Table of Contents
This article explores SOC quality measurement and how teams balance speed with accuracy, featuring insights from Ben Brigida and Ray Pugh, SOC operations leaders at Expel.
The complete interview can be found here: How to measure a SOC
SOC quality refers to the accuracy, thoroughness, and consistency of security analysis and decision-making within a security operations center (SOC). While speed matters in cybersecurity—every second counts when responding to threats—SOC quality management ensures quality cannot be sacrificed for efficiency. High-quality SOC operations ensure that analysts make correct decisions about which alerts represent genuine threats, provide complete and accurate incident reports, and maintain standards that protect organizations from both false negatives (missed threats) and false positives (wasted investigation effort).
How can teams ensure they’re not sacrificing quality for efficiency?
The tension between speed and quality represents one of the fundamental challenges in security operations. Organizations often feel pressure to optimize for faster response times, but pushing analysts to work faster without system-level improvements inevitably degrades quality. The solution lies in measuring quality systematically and using those measurements to drive improvements in people, processes, and technology.
Effective SOC quality management starts with being opinionated about what good looks like. You cannot hold someone accountable to a standard you never defined. Organizations must articulate their expectations clearly, communicate them through training, provide analysts with the space and tools to do quality work, and then inspect results against established standards.
Defining quality standards with rubrics
SOC quality management requires moving from subjective assessment to objective evaluation. The most effective approach involves creating detailed rubrics that define what constitutes good analysis, thorough investigation, and complete reporting.
A quality rubric should address specific elements: Does the analysis consider all relevant data sources? Does the investigation follow proper methodology? Does the report clearly explain what happened, when it occurred, and what actions are required? Are close reasons well-documented and justified? These criteria provide the foundation for consistent quality assessment.
Modern AI tools excel at rubric-based evaluation, making it possible to perform 100% sampling rather than traditional random sampling for quality control checks. This comprehensive approach catches quality issues faster and provides more complete visibility into operational performance.
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The critical importance of feedback loops
SOC quality management and inspection without action wastes effort and misses opportunities for improvement. The real value comes from creating effective feedback loops that translate quality findings into meaningful change.
When quality issues emerge, feedback must reach the analysts performing the work. This creates learning opportunities and helps prevent repeated mistakes. However, feedback shouldn’t focus solely on individual performance—often, quality problems stem from system issues like inadequate tools, unclear processes, or missing data that multiple analysts encounter.
Creating a culture where open, honest conversations about quality can occur requires psychological safety. Analysts need to feel comfortable owning mistakes, learning from them, and helping others improve. This cultural element often determines whether quality programs drive real improvement or simply create paperwork that everyone ignores.
Quality inspection should drive action at multiple levels: individual coaching for analysts, process improvements for common issues, product requirements for technology teams, and detection tuning for alert quality problems. Effective SOC operations use quality metrics to inform decisions across all these dimensions.
Measuring accuracy: True positives and false negatives
Beyond general quality assessment, SOC quality management must track specific accuracy metrics that reveal how well analysts distinguish genuine threats from benign activity.
True positive rate measures how often analysts correctly identify malicious activity when it exists. High true positive rates indicate that analysts effectively recognize threats and escalate appropriately.
False negative rate tracks closed alerts that should have triggered incidents. This represents the most dangerous quality failure—missed threats that allow attackers to achieve their objectives undetected. Organizations should track not just complete misses, but also situations where determination changed later in the lifecycle when additional context revealed that closed alerts actually related to confirmed incidents.
These metrics matter because they measure real security outcomes. A SOC with a 95% true positive rate sounds impressive until you realize that the 5% of missed threats might include the critical incidents that cause the most damage.
The concept of detection depth and “at bats”
SOC quality management extends beyond individual analyst decisions to encompass the overall detection strategy. A critical insight: analysts are not going to be 100% accurate, especially when facing sophisticated attackers. Setting perfection as a goal sets everyone up to fail.
The solution involves creating detection depth—giving analysts multiple opportunities (multiple “at bats”) to catch attacks. If a single alert represents the only chance to detect a particular attack, one analyst mistake means a complete miss. This approach places unreasonable pressure on individual decisions and creates unacceptable risk.
Robust detection programs ensure that attacks trigger multiple alerts through different detection methods. When an incident involves five different detection opportunities, the probability that analysts catch at least one increases dramatically. Organizations should track how many unique detection decisions contributed to each incident and identify “near misses” where incidents were caught with only a single alert—these indicate dangerous gaps requiring additional detection coverage.
Detection gap analysis
SOC quality management programs must include systematic detection gap analysis. Every incident provides answers to the test—attackers show exactly what techniques they used, what infrastructure they leveraged, and how they moved through the environment.
Effective gap analysis asks several questions: Where did we detect the attack? Where could we have detected it but didn’t? What detections should we add based on this incident? Which detection opportunities did we have, and which did we miss?
This analysis identifies improvements to detection logic, data collection requirements, and areas where additional coverage would increase the “at bats” available to catch similar attacks in the future. Organizations should treat every incident as a learning opportunity that strengthens their detection posture.
Balancing quality control with operational tempo
Quality inspection takes time and resources. Organizations must balance thorough quality review with the need to maintain operational tempo and avoid overwhelming teams with inspection overhead.
Statistical quality control methods borrowed from manufacturing provide solutions. Acceptable Quality Limits (AQL) sampling helps determine how many alerts, investigations, and incidents to review daily. When quality levels are good, lighter sampling suffices. When issues emerge, expanding sampling aperture identifies the extent and root causes of problems.
The goal is continuous quality improvement, not perfection. Trending defect rates over time reveals whether quality is improving, stable, or degrading. Organizations should aim for quality metrics that steadily improve as analysts gain experience, processes mature, and automation handles routine decisions.
| Quality metric | What it measures | Target direction |
|---|---|---|
|
Pass/fail rate |
Percentage of work meeting quality standards | Upward trend over time |
|
False negative rate |
Missed threats that should have been caught | Minimize, track near zero |
|
True positive rate |
Correct identification of genuine threats |
Maximize, approaching 100% |
|
Determination changes |
Alerts requiring reclassification after initial closure | Downward trend |
|
Detection gaps per incident |
Missed detection opportunities | Identify and remediate |
|
Near misses |
Incidents caught by single alerts | Investigate and add coverage |
Need comprehensive quality metrics tracking?
Download Expel’s free SOC Metrics Dashboard to monitor quality alongside efficiency metrics.
Quality drives automation priorities
SOC quality management metrics reveal opportunities for automation that simultaneously improve both speed and accuracy. When quality inspection identifies patterns—like inconsistent investigation approaches for specific alert types or commonly missed analysis steps—automation can standardize best practices.
For example, if quality review reveals that analysts investigate AWS alerts inconsistently, with some missing critical context that others routinely gather, automated orchestration can ensure every analyst has the same comprehensive data every time. This improves quality by eliminating gaps while increasing speed by removing manual lookup steps.
Expel’s approach demonstrates this principle: quality metrics identified business email compromise investigations as both common and prone to inconsistency. Automating data gathering and report generation improved both the quality and speed of BEC incident handling by 34%.
SOC quality FAQ
How do you measure something qualitative like analysis quality?
SOC quality management creates specific rubrics defining what good analysis looks like, then evaluates work against those criteria. Modern AI tools can apply rubrics consistently to enable 100% sampling rather than random sampling.
What’s more important: speed or quality?
Both are essential, but quality should never be sacrificed for speed. The solution is improving systems—better tools, automation, detection tuning—rather than pressuring analysts to work faster.
How often should we perform quality inspections?
Daily quality checks using statistical sampling methods provide ongoing visibility. Expand sampling when issues emerge, reduce it when quality is consistently high.
What do we do when quality metrics reveal problems?
Investigate root causes: Is it an individual training need? A process gap? Missing tools or data? Quality problems often indicate system issues affecting multiple analysts rather than individual performance issues.
Should we automate quality control?
AI-powered quality assessment enables comprehensive inspection that manual review cannot match. However, human review of quality findings and determination of appropriate corrective actions remains essential.
Getting started with SOC quality management programs
Organizations beginning quality initiatives should start with fundamentals:
Define clear quality standards through rubrics specifying what good analysis, investigation, and reporting look like. Document these standards and ensure all analysts understand expectations.
Implement systematic sampling using statistical methods to determine inspection frequency. Begin with manageable sample sizes and scale as processes mature.
Create feedback mechanisms ensuring quality findings reach analysts and inform improvements. Focus feedback on learning and system improvement rather than individual blame.
Track quality metrics over time, looking for trends that indicate whether quality is improving. Celebrate improvements and investigate degradation promptly.
Finally, consider whether partnership with managed detection and response providers makes sense. Building quality SOC operations requires significant investment in people, processes, and technology. Managed services provide access to proven quality programs without requiring organizations to develop these capabilities internally.
How Expel ensures quality at scale
At Expel, we believe you don’t have to trade quality for efficiency. Our approach combines clear standards, comprehensive inspection, effective feedback loops, and continuous improvement driven by quality metrics.
We’ve defined detailed rubrics specifying what constitutes quality analysis and reporting. Our security operations platform enables AI-assisted quality assessment that samples 100% of analyst work rather than random samples. This comprehensive approach identifies issues faster and provides complete visibility into quality trends.
When quality metrics reveal opportunities, we act decisively. Quality findings drive automation initiatives, detection tuning, process improvements, and analyst training. We track both accuracy metrics (true positive rates, false negative rates) and quality metrics (analysis thoroughness, report completeness) to ensure we maintain high standards across all dimensions.
Our quality program has driven concrete improvements: automated AWS alert investigation, streamlined business email compromise reporting, enhanced decision support tools, and continuous detection enhancements. By systematically measuring quality and using those measurements to inform improvements, we achieve both industry-leading response times and high-quality security operations.
Ready to experience quality-driven security operations?
Learn about Expel’s comprehensive managed detection and response services that deliver both speed and quality through proven processes and AI-powered automation.
Additional resources for SOC quality management
Organizations developing comprehensive SOC quality programs can benefit from additional resources and industry guidance:
- How to measure SOC quality provides detailed methodologies for implementing quality control programs including sampling techniques, check sheets, and inspection processes
- Performance metrics, part 3: Success stories demonstrates how quality metrics drive automation priorities and operational improvements
- From data to deployment: A deep dive into building our AI Resolutions (part two) explores how AI tools enable comprehensive quality assessment through rubric-based evaluation
- 7 habits of highly effective SOCs examines cultural practices that support quality-focused operations and continuous improvement
- How Expel’s Alert Similarity feature helps our customers discusses using pattern recognition to improve decision consistency and quality control
- Achieve world-class security operations metrics addresses how automation and AI enhance both speed and quality in security operations
- SOC metrics dashboard tool provides a free downloadable resource to track quality metrics alongside efficiency indicators
The success of SOC quality programs ultimately depends on defining clear standards, implementing systematic inspection processes, creating effective feedback loops, and using quality metrics to drive continuous improvement. Organizations that measure both speed and accuracy—and use those measurements to inform investments in automation, detection tuning, and analyst development—achieve the sustainable, high-quality security operations that protect effectively without sacrificing thoroughness or analyst wellbeing.