Building Detection Engineering Systems That Scale

Coming 2026

Detection engineering is not query writing.

It is the disciplined design, governance, and continuous refinement of security signal.

The LogSmith Playbook is a structured field guide for engineers and security leaders who want to move beyond alert chaos and build detection programs that scale — technically, operationally, and organisationally.

This is not theory.
It is built from real enterprise environments where structural weakness — not search syntax — was degrading signal quality.

Why This Exists

In one engagement, the platform was generating over 20,000 alerts per day.

The SPL was not terrible.

The system was.

There was:

• No naming discipline
• No ownership model
• No lifecycle governance
• No version control
• No structured deployment model
• No analyst feedback loop
• No performance guardrails

Signal was being diluted by the absence of engineering structure.

Detection engineering must be treated as an engineering discipline — not an operational afterthought.

The LogSmith Playbook exists to document what that discipline looks like.

The Structure of the Playbook

The Playbook is divided into four parts.

Each represents a core discipline required to build mature detection engineering systems.

Part I — The Forge

Data, Structure, and Foundations

Before signal can exist, the metal must be prepared.

The Forge establishes the foundations:

• CIM / ECS alignment discipline
• Field normalisation and enrichment strategy
• Asset and identity context integration
• Data quality governance
• Performance-aware search design

Detection built on unstable data will always fracture.

The Forge ensures the foundation holds.

Part II — The Craft

Designing Signal With Intent

The Craft focuses on deliberate detection design.

It covers:

• Hypothesis-led detection engineering
• ATT&CK-aligned behavioural modelling
• False positive modelling before deployment
• Risk-based prioritisation
• Naming conventions that scale
• Ownership and lifecycle governance

This is where detections stop being searches and become engineered artefacts.

Part III — The Machine

Git-First SIEM and Content at Scale

Signal does not scale without structure.

The Machine introduces a Git-First SIEM operating model:

• Git-backed detection repositories
• Pull request review discipline
• Structured promotion across environments
• Automated validation and performance guardrails
• Version control and transparent change history

This is not DevSecOps theatre.

It is detection treated as production software.

Part IV — The Mind of the LogSmith

Discipline, Signal, and Continuous Refinement

Tools do not create signal.

Engineers do.

The final part explores:

• Analyst feedback as operational telemetry
• Measuring signal quality over alert volume
• Alert fatigue reduction strategies
• Structured detection health reviews
• Building long-term detection intuition

The Mind is what separates query writers from detection engineers.

Who This Is For

The LogSmith Playbook is written for:

• Detection engineers
• Splunk Enterprise Security practitioners
• LogScale / SIEM engineers
• SOC technical leads
• Security architects

It assumes familiarity with complex environments and focuses on improving maturity — not introducing basic concepts.

Current Status

The LogSmith Playbook is currently in development and scheduled for release in 2026.

Chapters are being written and refined alongside live enterprise engagements to ensure the frameworks are field-tested and practical.

Early followers will receive:

• Preview chapters
• Governance templates
• Naming convention models
• Git-First SIEM framework patterns
• Detection lifecycle structures

About LogSmith

LogSmith is a structured approach to detection engineering.

It is built on the principle that signal must be forged — not assumed.

Through practical frameworks, governance models, and engineering discipline, LogSmith aims to raise the maturity of detection programs operating at scale.