The New Backbone of Cybersecurity: Why Data Quality is Winning Awards

STAMFORD, CT – November 26, 2025 – In the hyper-competitive cybersecurity market, a small but significant shift is gaining momentum, moving from a singular focus on threat detection to a foundational emphasis on the data that fuels it. This trend was cast into the spotlight recently when Axoflow, a Connecticut-based innovator, secured two coveted 'Top InfoSec Innovator Awards' for SOC Optimization and Security Data Pipeline Management. Presented by Cyber Defense Magazine during the influential RSA Conference, these awards are more than just a corporate accolade; they are a market validator for an emerging strategic imperative: treating security data as a managed asset, not an operational burden.

For years, the default strategy for Security Operations Centers (SOCs) has been to collect everything. This 'hoard-and-hope' approach, however, has led to what many CISOs now call a crisis of data. Security Information and Event Management (SIEM) platforms, the traditional nerve centers of the SOC, have become prohibitively expensive, with costs scaling directly with data ingestion volumes. The result is a paradox where security teams are simultaneously drowning in data yet starved of actionable insight.

“Security teams are drowning in data they can’t trust or use. Throwing everything into a SIEM doesn’t work - data arrives noisy, incomplete, and at sky-rocketing costs,” stated Balazs Scheidler, CEO and Co-Founder of Axoflow in the company's announcement. This win suggests the industry is finally ready to embrace a new model.

The Data Deluge Drowning the Modern SOC

The challenges Axoflow aims to solve are deeply felt across the industry. Enterprises generate petabytes of telemetry from a sprawling landscape of cloud services, network devices, endpoints, and applications. This raw data is often unstructured, redundant, and riddled with irrelevant 'noise' that provides no security value. When ingested directly into a SIEM, this low-quality data creates a cascade of problems.

First is the immense financial strain. Budgets are consumed by ingestion and storage fees for data that is often unused. One CISO at a major financial services firm, speaking on condition of anonymity, described it as “paying a premium to store digital garbage.” This forces security leaders into a difficult trade-off: either absorb the escalating costs or selectively drop data sources, creating dangerous blind spots.

Second is the human cost. Highly skilled security analysts spend an inordinate amount of time on 'data wrangling'—manually parsing, normalizing, and enriching logs to make them usable for investigation. This tedious work is a primary driver of analyst burnout and contributes to the high turnover rates plaguing SOCs globally. The result is slower investigations, increased mean time to respond (MTTR), and a higher risk of a minor incident escalating into a major breach.

A New Architecture for Data Control

Axoflow’s award-winning platform represents a new architectural approach: an autonomous data layer that sits between data sources and analytics tools. Instead of a simple pipeline, the company has engineered what it calls an “end-to-end architecture” dedicated to autonomously managing the security data lifecycle. This includes discovering new log sources, classifying data types, parsing disparate formats, and enriching logs with critical context before they ever reach the SIEM.

This is where the platform's differentiation from competitors like Cribl or custom-built open-source solutions becomes apparent. While other tools offer powerful routing and filtering capabilities, Axoflow emphasizes a higher degree of automation. “We created an end-to-end architecture that identifies new sources automatically, keeps pace as vendors change formats, and delivers clean, consistent data to any SIEM or analytics tool - without babysitting security data,” explained Sandor Guba, Axoflow's CTO and Co-Founder.

The market impact of this approach is tangible. Axoflow reports that its customers achieve up to 50% reductions in SIEM ingestion costs and accelerate investigations by as much as 70%. By intelligently reducing non-essential data and routing high-value, enriched logs to the expensive SIEM while offloading lower-value compliance data to cheaper storage, organizations can stabilize their security spend while enhancing visibility.

Beyond Vendor Lock-In: The Strategic Value of Open Standards

Perhaps one of the most strategic aspects of this new data-centric model is its commitment to open standards. For too long, organizations have been shackled by proprietary data formats, locking them into a single vendor's ecosystem. This lack of interoperability has stifled innovation and made it difficult to adopt best-of-breed tools without costly and complex integration projects.

Axoflow’s platform counters this by being vendor-agnostic and embracing open formats like the Open Cybersecurity Schema Framework (OCSF) and Apache Parquet. OCSF provides a standardized schema for security events, allowing different tools to communicate and understand data seamlessly. Parquet, a columnar storage format optimized for analytics, enables highly efficient and cost-effective querying of massive datasets, which is essential for building modern security data lakes.

By normalizing data into an open format, an organization regains ownership and control. It can send critical alerts to its primary SIEM, route network traffic logs to a specialized analytics platform, and store full-fidelity raw logs in a cost-effective data lake for long-term compliance and threat hunting. This flexibility not only future-proofs the security architecture but also shifts the balance of power back to the enterprise, enabling them to select tools based on capability, not data compatibility.

Fueling the AI-Powered Future of Cybersecurity

The timing of this shift toward data quality could not be more critical. As the industry pivots toward AI-driven defense to combat increasingly sophisticated, AI-assisted attacks, the adage 'garbage in, garbage out' has never been more relevant. The effectiveness of any machine learning model or AI security algorithm is entirely dependent on the quality of the data it is trained on.

An autonomous data layer provides the clean, structured, and contextualized data that is the essential fuel for these next-generation systems. Without it, AI initiatives are likely to fail, bogged down by false positives and an inability to distinguish real threats from background noise. By delivering a reliable and observable data foundation, platforms like Axoflow are not just optimizing current SOC operations; they are laying the groundwork for the AI-powered SOC of the future.

As Scheidler noted, “a reliable, observable, and cost-efficient security data layer becomes the backbone of the modern SOC.” This perspective reframes data management from a cost center into a force multiplier. As organizations look to harness the power of AI for threat detection and automated response, the strategic value of a well-managed data layer will only continue to grow, making it a cornerstone of resilient and effective cybersecurity programs.