DataBahnai Positions Data-Layer Optimization as Cost Focus in Evolving Security Stack

According to a recent LinkedIn post from DataBahnai, industry discussions on SolCyber Security Shorts suggest that much of the data ingested into security information and event management systems may be low-value noise. The post highlights what is described as a “first-mile problem,” where cost, detection quality, and vendor lock-in are shaped before data reaches SIEM, data lakes, or analytics layers.

The company’s LinkedIn post indicates that many chief information security officers are seeking to retain ownership of their security data in cloud data lakes from providers such as Microsoft, Databricks, AWS, or Google. This perspective implies a shift in SIEM’s role toward being primarily an analytics layer, which could favor vendors that enable flexible routing and normalization of data across multiple storage and analytics environments.

The post also suggests a structural change in security operations as AI agents increasingly become the primary consumers of security data. As described in the content, token costs for large language models may function as a new form of ingestion cost, making unfiltered or un-normalized telemetry economically inefficient and potentially compressing margins for organizations that do not optimize their data pipelines.

By positioning its offering around selecting and routing only high-value telemetry, DataBahnai appears to be emphasizing cost control and data-ownership flexibility as key value drivers. For investors, this framing points to potential demand from enterprises seeking to reduce SIEM and AI processing expenses while avoiding vendor lock-in, which could support recurring revenue opportunities in data-layer optimization if the approach gains broader adoption.