The latest articles, news, blogs and learnings from Databahn
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.
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.
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”.
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:
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.
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.
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.
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?
Typical quick fixes offer only partial relief and introduce new risks:
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).
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:
Key Outcomes with Databahn:
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.”
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:
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.
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.
The world’s data footprint is growing at an astonishing pace – by 2025 we will generate roughly 181 zettabytes of data per year (about 1.45 trillion gigabytes per day). This data deluge spans every device, cloud, and edge node, creating rich insights but also multiplying security and compliance challenges. In such a vast, distributed environment, relying on manual audits and static configurations is no longer tenable. Security teams face a simple fact: as networks grow in size and diversity (cloud, IoT, remote users), traditional perimeter defenses and hand‐crafted rules struggle to keep up. The stakes are high – costly breaches continue to occur when policies lapse. For example, the Equifax breach in 2017 exposed personal information for roughly 147 million people , and Uber’s 2016 hack compromised data for 57 million users. In each case, inconsistent enforcement of data‐handling policies contributed to the problem.
Security and compliance at enterprise scale suffer from several interlocking problems. First, data volume and diversity are exploding. Millions of new devices, microservices, and data flows appear each year (IoT alone will generate nearly half of new data). Second, misconfigurations and human error remain rampant: industry reports find that roughly 80% of security exposures stem from misconfigured credentials or policies. A single missing firewall rule or forgotten configuration – as one incident dubbed “the breach that never happened” illustrates – can linger quietly and eventually enable attackers to slip past defenses. Third, regulatory demands are multiplying. Organizations must simultaneously satisfy frameworks like PCI-DSS, HIPAA, GDPR, and NIST, each requiring specific technical controls (segmentation, encryption, logging, etc.) on a tight schedule. Auditors expect continuous evidence that policies are enforced everywhere across on-premises and cloud networks. In practice, many teams find they lack real-time visibility into policy compliance.
Conventional approaches – shipping everything to a central SIEM or relying on annual audits – simply can’t keep up. When policies are defined in documents rather than machines, enforcement is reactive and errors slip through. The result is “compliance by happenstance” and ever-growing risk.
A policy-driven security fabric is an architectural approach that embeds security and compliance policies directly into the network and data infrastructure, enforcing them automatically and uniformly at scale. Instead of relying on manually configured devices or point tools, a security fabric uses centralized policy definitions that propagate to every relevant element (switch, cloud service, endpoint, etc.) in real time. Key features include:
Unlike legacy systems that “shoot arrows and hope,” a policy-driven fabric automates the chain of trust. When a new application or device comes online, it automatically inherits the relevant policies (for encryption, retention, access, etc.) without manual setup. If a compliance rule changes (e.g. a new data-retention requirement), updating the central policy cascades the change network-wide. This ensures continuous compliance by design.
The move toward policy-driven security fabrics parallels several industry trends:
Real-world incidents highlight why the industry needs this approach. The Equifax breach and Uber cover-up both stemmed from policy gaps. In Uber’s case, hackers stole credentials and exfiltrated data on 57 million users; the company even paid the ransom quietly rather than reporting it. Had a policy-driven fabric been in place (for example, automatically logging and alerting on unauthorized data exfiltration, or enforcing stricter segmentation around customer data), the breach could have been detected or contained sooner. In Equifax’s case, attackers exploited outdated software (no security patch policy) and made off with 147 million records. Today, regulators explicitly require robust patching, encryption, and data-minimization policies – mandates that are easier to meet with automation.
Many organizations are already putting these ideas into practice:
These examples illustrate a common outcome: faster, more reliable compliance. By treating policies as code and applying them uniformly, organizations turn audit prep from a panic-driven scramble into an ongoing automated process.
Implementing a policy-driven fabric requires collaboration between security, network, and compliance teams. Key steps include:
In practice, this often means moving from a checklist mentality (“do we have X control?”) to an architecture where security and compliance are built from the start. Instead of patchy patch management or ad hoc segmentation, the network itself becomes “aware” of compliance constraints.
As data and networks scale to unprecedented levels, manual compliance is a lost cause. A policy-driven security fabric offers a transformative path forward: it embeds compliance into the architecture so that policy enforcement is automatic, continuous, and verifiable. The outcome is security at scale – fewer configuration errors, faster responses, and demonstrable audit trails.
Enterprises that embrace this approach find that compliance can shift from being a cost center to a trust builder. By codifying and automating policies, organizations reduce risk (breaches and fines), save time on audits, and free security teams to focus on strategic defense rather than firefighting. In a world of exploding data and tightening regulations, a policy-driven fabric isn’t just a nice-to-have – it’s the foundation of scalable, future-proof security.
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Teams running a Managed Security Service (MSS) are getting overwhelmed with the complexity of growth. Every new customer adds another SIEM, another region, another compliance regime – and delivers another sleepless night for your operations team.
Across the industry, managed security service providers (MSSPs) are discovering the same truth: the cost of complexity grows faster than the revenue it earns. Every tenant brings its own ingestion rules, detection logic, storage geography, and compliance boundaries. What once made sense for ten customers begins to collapse under the weight of 15, 25, and 40 customers.
This is not a technology failure; it’s an architectural mismatch. MSSPs must contend with and operate multiple platforms and pipelines not generally designed or built for multi-tenancy. They must engage with telemetry architecture that is meant to centralize many sources into a single SIEM, and create ways to federate, manage, and streamline security telemetry in a way that enables SOC operations for multiple users.
For most providers, tenant growth directly maps to operational sprawl. Each client has unique SIEM requirements, volume tiers, and compliance needs. Each requires custom integrations, schema alignment, and endless maintenance.
Three familiar challenges emerge:
It’s the hidden tax of being a multi-tenant provider without a true multi-tenant architecture.
Modern MSSPs need a control model that scales trust, not toil. They need a structured, infrastructure-driven way to give every tenant autonomy while maintaining centralized intelligence and oversight. We’ve built it, and we call it the Beacon Architecture.
At the heart of the Beacon Architecture is a single, federated control plane that can govern hundreds of isolated data planes below it. Each tenant operates independently with its own routing logic, volume policies, and SIEM integrations, yet all inherit global policies, monitoring, and governance from the Beacon.
The idea is simple: building a system that balances the requirement of guiding every tenant’s telemetry in a way that optimizes for tenant control while enabling centralized governance and management. This isn’t a tweak to traditional data routing; it’s a fundamental redesign around five principles:
Each tenant runs its own fully contained data plane – not as a workspace carved out of shared infrastructure. That means you can apply tailored enrichment, normalization, and reduction rules without cross-contamination or schema drift across tenants. Isolation protects autonomy, but the Beacon ensures every tenant still adheres to a consistent governance baseline.
Operationalizing this requires tagging data at the edge of the collection infrastructure, enabling centralized governance systems to isolate data planes based on these tags.
Instead of building custom pipelines and collection infrastructure for every client, MSSPs can define policy templates for each tenant and deploy them across existing integrations to deploy faster and with much lower effort.
A financial services customer in Singapore? Route and store PII for this client in local cloud systems for compliance.
A healthcare customer in Texas? Apply HIPAA-aligned masking at the edge before ingestion.
Tagging and applying policies for PII at the edge will help MSSPs ensure compliance with data localization and PII norms for customers.
The Beacon provides end-to-end observability – data lineage, drift alerts, pipeline health – across all tenants in a single pane of glass. MSSP operators can now easily track, monitor, and manage data movement. When a customer’s schema changes or a connector stalls, it’s detected automatically and surfaced for approval before it affects operations. It’s the difference between reactive monitoring and proactive assurance.
Leverage a mesh architecture to ensure resiliency and scalability, while utilizing agentic AI to proactively detect problems and errors more quickly.
Adding a tenant no longer means adding infrastructure. With a control plane that can spin up isolated data planes on demand, MSSPs can onboard new customers, regions, or sub-brands within hours, not weeks – with zero code duplication. Policy templates and pre-built connectors – including support for different destinations such as SIEMs, SOARs, data lakes, UEBAs, and observability tools – ensures seamless data movement.
Add new tenants through a fast, simple, and flexible process that helps MSSPs focus on providing services and customizations, not on repetitive data engineering.
With isolation and governance handled, MSSPs can now leverage anonymized telemetry patterns across tenants to identify shared threat trends – safely. This federated analytics layer transforms raw, siloed telemetry into contextual knowledge across the portfolio without exposing any customer’s data.
Anonymized pattern tracking to improve security outcomes without adding to the threat surface, thereby growing trust with customers without incurring prohibitively high costs.
Most MSSPs grow linearly; the cost and effort involved in onboarding each new customer constrain expansion and act as a bottleneck. With the bottleneck, the Beacon Architecture lets MSSPs grow exponentially. When operational effort is decoupled from tenant count, every new customer adds value – not workload.
The outcomes are measurable:
When these efficiencies compound across dozens or hundreds of tenants, the economics change completely: lower engineering overhead, predictable cost-to-serve, and capacity to onboard more customers with the same team, and being able to allocate more bandwidth to strategic security instead of data engineering plumbing.
Data sovereignty no longer necessitates the creation of separate environments. By tagging and routing data according to policy, MSSPs can automatically enforce where telemetry lives, which region processes it, and which SIEM consumes it. With Beacon, you can also add logic and rules to route less-relevant data to the right data lake and storage endpoint.
PII detection and masking happen at the edge – before data ever crosses borders – giving MSSPs fine-grained control over localization, privacy, and retention. This will enable MSSPs to simplify serving multinational clients or entering new markets without needing to engineer solutions for local compliance.
In other words: compliance becomes an attribute of the pipeline, not an afterthought of storage.
Every MSSP advertises 24x7 vigilance; few can actually deliver it at the data layer. Most MSSPs use complex workflows, relying on processes, systems, and human expertise to serve their clients. When new sources need to be added, pipelines break, or schemas shift, the tech debt increases, putting pressure on their entire business and operations.
With self-healing pipelines, automated schema-drift detection, lineage tracking across every route, and simplified no-code source addition, the Beacon Architecture provides the foundation to actually guarantee the kind of always-on vigilance fast-moving businesses need.
Engineers can see – and prove – that every event was collected, transformed, enriched, and delivered successfully. MSSPs and their clients can even measure their data coverage against security frameworks and baselines such as MITRE ATT&CK. These features become a differentiator in client renewals, audits, and compliance assessments.
When data is structured, governed, and trusted, it becomes teachable. The same architecture that isolates tenants today can fuel intelligent, cross-tenant analytics tomorrow – from AI-assisted threat correlation to federated reasoning models that learn from patterns across the entire managed estate.
That evolution – from managing tenants to managing intelligence – is where the next wave of MSSP competitiveness will play out.
Enterprises running multiple SIEMs across geographies face the same structural problems as MSSPs: fragmented visibility, inconsistent compliance, and duplicated effort. The Beacon model applies equally well here – CISOs operating multiple SIEMs across geographies can push compliance filtering and policies from the edge, ensuring seamless operations. Each business unit, region, or SOC can maintain its preferred SIEM while the organization gains a unified governance and observability layer – plus the freedom to evaluate or migrate between SIEMs without re-engineering the whole data pipeline.
Beacon Architecture isn’t just a new way to route data – it’s a new way to think about data ownership, autonomy, and assurance in managed security operations. It replaces replication with reuse, fragmentation with federation, and manual oversight with intelligent control. Every MSSP that adopts it moves one step closer to solving the fundamental equation of scale: how to ensure quality operations while adding customers without growing their cost base. They can achieve this by handling more data, and doing so intelligently.
Multi-tenancy isn’t about hosting more customers. It’s about hosting more confidence.
The MSSPs that master federated control today will define the managed security ecosystem tomorrow – guiding hundreds of tenants with the precision, predictability, and intelligence of a single Beacon.
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Every SOC depends on clear, actionable security event logs, but the drive for richer visibility often collides with the reality of ballooning security log volume.
Each new detection model or compliance requirement demands more context inside those security logs – more attributes, more correlations, more metadata stitched across systems. It feels necessary: better-structured security event logs should make analysts faster and more confident.
So teams continue enriching. More lookups, more tags, more joins. And for a while, enriched security logs do make dashboards cleaner and investigations more dynamic.
Until they don’t. Suddenly ingestion spikes, storage costs surge, queries slow, and pipelines become brittle. The very effort to improve security event logs becomes the source of operational drag.
This is the paradox of modern security telemetry: the more intelligence you embed in your security logs, the more complex – and costly – they become to manage.
Security operations once had a simple relationship with data — collect, store, search.
But as threats evolved, so did telemetry. Enrichment pipelines began adding metadata from CMDBs, identity stores, EDR platforms, and asset inventories. The result was richer security logs but also heavier pipelines that cost more to move, store, and query.
The problem isn’t the intention to enrich; it’s the assumption that context must always travel with the data.
Every enrichment field added at ingest is replicated across every event, multiplying storage and query costs. Multiply that by thousands of devices and constant schema evolution, and enrichment stops being a force multiplier; it becomes a generator of noise.
Teams often respond by trimming retention windows or reducing data granularity, which helps costs but hurts detection coverage. Others try to push enrichment earlier at the edge, a move that sounds efficient until it isn’t.
Most organizations enrich at the ingest layer, adding hostnames, geolocation, or identity tags to logs as they enter a SIEM or data platform. It feels efficient, but at scale it’s where volume begins to spiral. Every added field replicates millions of times, and what was meant to make data smarter ends up making it heavier.
The issue isn’t enrichment, it’s how rigidly most teams apply it.
Instead of binding context to every raw event at source, modern teams are moving toward adaptive enrichment, a model where context is linked and referenced, not constantly duplicated.
This is where agentic automation changes the enrichment pattern. AI-driven data agents, like Cruz, can learn what context actually adds analytical value, enrich only when necessary, and retain semantic links instead of static fields.
The result is the same visibility, far less noise, and pipelines that stay efficient even as data models and detection logic evolve.
In short, the goal isn’t to enrich everything faster. It’s to enrich smarter — letting context live where it’s most impactful, not where it’s easiest to apply.
In legacy pipelines, enrichment is a static process. Rules are predefined, transformations are rigid, and every event that matches a condition gets the same expanded schema.
But context isn’t static.
Asset ownership changes. Threat models evolve. A user’s role might shift between departments, altering the meaning of their access logs overnight.
A static enrichment model can’t keep up, it either lags behind or floods the system with stale attributes.
A dynamic enrichment architecture treats context as a living layer rather than a stored attribute. Instead of embedding every data point into every security log, it builds relationships — lightweight references between data entities that can be resolved on demand.
Think of it as context caching: pipelines tag logs with lightweight identifiers and resolve details only when needed. This approach doesn’t just cut cost, it preserves contextual integrity. Analysts can trust that what they see reflects the latest known state, not an outdated enrichment rule from last quarter.
When context is over-applied, it doesn’t just bloat data — it skews analytics.
Correlation engines begin treating repeated metadata as signals. That rising noise floor buries high-fidelity detections under patterns that look relevant but aren’t.
Detection logic slows down. Query times stretch. Mean time to respond increases.
Adaptive enrichment, in contrast, allows the analytics layer to focus on relationships instead of repetition. By referencing context dynamically, queries run faster and correlation logic becomes more precise, operating on true signal, not replicated metadata.
This becomes especially relevant for SOCs experimenting with AI-assisted triage or LLM-powered investigation tools. Those models thrive on semantically linked data, not redundant payloads.
If the future of SOC analytics is intelligent automation, then data enrichment has to become intelligent too.
The urgency is no longer hypothetical.
Security data platforms are entering a new phase of stress. The move to cloud-native architectures, the rise of identity-first security, and the integration of observability data into SIEM pipelines have made enrichment logic both more critical and more fragile.
Each system now produces its own definition of context, endpoint, identity, network, and application telemetry all speak different schemas. Without a unifying approach, enrichment becomes a patchwork of transformations, each one slightly out of sync.
The result? Gaps in detection coverage, inconsistent normalization, and a steady growth of “dark data” — security event logs so inflated or malformed that they’re excluded from active analysis.
A smarter enrichment strategy doesn’t just cut cost; it restores semantic cohesion — the shared meaning across security data that makes analytics work at all.
Adaptive enrichment becomes achievable when pipelines themselves learn.
Instead of following static transformation rules, agents observe how data is used and evolve the enrichment logic accordingly.
For example:
This transforms enrichment from a one-time process into a self-correcting system, one that continuously balances fidelity, performance, and cost.
In the next few years, CISOs and data leaders will face a deeper reckoning with their telemetry strategies.
Data volume will keep climbing. AI-assisted investigations will demand cleaner, semantically aligned data. And cost pressures will force teams to rethink not just where data lives, but how meaning is managed.
The future of enrichment isn’t about adding more fields.
It’s about building systems that understand when and why context matters, and applying it with precision rather than abundance.
By shifting from rigid enrichment at ingest to adaptive, agentic enrichment across the pipeline, enterprises gain three crucial advantages:
This is not a call to collect less — it’s a call to collect more wisely.
At Databahn, this philosophy is built into how the platform treats data pipelines, not as static pathways, but as adaptive systems that learn. Our agentic data layer operates across the pipeline, enriching context dynamically and linking entities without multiplying volume. It allows enterprises to unify security and observability data without sacrificing control, performance, or cost predictability.
In modern security, visibility isn’t about how much data you collect — it’s about how intelligently that data learns to describe itself.
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Security teams today are truly feeling alert fatigue in cybersecurity. Legacy SIEMs and point tools spit out tons of notifications, many of them low-priority or redundant. Analysts are often overwhelmed by a noisy tsunami of alerts from outdated pipelines. When critical alerts are buried under a flood of false positives, they can easily be missed — sometimes until it’s too late. The result is exhausted analysts, blown budgets, and dangerous gaps in protection. Simply throwing more alerts at the wall won’t help. Instead, alerting must become smarter and integrated across the entire data flow.
Traditional alerting is breaking under modern scale. Today’s SOCs juggle dozens of tools and 50–140 data sources (source). Each might generate its own alarms. Without a unified system, these silos create confusion and operational blind spots. For example, expired API credentials or a collector crash can stop log flows entirely, with no alarms triggered until an unrelated investigation finally uncovers the gap. Even perfect detection rules don’t fire if the logs never make it in or are corrupted silently.
Traditional monitoring stacks often leave SOCs blind. Alert fatigue in cybersecurity is built on disconnected alerts from devices, collectors, and analytic tools that create noise and gaps. For many organizations, visibility is the problem: thousands of devices and services are producing logs, but teams can’t track their health or data quality. Static inventories mean unknown devices slip through the cracks; unanalyzed logs clog the system. Siloed alert pipelines only worsen this. For instance, a failed log parser may simply drop fields silently — incident response only discovers it later when dashboards go dark. By the time someone notices a broken widget, attackers may have been active unnoticed.
Cybersecurity alert fatigue is part of this breakdown. Analysts bombarded with hundreds of alerts per hour inevitably become desensitized. Time spent investigating low-value alarms is time not spent on real incidents. Diverting staff to chasing trivial alerts directly worsens MTTD (Mean Time to Detect) and MTTR (Mean Time to Respond) for genuine threats. In practice, studies show most organizations struggle to keep up — 70% say they can’t handle their alert volume (source). The danger is that attacks or insider issues silently slip by under all that noise. In short, fragmented alerting slows response and increases risk rather than preventing it.
Implementing an intelligent, unified alerting framework brings concrete benefits:
By bringing these advantages together, unified alerting can truly change the game. Security teams are no longer scrambling to stitch together disconnected signals; instead, they operate on real-time, actionable intelligence. In one customer implementation, unified alerting led to a 50% reduction in alert noise and 40% improvement in mean time to resolution (source).
The power of smarter alerts is best seen in examples:
These real-world examples show how alerting should work: catching the problem upstream, with clear context. Detection engineering is only as strong as your data pipeline. If the pipeline fails, your alerts fail too. Robust monitoring of the pipeline itself is therefore as critical as detection rules.
The way forward is clear: don’t just add more alerts, get smarter about them. Modern SOCs need an alerting framework that is integrated, intelligent, and end-to-end. That means covering every part of your data pipeline — from device agents to analytics — under a single umbrella. It means correlating related events and routing them to the right people. And it means proactive, AI-driven checks so that problems are fixed before they cause trouble.
The payoff is huge. With unified alerting, security teams gain faster detection of real issues, fewer distractions from noise, and dramatic reductions in troubleshooting time. This approach yields fewer outages, faster recovery, and operational clarity. In other words, it helps SOCs scale safely and keep up with today’s complex environments.
Work smarter, not harder. By modernizing your alert pipelines, you turn alerting from an endless chore into a true force multiplier — empowering your team to focus on what really matters.
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For more than a decade, a handful of technical giants have been the invisible gravity that holds the digital world together. Together, they power over half of the world’s cloud workloads with Amazon S3 alone peaking at nearly 1 petabyte per second in bandwidth. With average uptimes measured at 99.999% and data centers spanning every continent, these clouds have made reliability feel almost ordinary.
When you order a meal, book a flight, or send a message, you don’t wonder where the data lives. You expect it to work – instantly, everywhere, all the time. That’s the brilliance, and the paradox, of hyperscale computing: the better it gets, the less we remember it’s there.
So, when two giants falter, the world didn’t just face downtime – it felt disconnected from its digital heartbeat. Snapchat went silent. Coinbase froze. Heathrow check-ins halted. Webflow blinked out.
And Meredith Whittaker, the President of Signal, reminded the internet in a now-viral post, “There are only a handful of entities on Earth capable of offering the kind of global infrastructure a service like Signal requires.”
She’s right, and that’s precisely the problem. If so much of the world runs on so few providers, what happens when the sky blinks?
In this piece, we’ll explore what the recent AWS and Azure outages teach us about dependency, and why multi-cloud resilience may be the only way forward. And how doing it right requires re-thinking how enterprises design for continuity itself.
For global enterprises, only three cloud providers on the planet – AWS, Azure, and Google Cloud – offer true global reach with the compliance, scale, and performance demanded by millions of concurrent users and devices.
Their dominance wasn’t luck; it was engineered. Over the past decade, these hyperscalers built astonishingly resilient systems with unmatched global reach, distributing workloads across regions, synchronizing backups between data centers, and making downtime feel mythical.
As these three providers grew, they didn’t just sell compute – they sold confidence. The pitch to enterprises was simple: stay within our ecosystem, and you’ll never go down. To prove it, they built seamless multi-region replication, allowing workloads and databases to mirror across geographies in real time. A failover in Oregon could instantly shift to Virginia; a backup in Singapore could keep services running if Tokyo stumbled. Multi-region became both a technological marvel and a marketing assurance – proof that a single-cloud strategy could deliver global continuity without the complexity of managing multiple vendors.
That’s why multi-region architecture became the de facto safety net. Within a single cloud system, creating secondary zones and failover systems was a simple, cost-effective, and largely automated process. For most organizations, it was the rational resilient architecture. For a decade, it worked beautifully.
Until this October.
The AWS and Azure outages didn’t start in a data center or a regional cluster. They began in the global orchestration layers – the digital data traffic control systems that manage routing, authentication, and coordination across every region. When those systems blinked, every dependent region blinked with them.
Essentially, the same architecture that made cloud redundancy easy also created a dependency that no customer of these three service providers can escape. As Meredith Whittaker added in her post, “Cloud infrastructure is a choke point for the entire digital ecosystem.”
Her words capture the uncomfortable truth that the strength of cloud infrastructure – its globe-straddling, unifying scale – has become its vulnerability. Control-plane failures have happened before, but they were rare enough and systems recovered fast enough that single-vendor, multi-region strategies felt sufficient. The events of October changed that calculus. Even the global scaffolding of these global cloud providers can falter – and when it does, no amount of intra-cloud redundancy can substitute for independence.
If multi-region resilience can no longer guarantee uptime, the next evolution isn’t redundancy; it is reinvention. Multi-cloud resilience – not as a buzzword, but as a design discipline that treats portability, data liquidity, and provider-agnostic uptime as first-class principles of modern architecture.
For years, multi-cloud has been the white whale of IT strategy – admired from afar, rarely captured. The premise was simple: distribute workloads across providers to minimize risk, prevent downtime, and avoid over-reliance on a single vendor.
The challenge was never conviction – it was complexity. Because true multi-cloud isn’t just about having backups elsewhere – it’s about keeping two living systems in sync.
Every transaction, every log, every user action must decide: Do I replicate this now or later? To which system? In what format? When one cloud slows or fails, automation must not only redirect workloads but also determine what state of data to recover, when to switch back, and how to avoid conflicts when both sides come online again.
The system needs to determine which version of a record is authoritative, how to maintain integrity during mid-flight transactions, and how to ensure compliance when one region’s laws differ from those of another. Testing these scenarios is notoriously difficult. Simulating a global outage can disrupt production; not testing leaves blind spots.
This is why multi-cloud used to be a luxury reserved for a few technology giants with large engineering teams. For everyone else, the math – and the risk – didn’t work.
Cloud’s rise, after all, was powered by convenience. AWS, Azure, and Google Cloud offered a unified ecosystem where scale, replication, and resilience were built in. They let engineering teams move faster by outsourcing undifferentiated heavy lifting – from storage and security to global networking. Within those walls, resilience felt like a solved problem.
Due to this complexity and convenience, single-vendor multi-region architectures have become the gold standard. They were cost-effective, automated, and easy to manage. The architecture made sense – until it didn’t.
The October outages revealed the blind spot. And that is where the conversation shifts.
This isn’t about distrust in cloud vendors – their reliability remains extraordinary. It’s about responsible risk management in a world where that reliability can no longer be assumed as absolute.
Forward-looking leaders are now asking a new question:
Can emerging technologies finally make multi-cloud feasible – not as a hedge, but as a new standard for resilience?
That’s the opportunity. To transform what was once an engineering burden into a business imperative – to use automation, data fabrics, and AI-assisted operations to not just distribute workloads, but to create enterprise-grade confidence.
Modern enterprises don’t just run on data: they run on uninterrupted access to it.
In a world where customers expect every transaction, login, and workflow to be instantaneous, resilience has become the most accurate measure of trust.
That’s why multi-cloud matters. It’s the only architectural model that promises “always-up” systems – platforms capable of staying operational even when a primary cloud provider experiences disruption. By distributing workloads, data, and control across multiple providers, enterprises can insulate their business from global outages and deliver the reliability their customers already expect to be guaranteed. It would put enterprises back in the driver’s seat on their systems, rather than leaving them vulnerable to provider failures.
The question is no longer whether multi-cloud is desirable, but how it can be achieved without increasing complexity to the extent of making it unfeasible. Enterprises that succeed tend to follow five foundational principles – pragmatic guardrails for transforming resilience into a lasting architecture.
When these five principles converge, resilience stops being reactive and becomes a design property of the enterprise itself. It turns multi-cloud from an engineering aspiration into a business continuity strategy – one that keeps critical services available, customer trust intact, and the brand’s promise uninterrupted.
Not long ago, multi-cloud resilience was a privilege reserved for the few – projects measured in years, not quarters.
Coca-Cola began its multi-cloud transformation around 2017, building a governance and management system that could span AWS, Azure, and Google Cloud. It took years of integration and cost optimization for the company to achieve unified visibility across its environments.
Goldman Sachs followed, extending its cloud footprint from AWS into Google Cloud by 2019, balancing trading workloads on one platform with data analytics and machine learning on another. Their multi-cloud evolution unfolded gradually through 2023, aligning high-performance finance systems with specialized AI infrastructure.
In Japan, Mizuho Financial Group launched its multi-cloud modernization initiative in 2020, achieving strict financial-sector compliance while reducing server build time by nearly 80 percent by 2022.
Each of these enterprises demonstrated the principle: true continuity and flexibility are possible, but historically only through multi-year engineering programs, deep vendor collaboration, and substantial internal bandwidth.
That equation is evolving. Advances in AI, automation, and unified data fabrics now make the kind of resilience these pioneers sought achievable in a fraction of the time – without rebuilding every system from scratch.
Modern platforms like Databahn represent this shift, enabling enterprises to seamlessly orchestrate, move, and analyze data across clouds. They transform multi-cloud from merely an infrastructure concern into an intelligence layer – one that detects disruptions, adapts automatically, and keeps the enterprise operational even when the clouds above encounter issues.
Every outage leaves a lesson in its wake. The October disruptions made one thing unmistakably clear: even the best-engineered clouds are not immune to failure.
For enterprises that live and breathe digital uptime, resilience can no longer be delegated — it must be designed.
And at the heart of that design lies data. Not just stored or secured, but liquid – continuously available, intelligently replicated, and ready to flow wherever it’s needed.
Liquid data powers cross-cloud recovery, real-time visibility, and adaptive systems that think and react faster than disruptions.
That’s the future of enterprise architecture: always-on systems built not around a single provider, but around intelligent fabrics that keep operations alive through uncertainty.
It’s how responsible leaders will measure resilience in the next decade – not by the cloud they choose, but by the continuity they guarantee.
At Databahn, we believe that liquid data is the defining resource of the 21st century – both the foundation of AI and the reporting layer that drives the world’s most critical business decisions. We help enterprises control and own their data in the most resilient and fault-tolerant way possible.
Did the recent outages impact you? Are you looking to make your systems multi-cloud, resilient, and future-proof? Get in touch and let’s see if a multi-cloud system is worthwhile for you.
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A Security Information and Event Management (SIEM) system aggregates logs and security events from across an organization’s IT infrastructure. It correlates and analyzes data in real time, using built-in rules, analytics, and threat intelligence to identify anomalies and attacks as they happen. SIEMs provide dashboards, alerts, and reports that help security teams respond quickly to incidents and satisfy compliance requirements. In essence, a SIEM acts as a central security dashboard, giving analysts a unified view of events and threats across their environment.
Pros of SIEM:
Cons of SIEM:
A security data lake is a centralized big-data repository (often cloud-based) designed to store and analyze vast amounts of security-related data in its raw form. It collects logs, network traffic captures, alerts, endpoint telemetry, threat intelligence feeds, and more, without enforcing a strict schema on ingestion. Using schema-on-read, analysts can run SQL queries, full-text searches, machine learning, and AI algorithms on this raw data. Data lakes can scale to petabytes, making it possible to retain years of data for forensic analysis.
Pros of Data Lakes:
Cons of Data Lakes:
Rather than choosing one side, many security teams adopt a hybrid architecture that uses both SIEM and data lake capabilities. Often called a “security data fabric,” this strategy decouples data collection, storage, and analysis into flexible layers. For example:
This modular security data fabric is an emerging industry best practice. It helps organizations avoid vendor lock-in and balance cost with capability. For instance, by filtering out irrelevant data, the SIEM can operate leaner and more accurately. Meanwhile, threat hunters gain access to the complete historical dataset in the lake.
Every organization’s needs differ. A full-featured SIEM might be sufficient for smaller environments or for teams that prioritize quick alerting and compliance out-of-the-box. Large enterprises or those with very high data volumes often need data lake capabilities to scale analytics and run advanced machine learning. In practice, many CISOs opt for a combined approach: maintain a core SIEM for active monitoring and use a security data lake for additional storage and insights.
Key factors include data volume, regulatory requirements, budget, and team expertise. Data lakes can dramatically reduce storage costs and enable new analytics, but they require dedicated data engineering and governance. SIEMs provide mature detection features and reporting, but can become costly and complex at scale. A hybrid “data fabric” lets you balance these trade-offs and future-proof the security stack.
At the end of the day, rethinking SIEM doesn’t necessarily mean replacing it. It means integrating SIEM tools with big-data analytics in a unified way. By leveraging both technologies — the immediate threat detection of SIEM and the scalable depth of data lakes — security teams can build a more flexible, robust analytics platform.
Ready to modernize your security analytics? Book a demo with Databahn to see how a unified security data fabric can streamline threat detection and response across your organization.
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For years, data governance has been synonymous with gatekeeping. Enterprises set up permissions, role-based access controls, and policy checklists to ensure the right people had the right access to the right data. Compliance meant defining who could see customer records, how long logs were retained, and what data could leave the premises. This access-centric model worked in a simpler era – it put up fences and locks around data. But it did little to improve the quality, context, or agility of data itself. Governance in this traditional sense was about restriction more than optimization. As long as data was stored and accessed properly, the governance box was checked.
However, simply controlling access doesn’t guarantee that data is usable, accurate, or safe in practice. Issues like data quality, schema changes, or hidden sensitive information often went undetected until after the fact. A user might have permission to access a dataset, but if that dataset is full of errors or policy violations (e.g. unmasked personal data), traditional governance frameworks offer no immediate remedy. The cracks in the old model are growing more visible as organizations deal with modern data challenges.
Today’s data environment is defined by velocity, variety, and volume. Rigid governance frameworks are struggling to keep up. Several pain points illustrate why the old access-based model is reaching a breaking point:
Unmanageable Scale: Data growth has outpaced human capacity. Firehoses of telemetry, transactions, and events are pouring in from cloud apps, IoT devices, and more. Manually reviewing and updating rules for every new source or change is untenable. In fact, every new log source or data format adds more drag to the system – analysts end up chasing false positives from mis-parsed fields, compliance teams wrestle with unmasked sensitive data, and engineers spend hours firefighting schema drift. Scaling governance by simply throwing more people at the problem no longer works.
Constant Change (Schema Drift): Data is not static. Formats evolve, new fields appear, APIs change, and schemas drift over time. Traditional pipelines operating on “do exactly what you’re told” logic will quietly fail when an expected field is missing or a new log format arrives. By the time humans notice the broken schema, hours or days of bad data may have accumulated. Governance based on static rules can’t react to these fast-moving changes.
Reactive Compliance: In many organizations, compliance checks happen after data is already collected and stored. Without enforcement woven into the pipeline, sensitive data can slip into the wrong systems or go unmasked in transit. Teams are then stuck auditing and cleaning up after the fact instead of controlling exposure at the source. This reactive posture not only increases legal risk but also means governance is always a step behind the data. As one industry leader put it, “moving too fast without solid data governance is exactly why many AI and analytics initiatives ultimately fail”.
Operational Overhead: Legacy governance often relies on manual effort and constant oversight. Someone has to update access lists, write new parser scripts, patch broken ETL jobs, and double-check compliance on each dataset. These manual processes introduce latency at every step. Each time a format changes or a quality issue arises, downstream analytics suffer delays as humans scramble to patch pipelines. It’s no surprise that analysts and engineers end up spending over 50% of their time fighting data issues instead of delivering insights. This drag on productivity is unsustainable.
Rising Costs & Noise: When governance doesn’t intelligently filter or prioritize data, everything gets collected “just in case.” The result is mountains of low-value logs stored in expensive platforms, driving up SIEM licensing and cloud storage costs. Security teams drown in noisy alerts because the pipeline isn’t smart enough to distinguish signal from noise. For example, trivial heartbeat logs or duplicates continue flowing into analytics tools, adding cost without adding value. Traditional governance has no mechanism to optimize data volumes – it was never designed for cost-efficiency, only control.
The old model of governance is cracking under the pressure. Access controls and check-the-box policies can’t cope with dynamic, high-volume data. The status quo leaves organizations with blind spots and reactive fixes: false alerts from bad data, sensitive fields slipping through unmasked, and engineers in a constant firefight to patch leaks. These issues demand excessive manual effort and leave little time for innovation. Clearly, a new approach is needed – one that doesn’t just control data access, but actively manages data quality, compliance, and context at scale.
What would it look like if data governance were proactive and intelligent instead of reactive and manual? Enter the world of agentic data governance – where intelligent agents imbued in the data pipeline itself take on the tasks of enforcing policies, correcting errors, and optimizing data flow autonomously. This shift is as radical as it sounds: moving from static rules to living, learning systems that govern data in real time.
Instead of simply access management, the focus shifts to agency – giving the data pipeline itself the ability to act. Traditional automation can execute predefined steps, but it “waits” for something to break or for a human to trigger a script. In contrast, an agentic system learns from patterns, anticipates issues, and makes informed decisions on the fly. It’s the difference between a security guard who follows a checklist and an analyst who can think and adapt. With intelligent agents, data governance becomes an active process: the system doesn’t need to wait for a human to notice a compliance violation or a broken schema – it handles those situations in real time.
Consider a simple example of this autonomy in action. In a legacy pipeline, if a data source adds a new field or changes its format, the downstream process would typically fail silently – dropping the field or halting ingestion – until an engineer debugs it hours later. During that window, you’d have missing or malformed data and maybe missed alerts. Now imagine an intelligent agent in that pipeline: it recognizes the schema change before it breaks anything, maps the new field against known patterns, and automatically updates the parsing logic to accommodate it. No manual intervention, no lost data, no blind spots. That is the leap from automation to true autonomy – predicting and preventing failures rather than merely reacting to them.
This new paradigm doesn’t just prevent errors; it builds trust. When your governance processes can monitor themselves, fix issues, and log every decision along the way, you gain confidence that your data is complete, consistent, and compliant. For security teams, it means the data feeding their alerts and reports is reliable, not full of unseen gaps. For compliance officers, it means controls are enforced continuously, not just at periodic checkpoints. And for data engineers, it means a lot less 3 AM pager calls and tedious patching – the boring stuff is handled by the system. Organizations need more than an AI co-pilot; they need “a complementary data engineer that takes over all the exhausting work,” freeing up humans for strategic tasks. In other words, they need agentic AI working for them.
At DataBahn, we’ve turned this vision of autonomous data governance into reality. It’s embodied in Cruz, our agentic AI-powered data engineer that works within DataBahn’s security data fabric. Cruz is not just another monitoring tool or script library – as we often describe it, Cruz is “an autonomous AI data engineer that monitors, detects, adapts, and actively resolves issues with minimal human intervention.” In practice, that means Cruz and the surrounding platform components (from smart edge collectors to our central data fabric) handle the heavy lifting of governance automatically. Instead of static pipelines with bolt-on rules, DataBahn provides a self-healing, policy-aware pipeline that governs itself in real time.
With these agentic capabilities, DataBahn’s platform transforms data governance from a static, after-the-fact function into a dynamic, self-healing workflow. Instead of asking “Who should access this data?” you can start trusting the system to ask “Is this data correct, compliant, and useful – and if not, how do we fix it right now?”. Governance becomes an active verb, not just a set of nouns (policies, roles, classifications) sitting on a shelf. By moving governance into the fabric of data operations, DataBahn ensures your pipelines are not only efficient, but defensible and trustworthy by default.
The shift from access to agency means your governance framework can finally scale with your data and complexity. Instead of a gatekeeper saying “no,” you get a guardian angel for your data: one that tirelessly cleans, repairs, and protects your information assets across the entire journey from collection to storage. For CISOs and compliance leaders, this translates to unprecedented confidence – policies are enforced continuously and audit trails are built into every transaction. For data engineers and analysts, it means freedom from the drudgery of pipeline maintenance and an end to the 3 AM pager calls; they gain an automated colleague who has their back in maintaining data integrity.
The era of autonomous, agentic governance is here, and it’s changing data management forever. Organizations that embrace this model will see their data pipelines become strategic assets rather than liabilities. They’ll spend less time worrying about broken feeds or inadvertent exposure, and more time extracting value and insights from a trusted data foundation. In a world of exploding data volumes and accelerating compliance demands, intelligent agents aren’t a luxury – they’re the new necessity for staying ahead.
If you’re ready to move from static control to proactive intelligence in your data strategy, it’s time to explore what agentic AI can do for you. Contact DataBahn or book a demo to see how Cruz and our security data fabric can transform your governance approach.
Every second, billions of connected devices quietly monitor the pulse of the physical world: measuring pressure in refineries, tracking vibrations on turbine blades, adjusting the temperature of precision manufacturing lines, counting cars at intersections, and watching valves that regulate clean water. This is the telemetry that keeps our world running. It is also increasingly what’s putting the world at risk.
In 2021, attackers tried to poison a water plant in Oldsmar, Florida, by changing chemical levels. In 2022, ransomware actors breached Tata Power in India, exfiltrating operational data and disrupting key functions. These weren’t IT breaches – they targeted operational technology (OT): the systems where the digital meets the physical. When compromised, they can halt production, damage equipment, or endanger lives.
Despite this growing risk, the telemetry from these systems – the rich, continuous streams of data describing what’s happening in the real world – aren’t entering enterprise-grade security and analytics tools such as SIEMs.
For decades, OT telemetry was designed for control, not correlation. Its data is continuous, dense, and expensive to store – the exact opposite of the discrete, event-based logs that SIEMs and observability tools were built for. This mismatch created an architectural blind spot: the systems that track our physical world can’t speak the same language as the systems that secure our digital one. Today, as plants and utilities connect to the cloud, that divide has become a liability.
Security teams managed discrete events – a log, an edit to a file, an alert. OT telemetry reflects continuous signals – temperature, torque, flow, vibrations, cycles. Traditional security logs are timestamped records of what happened. OT data describes what’s happening, sampled dozens or even thousands of times per minute. This creates three critical mismatches in OT and IT telemetry data:
This is why most enterprises either down-sample OT data or skip it entirely; and why most SIEMs don’t have the capacity to ingest OT data out of the box.
Without unified telemetry, security teams only see fragments of their operational truth. Silent sources or anomalous readings might seem harmless to OT engineers but might signal malicious interference; but that clue needs to be seen and investigated with SOCs to uncover the truth. Each uncollected and unanalyzed bit of data widens the gap between what has happened, what is happening, and what could happen in the future. In our increasingly connected and networked enterprises, that’s where risk lies.
For decades, OT systems operated in isolated environments – air-gapped networks, proprietary closed-loop control systems, and field devices that only speak to their own kind. However, as enterprises sought real-time visibility and data-driven optimization, operational systems started getting linked to enterprise networks and cloud platforms. Plants started streaming production metrics to dashboards; energy firms connected sensors to predictive maintenance systems, and industrial vendors began managing equipment remotely.
The result: enormous gains in efficiency – and a sudden explosion of exposure.
Attackers can now reach into building control systems inside manufacturing facilities, power plants, and supply chain networks to reach what was once unreachable. Suddenly, a misconfigured VPN or a vulnerability in middleware systems that connect OT to IT systems (current consensus suggests this is what exposed the JLR systems in the recent hack) could become an attacker’s entry point into core operations.
For many CISOs, CIOs, and CTOs, OT telemetry remains a budget line item – something to collect sparingly because of the cost of ingesting and storing it, especially in their favorite security tools and systems built over years of operations. But this misses the larger shift underway.
This data is no longer about just monitoring machines – it’s about protecting business continuity and understanding operational risk. The same telemetry that can predict a failing compressor can also help security teams catch and track a cyber intrusion.
Organizations that treat this data and its security management purely as a compliance expense will always be reactive; those that see this as a strategic dataset – feeding security, reliability, and AI-driven optimization – will turn it into a competitive advantage.
AI has always been most effective when it’s fed by diverse, high-quality data. This is the mindset with which the modern security team treated data, but ingestion-based pricing made them allergic to collecting OT telemetry at scale. But this same mindset is now reaching operational systems, and leading organizations around the world are treating IoT and OT telemetry as strategic data sources for AI-driven security, optimization, and resilience.
AI thrives on context, and no data source offers more context than telemetry that connects the digital and physical worlds. Patterns in OT data can reveal early indications of faltering equipment, sub-optimal logistical choices, and resource allocation signals that can help the enterprise save. It can also provide early indication of attack and defray significant business continuity and operational safety risk.
But for most enterprises, this value is still locked behind scale, complexity, and gaps in their existing systems and tools. Collecting, normalizing, and routing billions of telemetry signals from globally distributed sites is challenging to build manually. Existing tools to solve these problems (SIEM collectors, log forwarders) aren’t built for these data types and still require extensive effort to repurpose.
This is where Agentic AI can become transformative. Rather than analyzing data downstream after extensive tooling to manage data, AI can be harnessed to manage and govern telemetry from the point of ingestion.
The result is a system that scales not by collecting less, but by collecting everything and routing intelligently. AI is not just the reason to collect more telemetry – it is also the means to make that data valuable and sustainable at scale.
A global energy producer operating more than 80 distributed industrial sites faced the same challenge shared by many manufacturers: limited bandwidth, siloed OT networks, and inconsistent data formats. Each site generates between a few gigabytes to hundreds of gigabytes of log data daily – a mix of access control logs, process telemetry, and infrastructure events. Only a fraction of this data reached their security operations center. The rest stayed on-premise, trapped in local systems that couldn’t easily integrate with their SIEM or data lake. This created blind spots and with recent compliance developments in their region, they needed to integrate this into their security architecture.
The organization decided to re-architect their telemetry layer around a modular, pipeline-first approach. After an evaluation process, they chose Databahn as their vendor to accomplish this. They deployed Databahn’s collectors at the edge, capable of compressing and filtering data locally before securely transmitting it to centralized storage and security tools.
With bandwidth and network availability varying dramatically across sites, edge intelligence became critical. The collectors automatically prioritized security-relevant data for streaming, compressing non-relevant telemetry for slower transmission to conserve network capacity when needed. When a new physical security system needed to be onboarded – one with no existing connectors – an AI-assisted parser system was built in a few days, not months. This agility helped the team reduce their backlog of pending log sources and immediately increase their visibility across their OT environments.
In parallel, they used policy-driven routing to send filtered telemetry not only to their security tools, but also to the organization’s data lake – enabling business and engineering teams to analyze the same data for operational insights.
The outcome?
This is the power of treating telemetry as a strategic asset: and of using the pipeline as the control plane to ensure that the increased coverage and visibility don’t come at the cost of security posture or by destroying the IT/Security budget.
The convergence of IT and OT has and will continue to represent an increase in the attack surface and the vulnerability of digital systems deeply connected to physical reality. For factories and manufacturers like Jaguar Land Rover, this is about protecting their systems from ransomware actors. For power manufacturers and utilities distributors, it could mean the difference between life and death for their business, employees, and citizens with major national security implications.
To meet this increased risk threshold, telemetry must become the connective tissue of resilience. It must be more closely watched, more deeply understood, and more intelligently managed. Its value must be gauged as early as possible, and its volume must be routed intelligently to sanctify detection and analytics equipment while retaining the underlying data for bulk analysis.
The next decade of enterprise security and AI will depend upon how effectively organizations bridge this divide from the present into the ideal future. The systems that today are being kept out of SIEMs to stop them from flooding will need to fuel your AI. The telemetry from isolated networks will have to be connected to power real-time visibility across your enterprise.
The world will run on this data – and so should the security of your organization.
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