Organizations are drowning in security alerts, yet still missing the most critical threats. The truth is, most detection programs fail because they treat detection engineering as a one-off task rather than a disciplined, iterative process. Collectively, we should stop thinking of detection as just another checkbox and start treating it as a continuous lifecycle—one that requires precision, validation, and constant improvement.

As cyber threats mature, detection becomes more than just a defensive play; it’s an engineering discipline that, when done right, can make the difference between catching an attacker early and becoming the next headline. This blog will guide you through the four phases of the most effective detection engineering process step-by-step, so you can bolster your detection strategy and quickly mitigate threats.

Phase 1: Build Your Detection Library

The first critical step is developing a comprehensive detection library tailored to your organization’s needs. This is where detections are continuously added, modified, and deprecated and forms the backbone of a robust detection strategy, enabling your organization to swiftly respond to new threats without starting from scratch.

There are three main steps to building out your detection library:

Step 1: Prioritize Detections That Present the Biggest Risk to Your Organization

Prioritize your organization’s detections based on the needs of the business. This will help you effectively allocate resources while addressing the most critical threats first. By doing this analysis in advance, you can maximize the impact of the security operations team’s efforts and optimize security investments for the best return. Prioritizing detections can also reduce noise from false positives, allowing the security operations team to focus on genuine threats and respond more efficiently.

Step 2: Identify the Data Sources You Need (and the Ones You Don’t)

The next step is to make sure you have the data you need to create the detections you’ve prioritized. You need a variety of data sources for comprehensive visibility into your organization’s environment to enable quick and accurate threat identification. Some of your key data sources are likely foundational security technology (like EDRs and firewalls), historical data, threat intelligence, and any data specific to your business like phishing, business email compromise, and malware or ransomware.

Step 3: Determine the Right Mix of Detection Authors for an Adaptable Strategy

Finally, you need to determine who will create the logic to identify specific threats. Best practice is to include contributions from three main types of detection authors: technology vendors, internal security operations teams, and third-party security providers.

Using a combination of these authors helps achieve a multi-layered and adaptable detection handling strategy that can address diverse and evolving threat landscapes. Ensure all authors, regardless of vendor, use the required data to build a detection library that aligns with your priorities.

Phase 2: Test and Validate Your Detections

After your detection library is built, verify that the detections function correctly. Test and validate the detection logic to ensure it is effective, reliable, and capable of identifying the intended threats in real-world scenarios without generating too many false positives or negatives. There are four stages of testing detections, each with a specific objective and methodology for validating.

• Syntax validation: Ensures that the detection logic is syntactically correct and error-free.
• Data visibility verification: Confirms that the required event types and data sources are available and integrated.
• Threat and attack simulation: Tests the detection logic against real-world scenarios to validate that it identifies genuine threats.
• Operational validation: Validates the detection’s performance over time in a live environment.

Each stage builds on the previous one to make sure that the detections within the library are reliable, actionable, and effective in protecting the organization against real-world threats.

Phase 3: Deploy and Orchestrate Detections

After testing, the next step is deploying the detections across the organization. Typically, this is a manual process that gets in the way of rapid detection and containment. The better way to do this is through detection orchestration: build a detection once and then deploy it remotely across all the security technologies in your environment. This process centralizes and streamlines deployment, ensuring consistency and scalability while leaving your team free to focus on deeper analysis and response.

At-Storage vs. At-Source Detection

There are two ways to orchestrate your detections—at-storage and at-source.

With at-storage detection, the detection will execute from the detection library to your storage tool (e.g., a SIEM or data lake) and generate an alert if the query triggers a result.

The benefit of at-storage detection: Offers comprehensive capabilities for more complex detections and compliance but comes with higher costs and scalability challenges.

With at-source detection, the detection will execute directly at the source technology reporting the activity, bypassing the need for a storage tool.

The benefit of at-source detection: Provides lower latency and cost efficiency. More suitable for simpler and more straightforward detections.

We’ve found that the best strategy is to have a mix of at-storage and at-source detections based on your technologies. We discuss this approach in more detail in our guide to designing a detection strategy.

Phase 4: Measure and Improve

The only way to know if your detections are working is to measure their results. Here are three ways to track and improve their effectiveness:

Step 1: Align to a Framework to Assess Coverage

Compare your detection capabilities against a combination of established frameworks like MITRE ATT&CK or the Cyber Kill Chain for a multi-dimensional approach to measuring effectiveness. This will also help you identify any gaps in your security program.

Step 2: Develop Key Metrics

Once the detections are aligned to a framework that works best for your business, develop and monitor some KPIs. You may want to include metrics on:

• Coverage and visibility: Measure alignment with frameworks to understand detection gaps.
• Accuracy rate: Track false positives, true positives, and false negatives to assess detection precision.
• Attack simulation pass rate: Evaluate the effectiveness of detections through simulated attacks.
• Mean time to detect (MTTD): Measure the average time taken to detect threats to gauge responsiveness.

Step 3: Continuously Improve

Regularly review and refine your detection rules based on the metrics above. Consistently measuring detection effectiveness and establishing a repeatable process helps continually enhance your organization’s ability to identify and respond to threats effectively.

Conclusion

Detection engineering isn’t just about building detections; it’s about building a system that evolves, adapts, and outpaces attackers. Security teams that understand this thrive in a world of constant cyber risk. By treating the detection lifecycle as an ongoing, strategic process, you’ll not only detect threats more accurately but also maintain an environment that can withstand the evolving tactics of adversaries.

Detection engineering is an integral aspect of detection orchestration, the most effective detection-handling strategy for modern SOCs that want to keep up with evolving threats. This unified and efficient approach to threat detection centralizes the management and deployment of detection rules, enabling seamless implementation and updates across diverse environments. ReliaQuest’s security operations platform, GreyMatter, empowers enterprise security teams to leverage their current or future technology stack to drive greater visibility and automation without the need to centralize data or standardize tools.

• Detect threats no matter where the data lives, whether at-source or at-storage
• Contain threats faster with AI and automation
• Respond to threats within minutes
• Eliminate the need for Tier 1 and Tier 2 security operations activities