What is Auto-Parsing?

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A mature security program relies on data, yet security data creates unique challenges for security analysts and data scientists. For security and data science teams to collaborate, they need to normalize divergent data formats to create clean data optimized for various use cases.

Auto-parsing identifies the relevant data elements to transform security data into a consistent format. This process breaks down data silos for enhanced visibility into the organization’s security posture and efficiency. 

What is auto-parsing? 

The act of parsing, also called syntax analysis or syntactic analysis, is the process of using a technology called a parser to extract data elements from structured, semi-structured, and unstructured data across divergent formats to convert the data into a consistent format. Auto-parsing uses specifically developed parsers to automatically breakdown data into a format that is easier for analysis. In the extract, transform, and load (ETL) pipeline, parsing occurs as part of the transform process. 

What are the benefits of auto-parsing and data normalization? 

By converting raw, unstructured data into formats that are easier to analyze, use, or store, auto-parsers benefit data users as follows: 

  • Reduced time and costs: Auto-parsing reduces manual parsing by data engineers and analysts and the data source maintenance if the data feeds change over time.  
  • Data flexibility: Using a standardized data format enables teams to reuse data across various use cases. 
  • Higher quality: Normalizing data structures enables organizations to identify duplicated data, reduce inaccuracies, detect errors, and remove inconsistencies. 
  • Improved analytics: Clean data enables organizations to enhance data analytics’ accuracy and depth.  
  • Breaking data barriers: Creating a single structure and format across all data means that users can identify patterns across otherwise siloed datasets.

Why organizations should consider auto-parsing security data and telemetry 

Auto-parsing security telemetry is the foundation upon which organizations can build the analytics that enable valuable insights. However, many of the security tools that parse and normalize security data, like log aggregators and security information and event management (SIEM) platforms, still require manual parsing and upkeep of data sources and parsers. In addition, parsing all security data sources into a security data lake is a heavy engineering lift, and auto-parsing can automate this process. This is because traditional tools built to identify and investigate incidents were not built with data science in mind and often fail to work cohesively with analytics tools. Some challenges include:

Lack of resources 

Although companies may want to build a security data parser, they need people who can design it and compute resources to process it. Many companies may lack the financial, technical, and staffing resources to create a security telemetry parser, especially since it requires specific knowledge about log data formats. Complex security semantics require years of experience with and exposure to the tools. Organizations struggle to hire security analysts because the talent gap creates a shortage of experienced workforce members.

Testing and maintenance time 

Building and maintaining a custom data parser is time-consuming, even if the organization has the people and compute power necessary. Further, testing and maintaining data ingest and parsing can become time-consuming, often making the project cost ineffective. Organizations spend time on research, infrastructure development, and testing. Further, the organization must optimize the parser’s performance and update it for new data formats or sources.  

Why auto-parsing and data normalization are critical for optimizing security telemetry’s value 

With an auto-parser built for security data, organizations can gain insights from the vast amounts of data they collect and enable cross-functional stakeholders to communicate more effectively. However, when choosing a vendor, organizations should look for technologies that provide transparency and enable customization.

Normalize Diverse Log Formats 

At the enterprise level, an organization may have hundreds of technologies generating logs across any of the following formats: 

Apache Amazon Cloudfront  Amazon ELB  HTTP Headers 
Java  JSON  Linux System  MySQL 
Nginx  Node JS  PAM  PHP
Rails Syslog Windows  Python
MongoDB  Heroku Logs  HAProxy Logs   


Parsing this data enables organizations to create a standardized format across their current IT and security tooling, giving them greater visibility into threats facing their environment. Further, some events, like network traffic, may be reported by multiple tools, like firewall and network device logs. Parsing enables organizations to eliminate duplicative data for more accurate analytics.  

Make correlation easier from a variety of data sources 

Most security tools only normalize structured data, like log files. However, organizations collect important semi structured and unstructured security data like: 

  • Threat intelligence feeds 
  • Incident response reports 
  • Organizational data 

With a robust data parser, organizations can optimize their security data’s value by incorporating these unstructured data types into their analytics and reporting. 

Use case example: Continuous controls monitoring and reporting 

By auto-parsing, flattening, and normalizing data within the first steps of the data pipeline, organizations can establish a single source of information across IT, security, compliance, and senior leadership teams. Continuous controls monitoring (CCM) is the process of ensuring that technical controls continue to work as intended, so correlating this data across the complex environment is critical. Reports for compliance translate this technical information into business language that enables key stakeholders to make data-driven, informed decisions.  

By breaking down these silos, organizations can accelerate their governance, risk management, and compliance (GRC) maturity by: 

  • Gaining real-time, year-round visibility into historical controls and compliance trends 
  • Identifying and remediating control gaps 
  • Leveraging data analytics and business intelligence tools for reporting across executives, business, IT, and compliance teams 
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