Data Engineering for Cybersecurity sets out to bridge a gap many security teams encounter: knowing what to do with the flood of logs, events, and telemetry they collect.
About the author
James Bonifield has a decade of experience analyzing malicious activity, implementing data pipelines, and training others in the security industry. He has built enterprise-scale log solutions, automated detection workflows, and led analyst teams investigating major cyber threat actors.
Inside the book
The book is organized into four parts. The first, “Foundations of Secure Data Engineering,” introduces core concepts like getting data into a SIEM, managing throughput, and standardizing events. Bonifield covers data serialization formats such as JSON, YAML, and the Elastic Common Schema, and also explains the importance of temporary centralization and event caching. This section sets the stage for readers who might not have formal data engineering training but work daily with data in a security context.
The second part, “Log Extraction and Management,” digs into the collection side. There are dedicated chapters on endpoint and network data, Windows logs, integrating and storing data, and working with syslog. These chapters walk through tools like Filebeat, Winlogbeat, and Rsyslog, along with how to configure TLS for secure data transport. The focus is on reliable ingestion, with side discussions on pruning and privatizing sensitive data before it moves downstream.
Part three, “Data Transformation and Standardization,” looks at manipulating incoming data to make it more useful. Bonifield explains how to set up Logstash pipelines, apply transformation filters, and enrich events with additional context. The emphasis is on making data consistent and usable for detection, response, and threat hunting. This section will feel familiar to anyone who has worked on mapping diverse logs into a common schema, but it also includes concrete examples that help clarify the process.
The final part, “Data Centralization, Automation, and Enrichment,” focuses on scaling and efficiency. It covers centralizing security data in an environment like Elasticsearch, automating tool configurations with Ansible, and caching threat intelligence feeds. These chapters show how automation can reduce manual effort while improving consistency across a security program.
One of the book’s strengths is its emphasis on open source tooling. Every example is grounded in tools most teams can access without budget approval, making it practical for both enterprise and resource-constrained environments. While the steps can be detailed, Bonifield avoids assuming too much, and the explanations are clear enough for security professionals who may not be full-time engineers.
The style is instructional without being dry. Screenshots, configuration snippets, and logical progressions make it easy to follow along. The book does not spend much time on the “why” of security data collection, assuming the reader already understands the value. Instead, it focuses on the “how,” which is likely what most practitioners will be looking for.
Who is it for?
Data Engineering for Cybersecurity is best suited for security engineers, SOC analysts, and incident responders who want to improve their data pipelines or take more ownership of their log and telemetry flows.
If your team needs to move from ad-hoc log collection to a structured, automated, and secure pipeline, this book provides a roadmap you can adapt to your own environment.
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