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

Picture this: it’s a sold-out Saturday. The mobile app is pushing seat upgrades, concessions are running tap-to-pay, and the venue’s “smart” cameras are adjusting staffing in real time. Then, within minutes, queues freeze. Kiosks time out. Fans can’t load tickets. A firmware change on a handful of access points creates packet loss that never gets flagged because telemetry from edge devices isn’t normalized or prioritized. The network team is staring at graphs, the app team is chasing a “payments API” ghost, and operations is on a walkie-talkie trying to reroute lines like it’s 1999.

Nothing actually “broke” – but the system behaved like it did. The signal existed in the data, just not in one coherent place, at the right time, in a format anyone could trust.

That’s where the state of observability really is today: tons of data, not enough clarity – especially close to the source, where small anomalies compound into big customer moments.

Why this is getting harder, not easier

Every enterprise now runs on an expanding mix of cloud services, third-party APIs, and edge devices. Tooling has sprawled for good reasons – teams solve local problems fast – but the sprawl works against global understanding. Nearly half of organizations still juggle five or more tools for observability, and four in ten plan to consolidate because the cost of stitching signals after the fact is simply too high.

More sobering: high-impact outages remain expensive and frequent. A majority report that these incidents cost $1M+ per hour; median annual downtime still sits at roughly three days; and engineers burn about a third of their week on disruptions. None of these are “tool problems” – they’re integration, governance, and focus problems. The data is there. It just isn’t aligned.

What good looks like; and why we aren’t there yet

The pattern is consistent: teams that unify telemetry and move toward full-stack observability outperform. They see radically less downtime, lower hourly outage costs, and faster mean-time-to-detect/resolve (MTTD/MTTR). In fact, organizations with full-stack observability experience roughly 79% less downtime per year than those without – an enormous swing that shows what’s possible when data isn’t trapped in silos.

But if the winning patterns is so clear, why aren’t more teams there already?

Three reasons keep coming up in practitioner and leadership conversations:

1. Heterogeneous sources, shifting formats. New sensors, services, and platforms arrive with their own schemas, naming, and semantics. Without upstream normalization, every dashboard and alert “speaks a slightly different dialect.” Governance becomes wishful thinking.

2. Point fixes vs. systemic upgrades. It’s hard to lift governance out of individual tools when the daily firehose keeps you reactive. You get localized wins, but the overall signal quality doesn’t climb.

3. Manual glue. Humans are still doing context assembly – joining business data with MELT, correlating across tools, re-authoring similar rules per system. That’s slow and brittle.

Zooming out: what the data actually says

Let’s connect the dots in plain English:

• Tool sprawl is real. 45% of orgs use five or more observability tools. Most use multiple, and only a small minority use one. It’s trending down, and 41% plan to consolidate – but today’s reality remains multi-tool.

• Unified telemetry pays off. Teams with more unified data experience ~78% less downtime vs. those with siloed data. Said another way: the act of getting logs, metrics, traces, and events into a consistent, shared view delivers real business outcomes.

• The value is undeniable. Median annual downtime across impact levels clocks in at ~77 hours; for high-impact incidents, 62% say the hourly cost is at least $1M. When teams reach full-stack observability, hourly outage costs drop by nearly half.

• We’re still spending time on toil. Engineers report around 30% of their time spent addressing disruptions. That’s innovation time sacrificed to “finding and fixing” instead of “learning and improving.”

• Leaders want governance, not chaos. There’s a clear preference for platforms that are more capable at correlating telemetry with business outcomes and generating visibility without spiking manual effort and management costs.

The edge is where observability’s future lies

Back to our almost-dark stadium. The fix isn’t “another dashboard.” It’s moving control closer to where telemetry is born and ensuring the data becomes coherent as it moves, not after it lands.

That looks like:

• Upstream normalization and policy: standardizing fields, units, PII handling, and tenancy before data fans out to tools.

• Schema evolution without drama: recognizing new formats at collection time, mapping them to shared models, and automatically versioning changes.

• Context attached early: enriching events with asset identity, environment, service boundaries, and – crucially – business context (what this affects, who owns it, what “good” looks like), so investigators don’t have to hunt for meaning later.

• Fan-out by design, not duplication: once the signal is clean, you can deliver the same truth to APM, logs, security analytics, and data lakes without re-authoring rules per tool.

When teams do this, the graphs start agreeing with each other. And when the graphs agree, decisions accelerate. Every upstream improvement makes all of your downstream tools and workflows smarter. Compliance is easier and more governed; data is better structured; its routing is more streamlined. Audits are easier and are much less likely to surface annoying meta-needs but are more likely to generate real business value.

The AI inflection: less stitching, more steering

The best news? We finally have the toolsto automate the boring parts and amplify the smart parts.

• AIOps that isn’t just noise. With cleaner, standardized inputs, AI has less “garbage” to learn from and can detect meaningful patterns (e.g., “this exact firmware + crowd density + POS jitter has preceded incidents five times in twelve months”).

• Agentic workflows. Instead of static playbooks, agentic AI can learn and adapt: validate payloads, suggest missing context, test routing changes, or automatically revert a bad config on a subset of edge devices – then explain what it did in human terms.

• Human-in-the-loop escalation. Operators set guardrails; AI proposes actions, runs safe-to-fail experiments, and asks for approval on higher-risk steps. Over time, the playbook improves itself.

This isn’t sci-fi. In the same industry dataset, organizations leaning into AI monitoring and related capabilities report higher overall value from their observability investments – and leaders list adoption of AI tech as a top driver for modernizing observability itself.

Leaders are moving – are you?

Many of our customers are finding our AI-powered pipelines – with agentic governance at the edge through the data path – as the most reliable way to harness the edge-first future of observability. They’re not replacing every tool; they’re elevating the control plane over the tools so that managing what data gets to each tool is optimized for cost, quality, and usefulness. This is the shift that is helping our Fortune 100 and Fortune 500 customers convert flight data, OT telemetry, and annoying logs into their data crown jewels.

If you want the full framework and the eight principles we use when designing modern observability, grab the whitepaper, Principles of Intelligent Observability, and share it with your team. If you’d like to explore how AI-powered pipelines can make this real in your environment, request a demo and learn more about how our existing customers are using our platform to solve security and observability challenges while accelerating their transition into AI.

Enterprise security teams have been under growing pressure for years. Telemetry volumes have increased across cloud platforms, identity systems, applications, and distributed infrastructure. As data grows, - SIEM and storage costs rise faster than budgets. Pipeline failures - happen more often during peak times. Teams lose visibility precisely when they need it most. Data engineers are overwhelmed by the range of formats, sources, and fragile integrations across a stack that was never meant to scale this quickly. What was once a manageable operational workflow has become a source of increasing technical debt and operational risk.

These challenges have elevated the pipeline from a mere implementation detail to a strategic component within the enterprise. Organizations now understand that how telemetry is collected, normalized, enriched, and routed influences not only cost but also resilience, visibility, and the effectiveness of modern analytics and AI tools. CISOs are realizing that they cannot build a future-ready SOC without controlling the data plane that supplies it. As this shift speeds up, a clear trend has emerged among the Fortune 500 and Global 2000 companies - Security leaders are opting for independent, vendor-neutral pipelines that simplify complexity, restore ownership, and deliver consistent, predictable value from their telemetry.

Why Neutrality Matters More than Ever

Independent, vendor-neutral pipelines provide a fundamentally different operating model. They shift control from the downstream tool to the enterprise itself. This offers several benefits that align with the long-term priorities of CISOs.

Flexibility to choose best-of-breed tools

A vendor-neutral pipeline enables organizations to choose the best SIEM, XDR, SOAR, storage system, or analytics platform without fretting over how tooling changes will impact ingestion. The pipeline serves as a stable architectural foundation that supports any mix of tools the SOC needs now or might adopt in the future.

Compared to SIEM-operated pipelines, vendor-neutral solutions offer seamless interoperability across platforms, reduce the cost and effort of managing multiple best-in-breed tools, and deliver stronger outcomes without adding setup or operational overhead. This flexibility also supports dual-tool SOCs, multi-cloud environments, and evaluation scenarios where organizations want the freedom to test or migrate without disruptions.

Unified Data Ops across Security, IT, and Observability

Independent pipelines support open schemas and standardized models like OCSF, CIM, and ECS. They enable telemetry from cloud services, applications, infrastructure, OT systems, and identity providers to be transmitted into consistent and transparent formats. This facilitates unified investigations, correlated analytics, and shared visibility across security, IT operations, and engineering teams.

Interoperability becomes even more essential as organizations undertake cloud transformation initiatives, use security data lakes, or incorporate specialized investigative tools. When the pipeline is neutral, data flows smoothly and consistently across platforms without structural obstacles. Intelligent, AI-driven data pipelines can handle various use cases, streamline telemetry collection architecture, reduce agent sprawl, and provide a unified telemetry view. This is not feasible or suitable for pipelines managed by MDRs, as their systems and architecture are not designed to address observability and IT use cases. 

Modularity that Matches Modern Enterprise Architecture

Enterprise architecture has become modular, distributed, and cloud native. Pipelines tied to a single analytics tool or managed service provider - act as a challenge today for how modern organizations operate. Independent pipelines support modular design principles by enabling each part of the security stack to evolve separately.

A new SIEM should not require rebuilding ingestion processes from scratch. Adopting a data lake should not require reengineering normalization logic.and adding an investigation tool should not trigger complex migration events. Independence ensures that the pipeline remains stable while the surrounding technology ecosystem continues to evolve. It allows enterprises to choose architectures that fit their specific needs and are not constrained by their SIEM’s integrations or their MDR’s business priorities.

Cost Governance through Intelligent Routing

Vendor-neutral pipelines allow organizations to control data routing based on business value, risk tolerance, and budget. High-value or compliance-critical telemetry can be directed to the SIEM. Lower-value logs can be sent to cost-effective storage or cloud analytics services.  

This prevents the cost inflation that happens when all data is force-routed into a single analytics platform. It enhances the CISO’s ability to control SIEM spending, manage storage growth, and ensure reliable retention policies without losing visibility.

Governance, Transparency, and Control

Independent pipelines enforce transparent logic around parsing, normalization, enrichment, and filtering. They maintain consistent lineage for every transformation and provide clear observability across the data path.

This level of transparency is important because data governance has become a key enterprise requirement. Vendor-neutral pipelines make compliance audits easier, speed up investigations, and give security leaders confidence that their visibility is accurate and comprehensive. Most importantly, they keep control within the enterprise rather than embedding it into the operating model of a downstream vendor, the format of a SIEM, or the operational choices of an MDR vendor.

AI Readiness Through High-Quality, Consistent Data

AI systems need reliable, well-organized data. Proprietary ingestion pipelines restrict this because transformations are designed for a single platform, not for multi-tool AI workflows.

Neutral pipelines deliver:

• consistent schemas across destinations
• enriched and context-ready data
• transparency into transformation logic
• adaptability for new data types and workloads

This provides the clean and interoperable data layer that future AI initiatives rely on. It supports AI-driven investigation assistants, automated detection engineering, multi-silo reasoning, and quicker incident analysis.

The Long-Term Impact of Independence

Think about an organization planning its next security upgrade. The plan involves cutting down SIEM costs, expanding cloud logging, implementing a security data lake, adding a hunting and investigation platform, enhancing detection engineering, and introducing AI-powered workflows.

If the pipeline belongs to a SIEM or MDR provider, each step of this plan depends on vendor capabilities, schemas, and routing logic. Every change requires adaptation or negotiation. The plan is limited by what the vendor can support and - how they decide to support it.

When the pipeline is part of the enterprise, the roadmap progresses more smoothly. New tools can be incorporated by updating routing rules. Storage strategies can be refined without dependency issues. AI models can run on consistent schemas. SIEM migration becomes a simpler decision rather than a lengthy engineering project. Independence offers more options, and that flexibility grows over time.

Why Independent Pipelines are Winning

Independent pipelines have gained momentum across the Fortune 500 and Global 2000 because they offer the architectural freedom and governance that modern SOCs need. Organizations want to use top-tier tools, manage costs predictably, adopt AI on their own schedule, and retain ownership of the data that defines their security posture. Early adopters embraced SDPs because they sat between systems, providing architectural control, flexibility, and cost savings without locking customers into a single platform. As SIEM, MDR, and data infrastructure players have acquired or are offering their own pipelines, the market risks returning to the very vendor dependency that SIEMs were meant to eliminate. In a practitioner’s words from SACR’s recent report, “we’re just going to end up back where we started, everything re-bundled under one large platform.”

According to Francis Odum, a leading cybersecurity analyst, “ … the core role of a security data pipeline solution is really to be that neutral party that’s able to ingest no matter whatever different data sources. You never want to have any favorites, as you want a third-party that’s meant to filter.” When enterprise security leaders choose their data pipelines, they want independence and flexibility. An independent, vendor-neutral pipeline is the foundation of architectures that keep control with the enterprise.

Databahn has become a popular choice during this transition because it shows what an enterprise-grade independent pipeline can achieve in practice. Many CISOs worldwide have selected our AI-powered data pipeline platform due to its flexibility and ease of use, decoupling telemetry ingestion from SIEM, lowering SIEM costs, automating data engineering tasks, and providing consistent AI-ready data structures across various tools, storage systems, and analytics engines.

The Takeaway for CISOs

The pipeline is no longer an operational layer. It is a strategic asset that determines how adaptable, cost-efficient, and AI-ready the modern enterprise can be. Vendor-neutral pipelines offer the flexibility, interoperability, modularity, and governance that CISOs need to build resilient and forward-looking security programs.

This is why independent pipelines are becoming the standard for organizations that want to reduce complexity, maintain freedom of choice and unlock greater value from their telemetry. In a world where tools evolve quickly, where data volumes rise constantly and where AI depends on clean and consistent information, the enterprises that own their pipelines will own their future.

Modern enterprises depend on a complex mesh of SaaS tools, observability agents, and data pipelines. Each integration, whether a cloud analytics SDK, IoT telemetry feed, or on–prem collector, can become a hidden entry point for attackers. In fact, recent incidents show that breaches often begin outside core systems. For example, OpenAI’s November 2025 disclosure revealed that a breach of their third party analytics vendor Mixpanel exposed customers’ names, emails and metadata. This incident wasn’t due to a flaw in OpenAI’s code at all, but to the telemetry infrastructure around it. In an age of hyperconnected services, traditional security perimeters don’t account for these “data backdoors.” The alarm bells are loud, and we urgently need to rethink supply chain security from the data layer outwards.

Why Traditional Vendor Risk Management Falls Short

Most organizations still rely on point-in-time vendor assessments and checklists. But this static approach can’t keep up with a fluid, interconnected stack. In fact, SecurityScorecard found that 88% of CISOs are concerned about supply chain cyber risk, yet many still depend on passive compliance questionnaires. As GAN Integrity notes, “historically, vendor security reviews have taken the form of long form questionnaires, manually reviewed and updated once per year.” By the time those reports are in hand, the digital environment has already shifted. Attackers exploit this lag: while defenders secure every connection, attackers “need only exploit a single vulnerability to gain access”.

Moreover, vendor programs often miss entire classes of risk. A logging agent or monitoring script installed in production seldom gets the same scrutiny as a software update, yet it has deep network access. Legacy vendor risk tools rarely monitor live data flows or telemetry health. They assume trusted integrations remain benign. This gap is dangerous: data pipelines often traverse cloud environments and cross organizational boundaries unseen. In practice, this means today’s “vendor ecosystem” is a dynamic attack surface that traditional methods simply weren’t designed to cover.

Supply Chain Breaches: Stats and Incidents

The scale of the problem is now clear. Industry data show supply chain attacks are becoming common, not rare. The 2025 Verizon Data Breach Investigations Report found that nearly 30% of breaches involved a third party, up sharply from the prior year. In a SecurityScorecard survey, over 70% of organizations reported at least one third party cybersecurity incident in the past year  and 5% saw ten or more such incidents. In other words, it’s now normal for a large enterprise to deal with multiple vendor-related breaches per year.

Highprofile cases make the point vividly. Classic examples like the 2013 Target breach (via an HVAC vendor) and 2020 SolarWinds attack demonstrate how a single compromised partner can unleash devastation. More recently, attackers trojanized a trusted desktop app in 2023: a rogue update to the 3CX telecommunications software silently delivered malware to thousands of companies. In parallel, the MOVEit Transfer breach of 2023 exploited a zero-day in a file transfer service, exposing data at over 2,500 organizations worldwide. Even web analytics are not safe: 2023’s Magecart attacks injected malicious scripts into ecommerce payment flows, skimming card data from sites like Ticketmaster and British Airways. These incidents show that trusted data pipelines and integrations are attractive targets, and that compromises can cascade through many organizations.

Taken together, the data and stories tell us: supply chain breaches are systemic. A small number of shared platforms underpin thousands of companies. When those are breached, the fallout is widespread and rapid. Static vendor reviews and checklists clearly aren’t enough.

Telemetry Pipelines as an Attack Surface

The modern enterprise is drowning in telemetry: logs, metrics, traces, and events flowing continuously from servers, cloud services, IoT devices and business apps. This “data exhaust” is meant for monitoring and analysis, but its complexity and volume make it hard to control. Telemetry streams are typically high volume, heterogeneous, and loosely governed. Importantly, they often carry sensitive material: API keys, session tokens, user IDs and even plaintext passwords can slip into logs. Because of this, a compromised observability agent or analytics SDK can give attackers unintended visibility or access into the network.

Without strict segmentation, these pipelines become free-for-all highways. Each new integration (such as installing a SaaS logging agent or opening a firewall for an APM tool)  expands the attack surface. As SecurityScorecard puts it, every vendor relationship “expands the potential attack surface”. Attackers exploit this asymmetry: defending hundreds of telemetry connectors is hard, but an attacker needs only one weak link. If a cloud logging service is misconfigured or a certificate is expired, an adversary could feed malicious data or exfiltrate sensitive logs unnoticed. Even worse, an infiltrated telemetry node can act as a beachhead: from a log agent living on a server, an attacker might move laterally into the production network if there are no micro-segmentation controls.

In short, modern telemetry pipelines can greatly amplify risk if not tightly governed. They are essentially hidden corridors through which attackers can slip. Security teams often treat telemetry as “noise,” but adversaries know it contains a wealth of context and credentials. The moment a telemetry link goes unchecked, it may become a conduit for data breaches.

Securing Telemetry with a Security Data Fabric

To counter these risks, organizations are turning to the concept of a security data fabric. Rather than an adhoc tangle of streams, a data fabric treats telemetry collection and distribution as a controlled, policy-driven network. In practice, this means inserting intelligence and governance at the edges and in - flight, rather than only at final destinations. A well implemented security data fabric can reduce supply chain risk in several ways:

• Visibility into third - party data flows. The fabric provides full data lineage, showing exactly which events come from which sources. Every log or metric is tagged and tracked from its origin (e.g. “AWS CloudTrail from Account A”) to its destination (e.g. “SIEM”), so nothing is blind. In fact, leading security data fabrics offer full lifecycle visibility, with “silent device” alerts when an expected source stops sending data. This means you’ll immediately notice if a trusted telemetry feed goes dark (possibly due to an attacker disabling it) or if an unknown source appears.

• Policy - driven segmentation of telemetry pipelines. Instead of a flat network where all logs mix together, a fabric enforces routing rules at the collection layer. For example, telemetry from Vendor X’s devices can be automatically isolated to a dedicated stream. DataBahn’s architecture, for instance, allows “policy-driven routing” so teams can choose that data goes only to approved sinks. This micro-segmentation ensures that even if one channel is compromised, it cannot leak data into unrelated systems. In effect, each integration is boxed to its own lane unless explicitly allowed, breaking the flat trust model.

• Real-time masking and filtering at collection. Because the fabric processes data at the edge, it can scrub or redact sensitive content before it spreads. Inline filtering rules can drop credentials, anonymize PII, or suppress noisy events in real time. The goal is to “collect smarter” by shedding high risk data as early as possible. For instance, a context-aware policy might drop repetitive health - check pings while still preserving anomaly signals. Similarly, built -in “sensitive data detection” can tag and redact fields like account IDs or tokens on the fly. By the time data reaches the central tools, it’s already compliance safe, meaning a breach of the pipeline itself exposes far less.

• Alerting on silent or anomalous telemetry. The fabric continuously monitors its own health and pipelines. If a particular log source stops reporting (a “silent integration”), or if volumes suddenly spike, security teams are alerted immediately. Capabilities like schema drift tracking and real-time health metrics detect when an expected data source is missing or behaving oddly. This matters because attackers will sometimes try to exfiltrate data by quietly rerouting streams; a security data fabric won’t miss that. By treating telemetry streams as security assets to be monitored, the fabric effectively adds an extra layer of detection.

Together, these capabilities transform telemetry from a liability into a defense asset. By making data flows transparent and enforceable, a security data fabric closes many of the gaps that attackers have exploited in recent breaches. Crucially, all these measures are invisible to developers: services send their telemetry as usual, but the fabric ensures it is tagged, filtered and routed correctly behind the scenes.

Actionable Takeaways: Locking Down Telemetry

In a hyperconnected architecture, securing data supply chains requires both visibility and control over every byte in motion. Here are key steps for organizations:

• Inventory your telemetry. Map out every logging and monitoring integration, including cloud services, SaaS tools, IoT streams, etc. Know which teams and vendors publish data into your systems, and where that data goes.

• Segment and policy-enforce every flow. Use firewalls, VPC rules or pipeline policies to isolate telemetry channels. Apply the principle of least privilege: e.g., only allow the marketing analytics service to send logs to its own analytics tool, not into the corporate data lake.

• Filter and redact early. Wherever data is collected (at agents or brokers), enforce masking rules. Drop unnecessary fields or PII at the source. This minimizes what an attacker can steal from a compromised pipeline.

• Monitor pipeline health continuously. Implement tooling or services that alert on anomalies in data collection (silence, surges, schema changes). Treat each data integration as a critical component in your security posture.

The rise in supply chain incidents shows that defenders must treat telemetry as a first-class security domain, not just an operational convenience. By adopting a fabric mindset, one that embeds security, governance and observability into the data infrastructure, enterprises can dramatically shrink the attack surface of their connected environment. In other words, the next time you build a new data pipeline, design it as a zero-trust corridor: assume nothing and verify everything. This shift turns sprawling telemetry into a well-guarded supply chain, rather than leaving it an open backdoor.