From Spreadsheets to Systems

Remember when your detection rules fit in a single spreadsheet? For many teams, that nostalgic moment marks the beginning of a journey that leads through increasing complexity to an uncomfortable realization: the tools that got you started won't get you where you need to go.

The Spreadsheet Era: Where We All Begin

Every detection program starts simply. You have a handful of rules in your SIEM, a spreadsheet tracking what each rule does, maybe a Wiki page with deployment instructions, and email threads discussing new detection ideas.

This works brilliantly... for the first 20 rules.

The Breaking Point: When Simple Becomes Simplistic

As detection programs mature, cracks appear in the spreadsheet foundation:

The Transitional Phase: Duct Tape and Determination

Recognizing spreadsheet limitations, teams often build elaborate workarounds. The "Spreadsheet Plus" approach involves master spreadsheets with pivot tables, separate sheets per platform, macros for common operations, SharePoint workflows for approval, and scripts to generate detection code.

Progressive teams might add Git repositories for detection code, CI/CD pipelines for testing, Markdown documentation, and YAML configuration files. These improvements help, but they create new problems: multiple sources of truth, synchronization challenges, tool proliferation, and knowledge silos.

The System Mindset: Detection Engineering Grows Up

Modern detection engineering requires a fundamental shift from managing files to running systems. In the spreadsheet era, rules are just rows in a table. In the systems era, rules become living entities with telemetry, state, and behavior.

Instead of static documentation, modern systems automatically track deployment status across platforms, performance metrics in real-time, change history with full context, and dependencies between rules. The difference is like comparing a photograph to a live video feed.

The transition from manual to automated changes everything. Instead of copy, paste, deploy, document, you define once and deploy everywhere. Automation transforms operations through API-driven deployments, automated testing and validation, continuous performance monitoring, and self-documenting systems.

Perhaps most importantly, detection data stops living in isolation. Instead of spreadsheets disconnected from operational reality, everything connects. Integration enables real-time performance feedback, automated tuning recommendations, coverage gap identification, and threat intelligence correlation. Your detection management system becomes a living part of your security operations, not a static document sitting beside them.

The Principles of Systematic Detection Management

Four principles guide systematic detection management. First, establish a single source of truth. One system owns detection state—not a spreadsheet plus three wikis plus tribal knowledge. One system, comprehensive and authoritative.

Second, automate the entire lifecycle. From creation through retirement, every stage has automated support. Development happens with templates and libraries, testing uses validation frameworks, deployment flows through APIs, monitoring provides integrated metrics, tuning relies on performance data, and retirement occurs when rules become obsolete.

Third, embrace platform agnosticism. Your detection logic is more valuable than any single platform. Modern systems abstract detection content from implementation details, enabling multi-platform deployment, platform migration without rewrites, and best-of-breed tool selection.

Fourth, operate based on metrics. Every action generates data, and every decision uses data. Which rules need tuning? Check the false positive metrics. Where are coverage gaps? Review the MITRE mapping. What's the team working on? See the deployment pipeline. Data drives decisions, not intuition or politics.

What Modern Detection Systems Enable

Modern detection systems transform operations for everyone. Leadership gets strategic visibility through real-time detection coverage maps, performance metrics and trends, resource allocation insights, and risk-based prioritization. They can finally answer board questions about security posture with data.

Engineers gain operational efficiency through rapid rule development and deployment, automated testing and validation, performance feedback loops, and collaborative workflows. Instead of spending 70% of their time on manual tasks, they can focus on building new capabilities.

Analysts benefit from reliable detection through trustworthy, tuned detections, clear rule documentation, performance transparency, and reduced false positives. Alert fatigue decreases as detection quality improves.

Making the Transition: A Practical Path

Making the transition requires a practical approach. First, acknowledge reality. Your spreadsheet served you well, but it's time to evolve. Second, start where it hurts most. If deployment takes too long, start with automation. If you have no performance visibility, begin with metrics. If version control is chaos, implement proper repositories.

Third, build incrementally. Don't try to transform everything overnight. Pilot with one platform, prove value with metrics, expand systematically, and maintain momentum. Small wins build credibility for larger changes.

Fourth, embrace the culture shift. Moving from spreadsheets to systems isn't just technical—it's cultural. The shift goes from documentation to automation, reactive to proactive, individual to collaborative, and static to dynamic.

The Future of Detection Engineering

The evolution from spreadsheets to systems mirrors the broader transformation of security operations. Just as infrastructure moved from manual server management to infrastructure-as-code, detection engineering is embracing Detection-as-Code principles, GitOps workflows, Observability practices, and Platform engineering approaches.

Teams that make this transition don't just manage detections better—they fundamentally transform their security operations, moving from reactive rule management to proactive threat coverage optimization.

The Bottom Line