As teams grow and the scope of detection expands, adapting to the increased scale becomes a critical challenge. This often requires teams to embrace new obstacles that test both their technical capabilities and their agility. Being flexible and responsive is essential when developing detection mechanisms in a rapidly evolving environment. For example, as detection workloads increase, teams may find that the processes and tools that once worked well no longer meet the demands of the larger scale. This can lead to tough questions about the fundamental principles behind their existing security program. Addressing these issues head-on is crucial to ensuring the team can build a scalable, adaptable detection framework that evolves alongside the threat landscape.

Detection Engineering Process

Detection Engineering typically involves several steps that guide security teams from threat detection to resolution.

1. Data Collection

• Data Sources: Collect logs, telemetry, and data from various sources such as endpoints, network devices, applications, and cloud environments.
• Tools: Security Information and Event Management (SIEM), Endpoint Detection and Response (EDR), Network Traffic Analysis (NTA), etc.

2. Data Ingestion

• Ingesting Data into Tools: Filter and normalize data into your security monitoring tools.
• Normalization: Transform the data into a standardized format for analysis.
• Enrichment: Add additional context to data, such as threat intelligence feeds.

3. Detection Logic

• Rule Creation: Write rules and detection logic for identifying known and unknown threats. This can involve using threat signatures or behavior-based detection models.
• Detection Methodologies: Signature-based, anomaly-based, and behavior-based detection.
• Testing and Tuning: test, validate, and tune detection rules to minimize false positives/negatives before production.

4. Alert Generation

• Alert Criteria: Generate alerts when certain thresholds or patterns are detected in the data.
• Prioritization: Rank alerts based on severity, impact, and criticality.

5. Threat Investigation

• Alert Triage: Security analysts investigate alerts to determine if they represent real threats.
• Correlation: Cross-reference alerts with other data sources to gain context and assess the situation. This helps reduce False positivity.
• Threat Hunting: Proactively search for threats with a hypothesis that may not have been detected automatically.

6. Response & Mitigation

• Incident Response: Execute predefined response plans for containing and mitigating the threat.
• Automation: Use automation to trigger certain actions like isolating systems or blocking IP addresses.
• Remediation: Apply fixes and ensure the threat is neutralized.

7. Post-Incident Review

• Review and Analysis: Conduct a post-incident review to understand the root cause, identify gaps in detection, and improve defenses.
• Update Detection Rules: Modify detection logic based on insights gained during the incident to prevent future occurrences.

8. Continuous Improvement

• Threat Intelligence Integration: Continuously incorporate new threat intelligence and adapt the detection framework.
• Feedback Loop: Ensure constant updates to detection mechanisms as new threats evolve.

Challenges

Now that we understand the process, we can talk about the challenges when scalability comes into play.

1. Data Collection

• Data Volume and Complexity: Handling massive amounts of data from diverse sources, often in unstructured or inconsistent formats.
• Integrating Data: Collecting and normalizing data from a wide range of systems (e.g., cloud, on-premise, IoT) and logs with different formats.
• Environmental Complexity: Dealing with the scale and diversity of modern environments, such as development systems, cloud, hybrid infrastructure, etc.
• Incomplete Data: Gaps in data collection due to system misconfigurations or lack of visibility into specific assets or environments.

2. Data Ingestion

• Log Storage Costs: Managing the cost of ingesting and storing large volumes of logs while balancing performance in a SIEM environment.
• Indexing Performance: Ensuring efficient indexing of data without sacrificing query performance.
• Resource Constraints: Dealing with daily bandwidth limitations, especially in cloud-based SIEM systems.
• Data Quality Issues: Ensuring data is clean and standardized for accurate ingestion and analysis.

3. Detection Logic

• Balancing Detection Accuracy: Avoiding overly broad detection rules that lead to false positives while preventing narrow rules that miss real threats.
• Evolving Threats: Continuously updating detection logic to accommodate new and sophisticated attack vectors.
• Environmental Complexity: Crafting rules for diverse environments (e.g., development vs. production) to handle benign anomalies or unique configurations.
• Detection-as-Code: Implementing detection-as-code at scale, which involves managing and deploying detection rules via code repositories.

4. Alert Generation

• False Positives/Negatives: Reducing the rate of false positives that overload SOC teams, while minimizing false negatives that miss real threats.
• Alert Fatigue: Preventing alert fatigue among SOC analysts by ensuring alerts are meaningful and actionable.
• Prioritization: Ensuring that alerts are accurately prioritized based on threat severity and potential impact on the organization.
• Resource Constraints: Ensuring that SIEM tools and resources are optimized to generate timely alerts without overwhelming the system or analysts.

5. Threat Investigation

• SOC Feedback Loops: Obtaining timely and accurate feedback from SOC analysts to improve and refine detection rules.
• Collaboration Across Teams: Getting necessary context and information from different teams (e.g., IT, DevOps, networking) for efficient investigation.
• Investigating Complex Incidents: Addressing complex or persistent threats that require deep investigation across multiple data sources and systems.
• Resource Allocation: Allocating skilled analysts to high-priority incidents amidst resource constraints.

6. Response & Mitigation

• Incident Automation: Balancing automation in response processes with the need for human oversight, especially for critical incidents.
• Time to Response: Reducing the time it takes to identify, investigate, and respond to a threat without compromising accuracy.
• Resource Management: Efficiently managing resources (e.g., staff, tooling) to ensure rapid and effective incident response.
• Response Adaptability: Adapting response actions to different environments (e.g., development systems vs. production) to avoid disruptions.

7. Post-Incident Review

• Measurement and Improvement: Quantifying detection effectiveness, response times, and the overall return on investment (ROI) for detection efforts.
• Collaboration with Other Teams: Ensuring cross-team collaboration during post-incident reviews for a holistic understanding of root causes and resolution paths.
• Continuity Planning: Ensuring that continuous improvement strategies align with both immediate needs and long-term security goals.

8. Continuous Improvement

• Keeping Pace with Evolving Threats: Adapting detection rules and methodologies to account for rapidly changing cyber threats and emerging attack vectors.
• Talent and Expertise: Finding and retaining skilled detection engineers who possess both security knowledge and software engineering expertise.
• Operational Efficiency: Improving operational efficiency by continuously optimizing detection pipelines, workflows, and tool configurations.
• Feedback Integration: Ensuring quality feedback from SOC analysts and threat hunters is incorporated into ongoing detection efforts.
• Detection-as-Code Scalability: Scaling detection-as-code processes efficiently across a growing number of systems and rulesets.

Conclusion

By mapping these challenges to each phase of the detection engineering flow, organizations can better understand the specific difficulties they face at each stage and work toward addressing them systematically.