In their article about how banks can extract value from a new generation of AI technology, notable strategy and management consulting firm McKinsey identified AI-enabled data pipelines as an essential part of the ‘Core Technology and Data Layer’. They found this infrastructure to be necessary for AI transformation, as an important intermediary step in the evolution banks and financial institutions will have to make for them to see tangible results from their investments in AI.

The technology stack for the AI-powered banking of the future relies greatly on an increased focus on managing enterprise data better. McKinsey’s Financial Services Practice forecasts that with these tools, banks will have the capacity to harness AI and “… become more intelligent, efficient, and better able to achieve stronger financial performance.”

What McKinsey says

The promise of AI in banking

The authors point to increased adoption of AI across industries and organizations, but the depth of the adoption remains low and experimental. They express their vision of an AI-first bank, which -

1. Reimagines the customer experience through personalization and streamlined, frictionless use across devices, for bank-owned platforms and partner ecosystems
2. Leverages AI for decision-making, by building the architecture to generate real-time insights and translating them into output which addresses precise customer needs. (They could be talking about Reef)
3. Modernizes core technology with automation and streamlined architecture to enable continuous, secure data exchange (and now, Cruz)

They recommend that banks and financial service enterprises set a bold vision for AI-powered transformation, and root the transformation in business value.

AI stack powered by multiagent systems

The true potential of AI will require banks of the future to tread beyond just AI models, the authors claim. With embedding AI into four capability layers as the goal, they identify ‘data and core tech’ as one of those four critical components. They have augmented an earlier AI capability stack, specifically adding data preprocessing, vector databases, and data post-processing to create an ‘enterprise data’ part of the ‘core technology and data layer’. They indicate that this layer would build a data-driven foundation for multiple AI agents to deliver customer engagement and enable AI-powered decision-making across various facets of a bank’s functioning.

Our perspective

Data quality is the single greatest predictor of LLM effectiveness today, and our current generation of AI tools are fundamentally wired to convert large volumes of data into patterns, insights, and intelligence. We believe the true value of enterprise AI lies in depth, where Agentic AI modules can speak and interact with each other while automating repetitive tasks and completing specific and niche workstreams and workflows. This is only possible when the AI modules have access to purposeful, meaningful, and contextual data to rely on.

We are already working with multiple banks and financial services institutions to enable data processing (pre and post), and our Cruz and Reef products are deployed in many financial institutions to become the backbone of their transformation into AI-first organizations.

Are you curious to see how you can come closer to building the data infrastructure of the future? Set up a call with our experts to see what’s possible when data is managed with intelligence.

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.