Detection Engineering: Implementing Detection-as-Code for Modern Security Operations

Detection Engineering has emerged as a critical discipline in modern cybersecurity, bridging the gap between threat intelligence and operational security. As organizations face increasingly sophisticated attacks, the traditional approach of manually creating and maintaining detection rules in SIEM platforms is no longer sustainable. Enter Detection-as-Code (DaC) - a methodology that applies software engineering principles to threat detection development, revolutionizing how security teams build, test, and deploy detection capabilities.

What is Detection Engineering?

Detection Engineering is the practice of designing, developing, testing, and maintaining threat detection logic. It's a systematic approach to creating effective security monitoring capabilities that can identify malicious activities across an organization's infrastructure.

Core Responsibilities of Detection Engineers:

• Threat Analysis: Understanding attack techniques and developing detection strategies
• Data Source Identification: Determining which logs and telemetry are needed for detection
• Rule Development: Creating detection logic using platform-specific or agnostic languages
• Testing and Validation: Ensuring detections work correctly with minimal false positives
• Performance Optimization: Balancing detection accuracy with system performance
• Continuous Improvement: Monitoring and refining detections based on real-world usage

What Detection Engineering is NOT:

It's important to clarify that Detection Engineering doesn't include:

• Audit policy configuration and telemetry generation
• Telemetry collection and normalization
• Building tooling to apply detection logic to data

These activities, while related, fall outside the core scope of detection engineering and are typically handled by other security teams.

The Detection Development Life Cycle (DDLC)

Similar to software development's SDLC, Detection Engineering follows a structured approach called the Detection Development Life Cycle (DDLC). This framework consists of six key phases:

1. Requirement Gathering

Goal: Understand what threats need to be detected and define success criteria.

Key Activities:

• Identify specific threats or attack techniques to monitor
• Assess risk levels and urgency for prioritization
• Define clear success criteria for each detection
• Understand the business impact of missed detections

Questions to Answer:

• What specific threat are we trying to detect?
• What is the risk level and urgency?
• What constitutes a successful detection?
• What are the consequences of false positives vs. false negatives?

2. Design

Goal: Plan the optimal approach for detecting the identified threats.

Key Activities:

• Select appropriate data sources and events
• Identify required fields and data attributes
• Map detections to relevant threat taxonomies (MITRE ATT&CK, etc.)
• Consider edge cases and potential evasion techniques
• Address performance and scalability concerns

Questions to Answer:

• What's the best way to catch this threat?
• What logs or telemetry are needed?
• How can attackers evade this detection?
• What are the performance implications?

3. Development

Goal: Create the actual detection rule or logic.

Key Activities:

• Write detection queries using appropriate languages (KQL, EQL, SPL, Sigma)
• Implement the detection according to the design specifications
• Ensure proper documentation and metadata
• Follow coding standards and best practices

Best Practices:

• Use platform-agnostic formats when possible (Sigma rules)
• Include comprehensive metadata and documentation
• Follow consistent naming conventions
• Implement proper error handling and logging

4. Testing and Deployment

Goal: Validate and deploy detections to production environments.

Key Activities:

• Replay attack data to verify detection accuracy
• Simulate triggering behavior to test effectiveness
• Tune detections to minimize false positives and false negatives
• Deploy validated detections to production environments
• Monitor initial performance and adjust as needed

Testing Approaches:

• Unit Testing: Validate individual detection logic
• Integration Testing: Test detections with real data sources
• Attack Simulation: Use red team exercises to validate effectiveness
• Performance Testing: Ensure detections don't impact system performance

5. Monitoring

Goal: Continuously review and optimize detection performance.

Key Activities:

• Monitor false positive and false negative rates
• Adjust thresholds and filters based on real-world usage
• Decommission ineffective or unmaintainable detections
• Track detection performance metrics and trends

Key Metrics:

• Detection accuracy rates
• False positive/negative ratios
• Performance impact on systems
• Time to detection and response

6. Continuous Testing

Goal: Ensure detections remain effective against evolving threats.

Key Activities:

• Implement automated testing pipelines
• Conduct regular threat simulations
• Update detections for new attack variations
• Validate detection resilience to changes

What is Detection-as-Code (DaC)?

Detection-as-Code is the practice of managing and developing threat detections using software engineering principles. It treats detection rules as code, applying the same rigor, processes, and tools used in software development.

Key Principles of Detection-as-Code:

1. Version Control

• Store detection rules in Git repositories
• Track changes and maintain history
• Enable rollbacks when detections cause issues
• Facilitate collaboration among team members

2. Code Reviews & Pull Requests

• Require peer review for new detections and changes
• Encourage shared ownership and responsibility
• Improve code quality through collaborative review
• Maintain consistency across the detection library

3. Testing & Validation

• Implement unit tests for detection logic
• Validate syntax and schema compliance
• Test against real or simulated attack data
• Ensure optimal false positive/negative rates before deployment

4. CI/CD Pipelines

• Automate validation and deployment processes
• Run tests automatically on code changes
• Deploy detections automatically upon successful PR merge
• Implement continuous integration and delivery workflows

5. Standardized Format

• Use consistent formats (YAML, JSON) for detection rules
• Follow defined schemas and metadata standards
• Maintain consistent field naming and structure
• Enable easy searching and categorization

6. Reusable Components

• Create shared filters and functions
• Implement common mappings and taxonomies
• Reduce code duplication across detections
• Enable centralized updates to shared components

Benefits of Detection-as-Code

Collaboration

Detection-as-Code improves team collaboration by:

• Enabling peer review of detection logic
• Sharing knowledge and best practices
• Creating a collaborative development environment
• Building team ownership of detection capabilities

Consistency

Standardized approaches ensure:

• Common formats and field values across all detections
• Consistent use of threat taxonomies (MITRE ATT&CK)
• Uniform documentation and metadata
• Easier maintenance and searching of detection libraries

Quality

Software engineering practices improve quality through:

• Schema validation and syntax checking
• Automated testing and validation
• Peer review processes
• Best practice enforcement

Efficiency

Automation and standardization increase efficiency by:

• Reducing manual effort in detection development
• Automating testing and deployment processes
• Streamlining change management workflows
• Enabling faster detection development cycles

Scaling

Structured approaches enable scaling through:

• Managing large detection libraries efficiently
• Consistent delivery across multiple environments
• Automated deployment to numerous systems
• Standardized processes for growth

Improved Documentation

Enforced metadata and documentation provide:

• Comprehensive detection documentation
• Clear references and investigation notes
• Known false positive documentation
• Better knowledge sharing and training

Who Benefits from Detection-as-Code?

Managed Security Service Providers (MSSPs)

MSSPs gain significant advantages from Detection-as-Code:

Scalability: Manage detections across multiple clients efficiently Consistency: Standardize detection quality across all customers Efficiency: Reduce manual effort and operational costs Quality: Improve service quality through better detection management Automation: Deploy content at scale with minimal manual intervention

In-House Security Operations Centers (SOCs)

Internal SOCs also benefit significantly:

Maintainability: Create well-documented, maintainable detections Consistency: Ensure uniform detection quality across the organization Maturity: Improve overall security program maturity Efficiency: Reduce false positives and improve analyst productivity Agility: Respond faster to new threats and changing requirements

Implementation Considerations

Getting Started with Detection-as-Code

1. Assess Current State

   • Inventory existing detection rules
   • Identify gaps in current detection coverage
   • Evaluate team skills and capabilities
   • Assess tooling and infrastructure requirements

2. Define Standards

   • Establish detection rule formats and schemas
   • Create naming conventions and metadata standards
   • Define review and approval processes
   • Set up testing and validation requirements

3. Implement Tooling

   • Set up version control repositories
   • Configure CI/CD pipelines
   • Implement testing frameworks
   • Deploy monitoring and alerting

4. Train Teams

   • Educate staff on Detection-as-Code principles
   • Provide training on tools and processes
   • Establish best practices and guidelines
   • Create documentation and runbooks

Common Challenges and Solutions

Challenge: Resistance to change from traditional approaches Solution: Start with pilot projects, demonstrate clear benefits, provide training and support

Challenge: Lack of technical skills in detection development Solution: Invest in training, hire experienced personnel, partner with external experts

Challenge: Integration with existing SIEM platforms Solution: Use platform-agnostic formats (Sigma), implement proper converters, test thoroughly

Challenge: Maintaining detection quality at scale Solution: Implement automated testing, establish review processes, monitor performance metrics

Future Trends in Detection Engineering

AI and Machine Learning Integration

• Automated threat detection using ML models
• Intelligent false positive reduction
• Adaptive detection tuning based on environment
• Predictive threat modeling

Cloud-Native Detection

• Cloud-specific detection strategies
• Serverless detection functions
• Multi-cloud detection coordination
• Cloud-native security monitoring

Threat Intelligence Integration

• Automated threat intelligence ingestion
• Dynamic detection rule generation
• Threat actor-specific detection strategies
• Real-time threat intelligence correlation

DevSecOps Integration

• Detection development in CI/CD pipelines
• Security testing in development workflows
• Infrastructure-as-Code for detection deployment
• Automated security validation

Conclusion

Detection-as-Code represents a fundamental shift in how organizations approach threat detection. By applying software engineering principles to detection development, security teams can create more effective, maintainable, and scalable detection capabilities.

The benefits are clear: improved collaboration, enhanced consistency, better quality, increased efficiency, and the ability to scale detection operations effectively. Whether you're an MSSP managing multiple clients or an in-house SOC team, Detection-as-Code provides the framework needed to build world-class detection capabilities.

As threats continue to evolve and organizations face increasing security challenges, the adoption of Detection-as-Code will become essential for maintaining effective security operations. The organizations that embrace this approach today will be better positioned to defend against tomorrow's threats.

For organizations looking to improve their detection engineering capabilities, see our guide on Building an Effective Incident Response Program. For companies evaluating their security posture, take our Compliance Posture Survey. For organizations looking to enhance their threat detection capabilities, check out AI Security Best Practices for MLOps Engineers.