Introducing Cruz: An AI Data Engineer In-a-Box

“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.

The Cost & Compliance Crunch for Indian SOCs

Logs are piling up at 25%+ annual growth, and so are the bills. Indian security teams face a double bind: CERT-In’s directive now mandates 180-day log retention (within India) for compliance, yet storing all that data in a SIEM is prohibitively expensive. Running a SIEM today can feel like paying for every streaming channel 24/7 – even though you only watch a few. SIEM vendors charge by data ingested, so you end up paying for every byte, even the useless noise. It’s no surprise that many enterprises spend crores on SIEM licensing, only to have analysts waste 30% of their time chasing low-value alerts.

“You cannot stop collecting telemetry without creating blind spots, and you cannot keep paying for every byte without draining your budget.”

This catch-22 has left Security Operations Centers (SOCs) struggling. Some try to curb costs by turning off “noisy” data sources (firewalls, DNS, etc.), but that just creates dangerous visibility gaps. Others shorten retention or archive logs offline, but CERT-In’s 180-day rule means dropping data isn’t an option – and retrieving cold archives for an investigation can be painfully slow and costly. The tension is clear: How do you stay compliant and keep full visibility without blowing out your SIEM budget?

Why Traditional Cost-Cutting Falls Short

Typical quick fixes offer only partial relief and introduce new risks:

• Shorter retention periods: Saving less data in SIEM lowers costs but fails compliance audits and hampers investigations. (Six months is the bare minimum now, per CERT-In.)
• Cold archives only: Moving logs out of “hot” SIEM storage saves ingest costs initially, but when you do need those logs, rehydration fees and delays hit hard.
• Dropping noisy sources: Excluding high-volume sources trims volume, but you might miss critical incidents hidden in that data. Blind spots can cripple detection.
• Filtering inside the SIEM: By the time the SIEM discards a log, you’ve already paid to ingest it. Ingest-first, drop-later still racks up the bill for data that provided no security value.

All these measures chip away at the problem without solving it. They force security leaders into an unwinnable choice between cost, compliance, and visibility. What’s needed is a way to ingest everything (to satisfy compliance and visibility) while paying only for what truly matters (to control cost).

A Smarter Middle Path: Databahn’s Intelligent Security Data Pipeline

Instead of sacrificing either logs or budget, forward-thinking teams are turning to Databahn’s intelligent security data pipeline as the connective layer between log sources and the SIEM. This approach keeps every log for compliance but ensures that only the right logs enter your SIEM. By processing data before it hits the SIEM, Databahn ensures high-value, security-relevant events go into premium storage and analytics, while everything else is routed into affordable archives.

Think of it as triage for your telemetry with Databahn at the center:

• Pre-ingestion filtering: Databahn’s AI-powered library of 900+ filtering rules automatically deduplicates, compresses, and drops meaningless data (heartbeats, debug logs, duplicates, etc.) before it ever enters the SIEM. This immediately reduces incoming volume without losing security signal.

• Selective routing: Databahn forks data by value. Critical, security-relevant events stream into your SIEM for real-time detection. Meanwhile, bulk or low-risk logs (needed mainly for compliance or audits) are shunted to cold storage or a data lake. You retain 100% of logs for the required 180 days but only pay SIEM prices for the ones that matter.

• Cold storage compliance: With Databahn, logs that have no immediate security value are automatically routed into low-cost cold storage (cloud or on-prem) designated for compliance. This satisfies CERT-In’s log retention mandate without clogging the SIEM. Importantly, logs remain instantly retrievable for audit or investigation.

• Enrichment & normalization: Databahn enriches and normalizes logs in motion. By the time they hit the SIEM, fewer logs go in but each carries more context. That means streamlined, analysis-ready events instead of raw, noisy telemetry.

Key Outcomes with Databahn:

• 50%+ reduction in SIEM licensing and storage costs (guaranteed minimum savings).
• 900+ out-of-the-box rules cutting noise from day one.
• 100% log retention for 180 days in low-cost storage — ensuring full CERT-In compliance and auditability.

Cutting Costs, Keeping Everything (Proven Results)

This approach fundamentally changes the economics of security data. By aligning cost with value, teams escape the spiral of ever-increasing SIEM bills. In fact, many enterprises achieve 50–70% lower SIEM ingest volumes within weeks, instantly cutting costs in half. Storage footprints shrink as redundant data gets offloaded, often yielding up to 80% savings on storage spend.

Equally important, analysts get relief from alert fatigue. With noisy logs filtered out upstream, the alerts that reach your SOC are fewer but higher fidelity. Teams spend time on real threats, not on torrents of false positives. Compliance is no longer a headache either: every log is still at your fingertips (just in the right place and at the right price). Predictable budgets replace unpredictable spikes, and security leaders no longer have to choose between “spend more” vs. “see less.”

Real-world adopters of this model have reported results like a 60% reduction in daily ingest (saving ₹3+ crore annually) and an 80% log volume reduction in a global deployment – all while maintaining full visibility. The bottom line: SIEM cost reduction and complete visibility are no longer at odds.

“Cut SIEM costs by half and keep every log – it’s now achievable with the right data pipeline strategy.”

Future-Ready, AI-Ready SOC

Beyond immediate savings, a modern data pipeline sets you up for the future. Telemetry volumes will keep growing, and regulations like CERT-In will continue evolving. With an intelligent pipeline in place, your organization can scale and adapt with confidence:

• Need to onboard a new log source? The pipeline can absorb it without ballooning costs.
• Adopting AI-driven analytics? The pipeline’s normalization and context ensure your data is AI-ready out of the gate.
• Changing SIEM vendor or moving to a cloud-native stack? Simply re-point the pipeline – you’re not locked in by where your data lives.

In short, pipeline-driven architectures make your SOC more agile, compliant, and cost-efficient. They turn security data management from a bottleneck into a competitive advantage.

The Bottom Line: Compliance and Cost Savings, No Compromise

Indian enterprises no longer have to choose between meeting CERT-In compliance and controlling SIEM costs. By filtering and routing logs intelligently, you guarantee >50% savings on SIEM and storage spend while retaining 100% of your data for the required 180 days (and beyond). This means no blind spots, no compliance gaps, and no surprise bills – just a leaner, smarter way to handle security telemetry.

Ready to see how this works in practice for your organization? Book a demo now to see it in action.