The Cybersecurity Alert Fatigue Epidemic

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

Even as CISOs and SOCs are under tremendous pressure to secure their businesses against the rising tide of increasingly sophisticated cyberattacks. Most enterprise Security Operations Centres (SOCs) and Chief Information Security Officers (CISOs) continue to rely on Security Information and Event Management (SIEM) solutions; however, the dynamic and changing digital environment is making SIEMs, particularly older SIEM systems, increasingly challenging for the teams managing them.

While a vast majority of enterprises (80%+) utilize SIEMs as one of the core foundational tools in their security stack, legacy SIEMs present a significant challenge to SOC efficiency. Legacy SIEMs were designed as monolithic, on-premise aggregators of security logs and can severely impede SOC operations today, which require a more flexible approach. In this piece, we shall take a critical look at the drawbacks of legacy SIEMs and propose a modular approach to future-proof SIEM and SOC operations for enterprises.

Why are legacy SIEMs a problem?

SIEMs were first developed in the early 2000s for enterprises that managed their computing infrastructure and software they owned and operated, typically within their own office space. The term “SIEM” was coined by Gartner in 2005; however, early SIEM products date back to 2000, when ArcSight was released. Legacy SIEMs focus on log collection, rule-based correlation, and manual investigation workflows.

These systems were not designed to manage cloud-based platforms where data volumes have expanded and scaled. Security logs must be collected from digital devices and endpoints that are not limited by geographic boundaries. They are restricted in their scalability, speed, and adaptability to modern cloud-native environments and threats. These limitations arise from the fundamental design of these tools, leading to various challenges. Some of these challenges are:

Cloud and Hybrid Environments

Legacy SIEMs face substantial limitations in integrating with cloud-based environments and emerging technologies, such as containers, microservices, and serverless systems, as well as with new network architectures. Most of these SIEMs lack the necessary solutions and modules, like APIs, connectors, or native support, to ingest data from modern cloud and hybrid platforms seamlessly. SOCs must invest considerable engineering resources and expenses to link legacy SIEMs to an evolving cloud-native environment and maintain those telemetry pipelines.

Connecting and Collecting Security Data and Logs

Legacy SIEMs require point-to-point integration within a vast, complex, and diverse technological ecosystem. Maintaining these integrations over time demands ongoing engineering effort. There are no pre-built integrations for connecting SIEMs to commercial off-the-shelf solutions, and integrating with and managing data ingestion from custom in-house applications and microservices is even more challenging. Additionally, they lack telemetry pipeline health tracking to detect and resolve any disruptions in the pipeline.

Data Ownership and Avoiding Lock-in

Legacy SIEMs were designed to store all your security data and continue to serve as the all-purpose repository for numerous enterprises today. Many enterprises face complex data retention regulatory requirements, which bind them to their SIEM solution until the data retention requirement period has elapsed. Legacy SIEMs lack the flexibility and analytics capabilities in data storage that modern cloud-based data lake solutions offer, creating a significant opportunity cost in utilising SIEMs for storage.

Data Formats and Standardization

Legacy SIEMs were not designed to ingest and parse logs in various formats, then route those data streams in a way and format that downstream platforms can effectively utilise. Parsing, tagging, segmenting, and transforming data into different formats and data models for routing, visibility, and analytics consumes considerable data engineering resources. SOCs inevitably determine which data elements to transform, resulting in blindspots and gaps in visibility and management.

Cost Efficiency

SOCs and CISOs face increasing challenges due to ingestion and computational costs that significantly exceed the basic infrastructure expenses for legacy SIEMs. Moreover, data ingestion licenses and storage costs exert additional pressure as data volumes grow at twice the rate compared to IT budgets year-on-year. Beyond the sticker price for legacy SIEMs, the maintenance and updates of older SIEM products involve patch management, tuning, and configuration optimization. 

CISOs and SOCs remain attached to their legacy SIEMs because these systems provide context and have been optimized over time to minimize false positive rates. They are also concerned about the uncertainty, costs, and efforts involved in migrating to a new SIEM setup. However, if CISOs and SOCs want to enjoy the right SIEM for them (whether it’s legacy or next-gen) while ensuring that it can keep up with modern security threats, a modular SIEM architecture may be the solution.

Why should SIEMs be modular?

SIEM platforms serve as comprehensive solutions, incorporating data collection, log management, security data storage, threat detection, and incident response. A modular SIEM architecture divides these functions into separate, independently scalable components, enabling each to develop or be substituted without affecting the overall system. This strategy allows organizations to utilize best-in-class tools for auxiliary functions like log management, storage, and response, while SIEMs focus on their primary strength: advanced threat detection. By separating non-core functions, SIEMs can deliver quicker, more precise threat detection, integrate more seamlessly with other security tools, and scale more effectively to address changing security and compliance demands.

Roadmap for Implementing Modular SIEM Architecture

Phase 1: Data Tiering between SIEM and SDL/EDL

Lower SIEM licensing cost, no vendor lock-in, and higher data ownership‍

This phase separates data ingestion, data storage, and threat detection from the SIEM. This modular approach enables SOCs to select best-of-breed tools while maintaining complete control over their security infrastructure. Data ingestion can be managed by a Security Data Fabric orSecurity Data Pipeline Platform, which can handle schema drifts, provide visibility into data flow, connect to various systems and tools, and distribute data to multiple consumers without delays while minimizing ingress and egress costs. 

For data storage, decoupling SIEM from storage for non-security-relevant data unlocks significant cost efficiency and analytics capabilities.

1. Event Filtering:

SIEMs only receive pertinent security data to have all the threat models and policies defined.

2. Enterprise Data Lake Partitioning:

Partition tables within your Enterprise DataLake to host your Security Data Lake, requiring little or no maintenance for threat hunting capabilities.

3. Pre-processing Data:

Ensure data is parsed before seeding it into the data lake, making it usable. Dumping raw data into the Security Data Lake is ineffective; this is a key requirement of the Security Data Fabric /Security Data Pipeline Platform.

4. Cold Storage Utilization:

Push any compliance-related data that is not immediately needed to cold storage, with the ability to replay the data if required.

With only pertinent data going into the SIEM, detection rates should improve while reducing false positives. Threat hunting teams can leverage the complete data set in the security data lake needed to perform hunting activities. A SOAR that integrates with your SDL and SIEM should automate mature processes where required.

Phase 2: Headless SIEM Architecture using SDL/EDL

Future-proof architecture with optimized, AI and analytics-friendly data storage

The Next-Gen adaptive, intelligent HeadlessSIEM architecture focuses on what matters to CISOs — effective threat detection— rather than infrastructure concerns like plumbing and storage. Threat detection content is leveraged via the power of existing data lake for threat evaluation. A robust, GenAI-enabled SOAR automates responses and streamlines threat management.

Phase 3: AI-powered SOC and Data Analytics

Time: POC now with production timeline in 1 year

Just as CISO organizations seemed to have secured cloud computing and hybrid environments, the emergence of Generative AI is urging security teams to leverage lessons learned from their cloud adoption process to accelerate the protection and integration of this new disruptive technology. Security teams are dedicated to safeguarding generative AI capabilities while seeking methods to harness this technology to enhance their security measures. They aim to avoid limiting their use of Gen AI capabilities to just Co-Pilots. 

In today's ever-evolving threat landscape, CISO organizations are tackling various sophisticated threats while managing extensive institutional security data. The combination of this institutional knowledge with AI capabilities transforms enterprise security data into actionable insights. Currently, AI models mainly concentrate on web data, often neglecting the vital insights found within the organization’s security data. The security data fabric, along with the information in the Security data lake, helps bridge this gap by integrating institutional security data with AI, facilitating interaction with insights rather than just presenting irrelevant raw security data. Retrieval-AugmentedGeneration (RAG) enables your security data to be included in the prompts used to query the LLM model, thereby providing valuable detection insights and strengthening your threat-hunting capabilities.

Additionally, integrating Identity, Vulnerability, and GRC data into the security data lake would enhance the efficiency and modularity of the security stack. This approach allows for the use of best-of-breed solutions, facilitating theadoption of new technologies to defend against emerging threats and comply with the fast-changing regulatory landscape. It also ensures full data control while optimizing the security budget. 

Value Delivered by Modular SIEM

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Future Outlook

The proposed architecture supports an adaptive security posture for the foreseeable future. Security data fabric will become an integral part of the organization, functioning as a centralized data bus. SOAR can truly evolve into a security orchestration and automation platform by not only automating a few incident response playbooks but also streamlining vulnerability ticketing, quarterly access certifications, third-party vendor questionnaires, and GRC metrics reporting, all based on the data orchestrated by the security data fabric.

Conclusion

Today’s consolidated platform approach comes with a significant risk of vendor lock-in at the cost of innovation when the threat landscape is changing at a rapid pace. Leading security market players should embrace standards so that the data is interoperable on any SIEM platform rather than locking their customers with a proprietary data format and making it difficult to switch SIEM providers. The SIEM vendor that builds adapters to seamlessly operate on existing proprietary formatted log data from other impacted SIEM vendors due to consolidation will take the market share faster than consolidated platforms.

The current SIEM market consolidation will force CISO organizations to fast-track adoption of flexible, modular, future proof security architecture. By standardizing log data collection and ingestion, segmenting data storage based on usage leveraging standardized format and adopting innovative best-of-breed threat detection capabilities on top of standardized data, the organizations will be well positioned to take advantage of emerging technologies just as the threat actors are doing. This will help the CISO organizations to stay toe-to-toe with threat actors, if not a step ahead.

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.