In September 2022, cybercriminals accessed, encrypted, and stole a substantial amount of data from Suffolk County’s IT systems, which included personally identifiable information (PII) of county residents, employees, and retirees. Although Suffolk County did not pay the ransom demand of $2.5 million, it ultimately spent $25 million to address and remediate the impact of the attack.

Members of the county’s IT team reported receiving hundreds of alerts every day in the weeks leading up to the attack. Several months earlier, frustrated by the excessive number of unnecessary alerts, the team redirected the notifications from their tools to a Slack channel. Although the frequency and severity of the alerts increased leading up to the September breach, the constant stream of alerts wore the small team down, leaving them too exhausted to respond and distinguish false positives from relevant alerts. This situation created an opportunity for malicious actors to successfully circumvent security systems.

The alert fatigue problem

Today, cybersecurity teams are continually bombarded by alerts from security tools throughout the data lifecycle. Firewalls, XDRs/EDRs, and SIEMs are among the common tools that trigger these alerts. In 2020, Forrester reported that SOC teams received 11,000 alerts daily, and 55% of cloud security professionals admitted to missing critical alerts. Organizations cannot afford to ignore a single alert, yet alert fatigue (and an overwhelming number of unnecessary alerts) causes SOCs to miss up to 30% of security alerts that go uninvestigated or are completely overlooked.

While this creates a clear cybersecurity and business continuity problem, it also presents a pressing human issue. Alert fatigue leads to cognitive overload, emotional exhaustion, and disengagement, resulting in stress, mental health concerns, and attrition. More than half of cybersecurity professionals cite their workload as the primary source of stress, two-thirds reported experiencing burnout, and over 60% of cybersecurity professionals surveyed stated it contributed to staff turnover and talent loss.

Alert fatigue poses operational challenges, represents a critical security risk, and truly becomes an adversary of the most vital resource that enterprises rely on for their security — SOC professionals doing their utmost to combat cybercriminals. SOCs are spending so much time and effort triaging alerts and filtering false positives that there’s little room for creative threat hunting.

Data is the problem – and the solution

Alert fatigue is a result, not a root cause. When these security tools were initially developed, cybersecurity teams managed gigabytes of data each month from a limited number of computers on physically connected sites. Today, Security Operations Centers (SOCs) are tasked with handling security data from thousands of sources and devices worldwide, which arrive through numerous distinct devices in various formats. The developers of these devices did not intend to simplify the lives of security teams, and the tools they designed to identify patterns often resemble a fire alarm in a volcano. The more data that is sent as an input to these machines, the more likely they are to malfunction – further exhausting and overwhelming already stretched security teams.

Well-intentioned leaders advocate for improved triaging, the use of automation, refined rules to reduce false-positive rates, and the application of popular technologies like AI and ML. Until we can stop security tools from being overwhelmed by large volumes of unstructured, unrefined, and chaotic data from diverse sources and formats, these fixes will be band aids on a gaping wound.

The best way to address alert fatigue is to filter out the data being ingested into downstream security tools. Consolidate, correlate, parse, and normalize data before it enters your SIEM or UEBA. If it isn’t necessary, store it in blob storage. If it’s duplicated or irrelevant, discard it. Don’t clutter your SIEM with poor data so it doesn’t overwhelm your SOC with alerts no one requested.

How Databahn helps

At DataBahn, we help enterprises cut through cybersecurity noise with our security data pipeline solution, which works around the clock to:

1. Aggregates and normalizes data across tools and environments automatically

2. Uses AI-driven correlation and prioritization

3. Denoises the data going into the SIEM, ensuring more actionable alerts with full context

SOCs using DataBahn aren’t overwhelmed with alerts; they only see what’s relevant, allowing them to respond more quickly and effectively to threats. They are empowered to take a more strategic approach in managing operations, as their time isn’t wasted triaging and filtering out unnecessary alerts.

Organizations looking to safeguard their systems – and protect their SOC members – should shift from raw alert processing to smarter alert management, driven by an intelligent pipeline which combines automation, correlation, and transformation that filters out the noise and combats alert fatigue.

Interested in saving your SOC from alert fatigue? Contact DataBahn
In the past, we've written about how we solve this problem for Sentinel. You can read more here: AI-powered Sentinel Log Optimization

DataBahn recognized as leading vendor in SACR 2025 Security Data Pipeline Platforms Market Guide

As security operations become more complex and SOCs face increasingly sophisticated threats, the data layer has emerged as the critical foundation. SOC effectiveness now depends on the quality, relevance, and timeliness of data it processes; without a robust data layer, SIEM-based analytics, detection, and response automation crumble under the deluge of irrelevant data and unreliable insights.

Recognizing the need to engage with current SIEM problems, security leaders are adopting a new breed of security data tools known as Security Data Pipeline Platforms. These platforms sit beneath the SIEM, acting as a control plane for ingesting, enriching, and routing security data in real time. In its 2025 Market Guide, SACR explores this fast-emerging category and names DataBahn among the vendors leading this shift.
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‍Understanding Security Data Pipelines: A New Approach

The SACR report highlights this breaking point: organizations typically collect data from 40+ security tools, generating terabytes daily. This volume overwhelms legacy systems, creating three critical problems:

First, prohibitive costs force painful tradeoffs between security visibility and budget constraints. Second, analytics performance degrades as data volumes increase. Finally, security teams waste precious time managing infrastructure rather than investigating threats.

Fundamentally, security data pipeline platforms partially or fully resolve the data volume problems with differing outcomes and performance. DataBahn decouples collection from storage and analysis, automates and simplifies data collection, transformation, and routing. This architecture reduces costs while improving visibility and analytic capabilities—the exact opposite of the traditional, SIEM-based approach.

AI-Driven Intelligence: Beyond Basic Automation

The report examines how AI is reshaping security data pipelines. While many vendors claim AI capabilities, few have integrated intelligence throughout the entire data lifecycle.

DataBahn's approach embeds intelligence at every layer of the security data pipeline. Rather than simply automating existing processes, our AI continually optimizes the entire data journey—from collection to transformation to insight generation.

This intelligence layer represents a paradigm shift from reactive to proactive security, moving beyond "what happened?" to answering "what's happening now, and what should I do about it?"

Take threat detection as an example: traditional systems require analysts to create detection rules based on known patterns. DataBahn's AI continually learns from your environment, identifying anomalies and potential threats without predefined rules.  

The DataBahn Platform: Engineered for Modern Security Demands

In an era where security data is both abundant and complex, DataBahn's platform stands out by offering intelligent, adaptable solutions that cater to the evolving needs of security teams.

Agentic AI for Security Data Engineering: Our agentic AI, Cruz, automates the heavy lifting across your data pipeline—from building connectors to orchestrating transformations.  Its self-healing capabilities detect and resolve pipeline issues in real-time, minimizing downtime and maintaining operational efficiency.

Intelligent Data Routing and Cost Optimization: The platform evaluates telemetry data in real-time, directing only high-value data to cost-intensive destinations like SIEMs or data lakes. This targeted approach reduces storage and processing costs while preserving essential security insights.

Flexible SIEM Integration and Migration: DataBahn's decoupled architecture facilitates seamless integration with various SIEM solutions. This flexibility allows organizations to migrate between SIEM platforms without disrupting existing workflows or compromising data integrity.  

Enterprise-Wide Coverage: Security, Observability, and IoT/OT: Beyond security data, DataBahn's platform supports observability, application, and IoT/OT telemetry, providing a unified solution for diverse data sources. With 400+ prebuilt connectors and a modular architecture, it meets the needs of global enterprises managing hybrid, cloud-native, and edge environments.
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Next-Generation Security Analytics

One of DataBahn’s standout features highlighted by SACR is our newly launched "insights layer”—Reef. Reef transforms how security professionals interact with data through conversational AI. Instead of writing complex queries or building dashboards, analysts simply ask questions in natural language: "Show me failed login attempts for privileged users in the last 24 hours" or "Show me all suspicious logins in the last 7 days"

Critically, Reef decouples insight generation from traditional ingestion models, allowing Security analysts to interact directly with their data, gain context-rich insights without cumbersome queries or manual analysis. This significantly reduces the mean time to detection (MTTD) and response (MTTR), allowing teams to prioritize genuine threats quickly.

Moving Forward with Intelligent Security Data Management

DataBahn's inclusion in the SACR 2025 Market Guide affirms our position at the forefront of security data innovation. As threat environments grow more complex, the difference between security success and failure increasingly depends on how effectively organizations manage their data.

We invite you to download the complete SACR 2025 Market Guide to understand how security data pipeline platforms are reshaping the industry landscape. For a personalized discussion about transforming your security operations, schedule a demo with our team. Click here

In today's environment, your security data should work for you, not against you.

Telemetry Data Pipelines

and how they impact decision-making for enterprises

For effective data-driven decision-making, decision-makers must access accurate and relevant data at the right time. Security, sales, manufacturing, resource, inventory, supply chain, and other business-critical data help inform critical decisions. Today’s enterprises need to aggregate relevant data from around the world and various systems into a single location for analysis and presentation to leaders in a digestible format in real time for them to make these decisions effectively.

Why telemetry data pipelines matter

Today, businesses of all sizes need to collect information from various sources to ensure smooth operations. For instance, a modern retail brand must gather sales data from multiple storefronts across different locations, its website, and third-party sellers like e-commerce and social media platforms to understand how their products performed. It also helps inform decisions such as inventory, stocking, pricing, and marketing.

For large multi-national commercial enterprises, this data and its importance get magnified. Executives have to make critical decisions with millions of dollars at stake and in an accelerated timeline. They also have more complex and sophisticated systems with different applications and digital infrastructures that generate large amounts of data. Both old and new-age companies must build elaborate systems to connect, collect, aggregate, make sense of, and derive insights from this data.

What is a telemetry data pipeline?

Telemetry data encompasses various types of information captured and collected from remote and hard-to-reach sources. The term ‘telemetry’ originates from the French word ‘télémètre’, which means a device for measuring (“mètre”) data from afar (“télé”). In the context of modern enterprise businesses, telemetry data includes application logs, events, metrics, and performance indicators which provide essential information that helps run, maintain, and optimize systems and operations.

A telemetry pipeline, as the name implies, is the infrastructure that collects and moves the data from the source to the destination. But a telemetry data pipeline doesn’t just move data; it also aggregates and processes this data to make it usable, and routes it to the necessary analytics or security destinations where it can be used by leaders to make important decisions.

Core functions of a telemetry data pipeline

Telemetry data pipelines have 3 core functions:

1. Collecting data from multiple sources;
2. Processing and preparing the data for analysis; and
3. Transferring the data to the appropriate storage destination.

DATA COLLECTION

The first phase of a data pipeline is collecting data from various sources. These sources can include products, applications, servers, datasets, devices, and sensors, and they can be spread across different networks and locations. The collection of this data from these different sources and moving them towards a central repository is the first part of the data lifecycle.

Challenges: With the growing number of sources, IT and data teams find it difficult to integrate new ones. API-based integrations can take between four to eight weeks for an enterprise data engineering team, placing significant demands on technical engineering bandwidth. Monitoring and tracking sources for anomalous behavior, identifying blocked data pipelines, and ensuring the seamless flow of telemetry data are major pain points for enterprises. With data volumes growing at ~30% Y-o-Y, being able to scale data collection to manage spikes in data flow is an important problem for engineering teams to solve, but they don’t always have the time and effort to invest in such a project.

DATA PROCESSING & PREPARATION

The second phase of a data pipeline is aggregating the data, which requires multiple data operations such as cleansing, de-duplication, parsing, and normalization. Raw data is not suitable for leaders to make decisions, and it needs to be aggregated from different sources. Data from different sources have to be turned into the same format, stitched together for correlation and enrichment, and prepared to be further refined for further insights and decision-making.

Challenges: Managing the different formats and parsing it can get complicated; and with many enterprises building or having built custom applications, parsing and normalizing that data is challenging. Changing log and data schemas can create cascading failures in your data pipeline. Then there are challenges such as identifying and masking sensitive data and quarantining it to protect PII from being leaked.

DATA ROUTING

The final stage is taking the data to its intended destination – a data lake or lakehouse, a cloud storage service, or an observability or security tool. For this, data has to be put into a specific format and has to be optimally segregated to avoid the high cost of the real-time analysis tools.

Challenges: Different types of telemetry data have different values, and segregating the data optimally to manage and reduce the cost of expensive SIEM and observability tools is high priority for most enterprise data teams. The ‘noise’ in the data also causes an increase in alerts and makes it harder for teams to find relevant data in the stream coming their way. Unfortunately, segregating and filtering the data optimally is difficult as engineers can't predict what data is useful and what data isn’t. Additionally, the increasing volume of data with the stagnant IT budget means that many teams are making sub-optimal choices of routing all data from some noisy sources into long-term storage, meaning that some insights are lost.

How can we make telemetry data pipelines better?

Organizations today generate terabytes of data daily and use telemetry data pipelines to move the data in real-time to derive actionable insights that inform important business decisions. However, there are major challenges in building and managing telemetry data pipelines, even if they are indispensable.

Agentic AI solves for all these challenges and is capable of delivering greater efficiency in managing and optimizing telemetry data pipeline health. An agentic AI can –

1. Discover, deploy, and integrate with new data sources instantly;
2. Parse and normalize raw data from structured and unstructured sources;
3. Track and monitor pipeline health; be modular and sustain loss-less data flow;
4. Identify and quarantine sensitive and PII data instantly;
5. Manage and fix for schema drift and data quality;
6. Segregate and evaluate data for storage in long-term storage, data lakes, or SIEM/observability tools
7. Automate the transformation of data into different formats for different destinations;
8. Save engineering team bandwidth which can be deployed on more strategic priorities

Curious about how agentic AI can solve your data problems? Get in touch with us to explore Cruz, our agentic AI data-engineer-in-a-box to solve your telemetry data challenges.