see full context for alerting and response.

• Smart edge processing: The pipeline often pushes intelligence to the collectors. For example, context-aware suppression rules can drop routine, high-frequency logs before they ever traverse the network. Meanwhile micro-indexes are built at the edge for instant lookups, and in-stream enrichment injects critical metadata at source.

• Policy-driven routing: Administrators can define where each event goes. For instance, PCI-compliant logs might be routed to a secure archive, high-priority alerts forwarded to a SIEM or XDR, and raw telemetry for deep analytics sent to a data lake. This “push where needed” model cuts data movement and aligns with compliance.

These capabilities transform a SOC’s data flow. In one illustrative implementation, logs enter the fabric, get parsed and tagged in-stream, and are forked by policy: security-critical events go into the SIEM index, vast bulk archives into cheap object storage, and everything to a searchable data lake for hunting and machine learning. By handling normalization, parsing and even initial threat-scoring in the fabric layer, the SIEM/XDR can focus on analytics instead of housekeeping. Studies show that teams using such data fabrics routinely shrink SIEM ingest by tens of percent without losing visibility – freeing resources for the alerts that really matter.

• Context-aware filtering and index: Fabric nodes can discard or aggregate repetitive noise and build tiny local indexes for fast lookups.
• In-stream enrichment: Tags (asset, user, location, etc.) are added at the source, so downstream tools share a consistent view of the data.
• Governed routing: Policy-driven flows send each event to the optimal destination (SIEM, SOAR playbooks, XDR, cloud archive, etc.).

By architecting the SOC stack this way, teams get resilience and agility. Each component (SIEM engine, XDR module, SOAR workflows, threat-hunting tools) plugs into the fabric rather than relying on point-to-point integrations. New tools can be slotted in (or swapped out) by simply connecting to the common data fabric. This composability also accelerates cloud adoption: for example, AWS Security Lake and other data lake services work as fabric sinks, ingesting contextualized data streams from any collector.

In sum, a security data fabric lets SOC teams control what data flows and where, rather than blindly ingesting everything. The payoffs are significant: faster queries (less noise), lower storage costs, and a more panoramic view of threats. In one case, a firm reduced SIEM data by up to 70% while actually enhancing detection rates, simply by forwarding only security-relevant logs.

Takeaway

Legacy SOC tools equated volume with visibility – but today that approach collapses under scale. Organizations should audit their data pipelines and embrace a composable, fabric-based model. In practice, this means pushing smart logic to collectors (filtering, normalizing, tagging), and routing streams by policy to the right tools. Start by mapping which logs each team actually needs and trimming the rest (many find 50% or more can be diverted away from costly SIEM tiers). Adopt a centralized pipeline layer that feeds your SIEM, XDR, SOAR and data lake in parallel, so each system can be scaled or replaced independently.

The clear, immediate benefit is a leaner, more resilient SOC. By turning data ingestion into a governed, adaptive fabric, security teams can reduce noise and cost, improve analysis speed, and stay flexible – without sacrificing coverage. In short, “move the right data to the right place.” This composable approach lets you add new detection tools or analytics as they emerge, confident that the underlying data fabric will deliver exactly the telemetry you need.