Understanding the 'Eat the Cost' Mentality

In the realm of cybersecurity, the phrase "eating the cost" often surfaces during budget discussions or strategic planning meetings. Security leaders, faced with the complex task of safeguarding digital assets while balancing financial constraints, sometimes opt to absorb certain expenses as part of their operational strategy. This decision, while seemingly straightforward, underscores a deeper commitment to resilience and efficiency in the face of ever-evolving cyber threats.

One of the more tactical yet significant ways security teams can "eat cost" without compromising on safety is by addressing the ingestion of duplicate events across various data sources. For instance, cloudflare DNS and CrowdStrike DNS logs might both record similar activities, leading to redundancy. This redundancy not only strains storage and processing resources but also dilutes the focus of security analysts, who must sift through voluminous logs to identify genuine threats.

Our study introduces a Python-based solution that utilizes Natural Language Processing (NLP) and schema comparison techniques to tackle the challenge of duplicate event entries across different log sources. By leveraging the SpaCy library for parsing and comparing log schema fields, we observed significant advancements in data analysis and event correlation. The research, which analyzed 207 distinct log schemas from Panther, revealed a notable percentage of duplicated fields, highlighting the potential for streamlining log data management. Notably, the SentinelOne_DeepVisibilityV2 schema showed the highest number of similarity matches (3710), while the Zeek_OCSP schema exhibited the highest percentage of similarity matches at 7.62%. These findings underscore the critical need for efficient log data analysis in cybersecurity, pointing to the considerable impact of duplicated data on storage requirements and the complexity of data analysis efforts.

In conclusion, when security leaders decide to "eat the cost," it is not merely a financial decision but a strategic move towards operational efficiency and effectiveness. By eliminating unnecessary noise from security data, analysts are better positioned to detect and respond to genuine threats with greater accuracy and speed. The result is a more agile, responsive security operation that can adapt to threats with minimal resource wastage. As cybersecurity challenges continue to grow in complexity, such strategic decisions will become increasingly critical in shaping resilient security postures that can withstand the pressures of the digital age.