Why Your DIY Security Data Lake Might Just Sink Why Your DIY Security Data Lake Might Just Sink

September 21, 2023

Why Your DIY Security Data Lake Might Just Sink?

Drowning in Data, Yet Thirsty for Insight: A CISO’s Conundrum

Sarah, the battle-hardened CISO, oversees a formidable array of cybersecurity tools. Her organization has invested in next-gen IDS/IPS, state-of-the-art firewalls, and MFA protocols. They’ve even ventured into EDR, Zero Trust architecture, and vulnerability management tools to detect and remediate vulnerabilities proactively. Recently, her team rolled out an asset inventory tool to keep tabs on the growing number of devices and applications in the network. Each tool operates in its silo, generating its own set of alerts and reports, adding to the analytical workload rather than lightening it.

In this sea of complexity, Sarah feels like she’s drowning. Each security tool in her arsenal generates its cascade of alerts, with no easy way to prioritize or correlate them. While Security Information and Event Management (SIEM) systems often use data lakes to centralize real-time monitoring and alerting, Sarah faces a different, yet interrelated, challenge. Her conundrum concerns more than just real-time responses but also understanding and quantifying the broader risks associated with the current cybersecurity landscape. 

In the context of her security challenges, Sarah discovers that even her asset inventory tool, designed to provide a comprehensive attack surface picture, falls short of tying into the overarching cybersecurity landscape. Despite the variety of tools at her disposal, she notices glaring coverage gaps that none of her solutions adequately address.This barrage of information creates an ‘analysis paralysis,’ where crucial alerts can slip through the cracks, camouflaged by many false positives and low-priority issues. Her multiple dashboards feel like a maze with no exit, each one demanding specialized expertise and none offering a bird’s-eye view of the overall security posture. When it comes to her quarterly board meetings, the situation grows even grimmer. Sarah has an ocean of data but lacks a navigable map to sail through it. Board members ask pointed questions about cyber risk, but the fragmented data prevents her from painting a unified and quantifiable picture. The tools that should empower her create a web of confusion, leaving Sarah and her team in perpetual anxiety and uncertainty. 

Sarah’s plight is not isolated; it’s more a pervasive issue. When it comes to cybersecurity, the challenges Sarah faces in navigating the maze of fragmented data and disparate tools echo the frustrations that we have often heard in our discussions with Fortune 1000 prospects.

DIY Security Data Lakes- A Bane or A Boon

A security leader from one of Canada’s leading Integrated Health Services companies echoed this sentiment:

“Although our dashboard is 80% automated, sourcing data through APIs from third-party assessment tools, vulnerability management systems, phishing awareness programs, and our SOC, the remaining 20% manual effort unveils a significant issue. Even with all these categories of tools, we’re not equipped to convey what the board most needs to understand. I want to be able to tell them, ‘Our existing risks could result in a $5 million loss by the end of the year.’ Without that level of quantified risk assessment, our array of tools and data falls short of grabbing the board’s attention on the critical issues at hand.”

The approach they followed to solve the problem of siloed data from tools was the creation of a DIY data lake. Such repositories are prevalent among large organizations, serving as centralized hubs for aggregating and analyzing a wide array of raw security data from various tools and platforms. The ultimate aim is to provide a cohesive snapshot of an organization’s cyber risk landscape for facilitating more informed decision-making. However, given the challenges many of our customers and prospects have encountered, it raises serious questions about the actual effectiveness of DIY Security Data Lakes. What’s behind this discrepancy?

Our engagement with Fortune 1000 companies reveals that about 25% have invested in some form of security data lake, with annual expenditures often exceeding $5 million. Maintaining a data lake requires significant investments – ever-growing scale, increasing and diverse data sources, periodically changing data schemas and APIs from each such tool, evolving technologies – all require a considerable engineering effort and opex that security teams, already stifled for resources, cannot continually provide and budget for. And even when they try, this significant financial commitment hasn’t translated into satisfaction; fewer than 10% of executives believe these initiatives meet their objectives. Consequently, most are left struggling to quantify cyber risk effectively, raising serious doubts about the true value of these costly data lake endeavors.

Why do most DIY Security Data Lakes Fall Short?

The DIY security data lake concept may sound good on paper, but it’s fraught with challenges that can impede its utility, even in well-funded organizations. Here are some of the critical issues:

  • Dirty Data: The problem starts with data quality. When organizations throw in all kinds of data without adequate cleaning, deduplication, or categorization, the result is a polluted lake. 
  • Data Collection Issues: The data lake is only as good as the data sources you rely on. Organizations often face difficulty sourcing and collecting relevant data sources, leaving gaps.
  • Lack of Post-Collection Utility: Once data enters the lake, not only does retrieving it for meaningful analysis become a hurdle, but organizations also struggle to derive ongoing value from it. A case in point is when a data lake is built for a specific purpose, such as compliance, but fails to be helpful for any subsequent initiatives. Moreover, without compelling data correlation, organizations miss the opportunity to map cybersecurity or other operational elements to broader business contexts and strategies, resulting in lost opportunities for actionable insights.
  • Limited to Keyword Search: Despite initial ambitions for advanced analytics, organizations often find themselves constrained to basic keyword searches in log files. Such limitations undermine the data lake’s potential to serve as a robust tool for complex analysis and insight.
  • Complexity of Analytics: Organizations frequently overlook the complexity of establishing analytics within a data lake. This oversight can lead to ballooning costs and delayed timelines, resulting from challenges like algorithm configuration and data type integration, which ultimately compromise the lake’s effectiveness.
  • Lack of Defined Security Objectives and Use Cases: Organizations often invest in data lakes with broad goals like “detect threats” but without actionable plans, metrics, or clearly defined security use cases—this lack of focus results in a costly infrastructure that fails to deliver on its security monitoring potential.
  • Talent Gaps: The success of a data lake not only hinges on analytics and extensive data expertise but also requires skills in machine learning algorithms and artificial intelligence. Organizations commonly face challenges in recruiting proficient professionals in these advanced domains. This skill shortage can result in underutilized resources and a failure to unlock the full potential of the security data lake.
Critical issues in most DIY security data lakes
Reasons why most DIY security data lakes fall short

Rethinking DIY Security Data Lakes

The shortcomings of traditional DIY security data lakes invite a pressing question: Is there a better way to aggregate and make sense of the sprawling cybersecurity landscape? Organizations desperately need a more holistic framework that serves as a unified repository for data across various security, IT, and business tools and scales efficiently while managing costs. The goal should be to provide real-time, continuous visibility into cyber risk, with actionable, inspectable insights that offer clear guidance on aspects like which software needs patching and how to choose among multiple patch options.

Imagine trying to conduct a symphony orchestra without a musical score, where each section of musicians plays from different compositions. In this scenario, extensive data models and connector frameworks act like the musical score, coordinating the instruments (data sources!) to produce a harmonious sound. They provide the structure for data ingestion, correlation, deduplication, and aggregation at individual asset levels, laying the groundwork for effective cybersecurity. Without this coordinating score, each section may play its tune well, but collectively, they produce a cacophony, making it nearly impossible to understand the intended composition. Therefore, when assessing the limitations of DIY data lakes, the importance of well-designed data models and frameworks that can integrate various data sources should not be overlooked.

Wrapping up:

In conclusion, while DIY Security Data Lakes offer the promise of centralizing and analyzing vast amounts of security data, they often fall short due to issues like data quality, complexity, and lack of focus. Organizations truly need a more cohesive and scalable solution that offers actionable, inspectable insights for managing cyber risk effectively. Stay tuned: in the next blog, we will discover how Balbix’s rapid integration framework works and delivers strategic value.If you’d like to learn more about how Balbix can eliminate the headaches associated with deploying and managing a data lake for risk and cybersecurity posture management, please sign up for a 30-minute demo.