This detection engineering framework allows for strategic and effective decision making in a phased manner when dealing with planning, developing, delivering and improving new use cases for the SOC.
Each of these phases is separately addressed hereafter. Before we look at the phases, it is important to note that in this framework itself, two-way traceability is important. Thus, it must be possible to connect elements at the operational layer to elements at the tactical and ultimately strategical layers and vice versa. This allows the SOC to show how business drivers are implemented in operational monitoring (top-down) and which monitoring rules relate to which specific threats and business drivers (bottom-up).
🔄 Key Principle: The key principle that connects each phase is Two-way traceability throughout the framework lifecycle.
graph LR
subgraph "📈 Strategic Layer"
A[🎯 Business Drivers]
end
subgraph "⚔️ Tactical Layer"
B[🛡️ Security Controls]
end
subgraph "⚙️ Operational Layer"
C[🔍 Detection Rules]
end
A -.->|Top-Down| B
B -.->|Top-Down| C
C -.->|Bottom-Up| B
B -.->|Bottom-Up| A
style A fill:#0d419d
style B fill:#1158c7
style C fill:#1f6feb
Benefits:
- 📊 Top-Down: Show how business drivers are implemented in operational monitoring
- 📈 Bottom-Up: Connect monitoring rules to specific threats and business drivers
🏗️ Foundation: In this phase, the foundation for the use case and detection formation is laid down.
⚒️ Creation: Once the planning phase is complete, you move on to the development phase.
🚀 Implementation: The delivery phase involves implementing the detection rules and use cases into the SIEM system.
🔄 Continuous Enhancement: The improvement phase is an ongoing process that occurs after the initial delivery.
graph LR
A[🎯 Planning Phase] --> B[⚙️ Development Phase]
B --> C[🚀 Delivery Phase]
C --> D[🔧 Improvement Phase]
D --> A
style A fill:#0c2d6b,stroke:#fff,stroke-width:2px,color:#fff
style B fill:#9e6a03,stroke:#fff,stroke-width:2px,color:#fff
style C fill:#196c2e,stroke:#fff,stroke-width:2px,color:#fff
style D fill:#553098,stroke:#fff,stroke-width:2px,color:#fff
This comprehensive framework transforms business monitoring needs due to Risk, Threat, Compliance drivers into operational security detection and response through a structured 12-phase methodology that ensures robust detection engineering practices. Lets have a look at the steps involved in each phase.
flowchart TD
%% Planning Phase
A1[🎯 Risk] --> B[📋 Use Case Request]
A2[⚠️ Threat] --> B
A3[📊 Compliance] --> B
%% Development Phase
B --> C[🛠️ Prep for Development]
C --> D[🔍 Technical Analysis]
D --> E[⚔️ Attack Simulation]
E --> F[📝 Development Pre-req]
F --> G[💻 Code Development]
G --> H[🧪 Code Testing]
H --> I[📈 Response Development]
I --> J[🔬 Response Testing]
%% Deployment Phase
J --> K[🤝 SOC Handover]
K --> L[⚡ Rule Activation]
L --> M[📚 Cataloguing]
%% Improvement Phase
M --> N[Refinement]
N -.->|Use-case Improvement| B
N -.->|Detection Improvement| G
N -.->|Response Improvement| I
%% Styling
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style A2 fill:#ff6b6b,stroke:#d63031,stroke-width:3px,color:#fff
style A3 fill:#ff6b6b,stroke:#d63031,stroke-width:3px,color:#fff
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style C fill:#00b894,stroke:#00a085,stroke-width:2px,color:#fff
style D fill:#00b894,stroke:#00a085,stroke-width:2px,color:#fff
style E fill:#00b894,stroke:#00a085,stroke-width:2px,color:#fff
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style J fill:#74b9ff,stroke:#0984e3,stroke-width:2px,color:#fff
style K fill:#74b9ff,stroke:#0984e3,stroke-width:2px,color:#fff
style L fill:#74b9ff,stroke:#0984e3,stroke-width:2px,color:#fff
style M fill:#74b9ff,stroke:#0984e3,stroke-width:2px,color:#fff
style N fill:#484f58,stroke:#21262d,stroke-width:2px,color:#fff
The framework recognizes that effective detection engineering cannot exist in a vacuum. Every use case must be anchored to legitimate business needs that justify the investment of time, resources, and organizational focus. This step establishes the critical foundation by identifying and documenting the specific business drivers that necessitate new detection capabilities.
Risk drivers emerge from enterprise risk management activities where organizations identify potential threats to their business operations, assets, or reputation. These drivers often stem from business impact analyses that reveal vulnerabilities in critical processes or systems. When risk assessments highlight gaps in current security posture, they generate requirements for enhanced detection capabilities that can provide early warning of potential incidents.
Threat drivers originate from intelligence about active adversaries and attack campaigns targeting the organization or its industry. These drivers are informed by threat intelligence feeds that provide insights into current attack patterns, emerging tactics, techniques, and procedures used by threat actors. Organizations must continuously adapt their detection strategies to address evolving threat landscapes and industry-specific attack vectors.
Compliance drivers arise from regulatory obligations and industry standards that mandate specific security controls and monitoring capabilities. Whether driven by financial regulations like SOX, healthcare standards like HIPAA, or payment processing requirements like PCI-DSS, compliance drivers ensure that detection capabilities align with external requirements and audit expectations.
Once business drivers have been identified, organizations must translate these high-level requirements into specific, actionable use case requests. This step serves as the critical bridge between strategic business needs and tactical detection engineering work. The use case request process ensures that every detection engineering effort is properly scoped, justified, and aligned with organizational priorities.
The documentation process begins with capturing the business justification that links back to the original drivers. This documentation must clearly articulate why the detection capability is needed, what business problem it solves, and how success will be measured. The use case request also defines the specific detection objectives, outlining what types of threats or activities the new capability should identify and under what circumstances alerts should be generated.
Priority categorization becomes essential when organizations face multiple competing demands for detection engineering resources. The framework provides structured criteria for evaluating use case requests based on factors such as potential business impact, regulatory requirements, threat severity, and available resources. This prioritization ensures that the most critical detection capabilities are developed first while maintaining clear visibility into the pipeline of future work.
The preparation step establishes the necessary infrastructure, resources, and planning required for successful detection engineering work. This step recognizes that effective detection development requires more than just technical skills – it demands proper environment setup, resource allocation, and project planning to ensure smooth execution.
Environment setup involves preparing development and testing infrastructure that mirrors production systems while providing safe spaces for experimentation and validation. This includes configuring access to relevant data sources, setting up development tools, and establishing secure channels for collaboration between team members. The preparation step also involves assembling the right mix of skills and expertise, bringing together detection engineers, threat intelligence analysts, and subject matter experts who understand the specific technologies and attack vectors being addressed.
Technical analysis represents the most critical investigative step of the framework, where detection engineers develop deep understanding of the technical landscape that will support their detection logic. This step goes far beyond surface-level requirements gathering to examine the underlying data sources, infrastructure constraints, and technical feasibility of proposed detection approaches.
Data source analysis forms the cornerstone of this step, requiring engineers to thoroughly understand what log data is available, how it is formatted, where it is stored, and what information it contains. This analysis often reveals gaps between what organizations think they are logging and what data is actually available for detection purposes. Engineers must also assess the quality, completeness, and reliability of data sources to ensure that detection logic will function consistently in production environments.
Infrastructure evaluation examines the technical capabilities and constraints of existing security tools and platforms. This includes understanding processing capacity, storage limitations, query performance characteristics, and integration capabilities that will impact detection rule deployment and operation.
Attack simulation provides empirical validation of detection approaches before significant development investment occurs. This step moves beyond theoretical analysis to actual testing of attack scenarios in controlled environments, providing concrete evidence of what detection approaches will and will not work in practice.
The simulation process begins with developing realistic attack scenarios based on current threat intelligence and the specific use case requirements. These scenarios must accurately reflect the tactics, techniques, and procedures that real adversaries would use, ensuring that detection logic will be effective against actual threats rather than theoretical ones. Simulation activities are conducted in isolated environments that mirror production systems but prevent any risk to operational infrastructure.
Through controlled testing, engineers establish baseline behavior patterns that distinguish normal activity from malicious actions. This baseline establishment is crucial for developing detection logic that minimizes false positives while maintaining high sensitivity to actual threats. The simulation step often reveals important insights about attack detection that are not apparent from purely analytical approaches. The implementation and development steps represent the core technical work where detection concepts are transformed into operational capabilities. These four interconnected steps build upon each other to create robust, tested, and deployable detection solutions that meet the requirements established in earlier steps.
The development prerequisites step finalizes all technical requirements and design decisions before coding begins. This step serves as the final checkpoint to ensure that all necessary groundwork has been completed and that development can proceed efficiently without major roadblocks or design changes.
Technical requirements finalization involves converting the insights gained from analysis and simulation steps into specific, implementable specifications. These requirements must be detailed enough to guide development work while flexible enough to accommodate technical discoveries made during implementation. The architecture design process establishes the overall structure and approach for the detection solution, defining how different components will interact and how the solution will integrate with existing security infrastructure.
Data modeling becomes particularly important during this step, as engineers must define exactly how information will be structured, processed, and stored throughout the detection pipeline. This includes designing schemas for log parsing, defining data enrichment processes, and establishing the format for detection outputs that will be consumed by downstream systems.
Code development transforms design specifications into functional detection logic that can identify threats and generate appropriate alerts. This step requires deep technical expertise in detection engineering tools and platforms, as well as thorough understanding of the attack scenarios and data sources established in previous steps.
Detection logic creation involves writing the core algorithms and rules that will identify malicious activity within the monitored data streams. This logic must be precise enough to minimize false positives while comprehensive enough to catch variations in attack techniques. Engineers must consider factors such as timing windows, correlation logic across multiple data sources, and threshold settings that balance sensitivity with operational practicality.
Query development translates detection logic into the specific syntax and structure required by SIEM platforms and security tools. This often involves optimizing queries for performance while maintaining detection effectiveness, as poorly optimized detection rules can impact overall system performance and response times.
Comprehensive testing validates that developed detection capabilities function correctly across a wide range of scenarios and conditions. This step goes beyond simple functionality testing to examine performance characteristics, edge cases, and integration behavior that could impact production deployment.
Unit testing focuses on validating individual components of the detection logic, ensuring that each element functions correctly in isolation. This includes testing parsing logic, correlation algorithms, and output formatting to verify that each component produces expected results when presented with known inputs.
Integration testing examines how detection components work together and how they interact with existing security infrastructure. This testing reveals potential conflicts with other detection rules, resource consumption patterns, and integration issues that might not be apparent during unit testing.
Performance testing evaluates how detection rules behave under realistic load conditions, measuring factors such as processing time, memory consumption, and system impact. This testing is crucial for ensuring that new detection capabilities do not adversely affect overall security platform performance.
Response development creates the procedures, workflows, and automation that will handle alerts generated by the new detection capabilities. This step recognizes that effective detection is only valuable if it leads to appropriate and timely response actions.
Alert configuration establishes how detection events will be formatted, prioritized, and routed to appropriate response teams. This includes defining alert severity levels, establishing escalation criteria, and configuring notification mechanisms that ensure critical threats receive immediate attention while minimizing alert fatigue from lower-priority events.
Playbook development creates standardized procedures that guide security analysts through the investigation and response process when alerts are triggered. These playbooks must provide clear, step-by-step guidance while remaining flexible enough to accommodate variations in attack techniques and environmental factors. Effective playbooks also include decision trees that help analysts determine appropriate response actions based on investigation findings.
Response testing validates that the complete detection and response system functions effectively under realistic operational conditions. This step moves beyond the technical testing of detection logic to examine the human and procedural elements that determine whether detection capabilities translate into effective security outcomes.
Tabletop exercises engage security teams in simulated incident scenarios that test not only the technical detection capabilities but also the human response processes, communication protocols, and decision-making procedures that activate when alerts are generated. These exercises reveal gaps between theoretical response procedures and practical operational realities, highlighting areas where additional training, process refinement, or tool configuration may be needed.
Simulated incident response drills provide hands-on testing of the complete alert-to-resolution workflow, measuring how effectively security teams can investigate alerts, make appropriate decisions, and take corrective actions. These drills often reveal important insights about alert quality, investigation tools, and procedural effectiveness that cannot be discovered through purely technical testing approaches.
The final steps of the framework focus on transitioning developed detection capabilities from development environments into operational production use. These steps ensure that detection capabilities are not only technically sound but also operationally sustainable and properly integrated into existing security operations workflows.
The SOC handover step manages the critical transition of detection capabilities from development teams to operational security teams. This transition requires more than simply deploying technical capabilities – it demands comprehensive knowledge transfer, training, and support structure establishment to ensure operational success.
Documentation development creates comprehensive reference materials that enable SOC analysts to effectively operate and maintain the new detection capabilities. This documentation must balance completeness with usability, providing detailed technical information while remaining accessible to analysts with varying levels of expertise. Effective documentation includes not only technical specifications but also decision trees, troubleshooting guides, and escalation procedures that support day-to-day operations.
Training programs ensure that SOC teams have the knowledge and skills necessary to operate new detection capabilities effectively. These programs must address both technical aspects of the detection tools and procedural aspects of incident response, providing hands-on experience that builds confidence and competence in real-world scenarios.
Rule activation represents the transition from testing to live production operation, where detection capabilities begin monitoring actual organizational activity and generating alerts for real security events. This step requires careful orchestration to ensure smooth deployment while minimizing operational disruption.
Production deployment involves implementing detection rules in live environments while maintaining continuous monitoring of system performance and alert quality. Initial deployment often uses graduated approaches, such as shadow mode operation or limited scope deployment, that allow for real-world validation without full operational impact.
Baseline establishment during the initial operational period provides critical data about normal detection behavior, false positive rates, and system performance under actual load conditions. This baseline data becomes essential for ongoing tuning and optimization efforts that improve detection effectiveness over time.
The cataloguing step ensures that organizational knowledge and experience gained through the detection engineering process is captured, documented, and made available for future use. This step recognizes that effective detection engineering programs build upon accumulated knowledge and that individual detection projects contribute to broader organizational capabilities.
Comprehensive documentation goes beyond simple technical specifications to capture the reasoning, trade-offs, and lessons learned throughout the development process. This documentation includes not only what was built and how it works, but also why specific design decisions were made, what alternatives were considered, and what challenges were encountered during development and deployment.
Knowledge management processes ensure that insights gained from individual detection engineering projects are incorporated into organizational best practices and training materials. This includes updating development methodologies based on lessons learned, refining testing procedures based on operational experience, and sharing successful approaches across different detection engineering teams.
| Phase | 📊 Key Metrics | 🎯 Success Indicators |
|---|---|---|
| 🏗️ Planning | Scope Definition, Resource Allocation | Clear objectives, Stakeholder buy-in |
| ⚒️ Development | Rule Quality, Coverage | Comprehensive detection, Low false positives |
| 🚀 Delivery | Implementation Speed, Integration Success | Operational SIEM, Real-time monitoring |
| 🔄 Improvement | Detection Rate, Response Time | Reduced incidents, Faster resolution |
🔧 Framework Philosophy: "Every detection capability should be purposefully designed, rigorously tested, and operationally sustainable."
💡 Pro Tip: The Detection Engineering Framework emphasizes the importance of end-to-end traceability from business drivers to operational outcomes, ensuring that every detection investment delivers measurable business value.