This article explores how isolated SOC performance metrics can mislead security operations center (SOC) performance assessment. The article features insights from a video interview with Ben Brigida and Ray Pugh, SOC operations leaders at Expel.
The complete interview can be found here: How to measure a SOC
Individual SOC performance metrics rarely tell complete stories. The most dangerous mistake in SOC operations management involves drawing conclusions from isolated data points without investigating the context, variables, and human factors that shape those numbers. This approach doesn’t just risk misunderstanding performance—it can lead to fundamentally wrong assessments of your best analysts.
Understanding how to properly interpret SOC performance metrics requires recognizing that every number exists within a broader operational context. Surface-level analysis might suggest poor performance where excellence actually exists, or mask problems that demand attention. The difference between effective and ineffective metrics programs often comes down to how thoroughly teams investigate what lies beneath the numbers.
When your best analyst appears to be your worst
Real-world examples expose the dangers of isolated SOC performance metrics interpretation most clearly. Consider an analyst widely recognized as one of the team’s strongest performers—someone leading shifts and consistently delivering high-quality work. When individual performance metrics were examined internally, this analyst’s mean time to respond appeared significantly longer than peers.
Surface-level analysis might conclude this person wasn’t as strong as believed. The data seemed to tell a clear story: longer response times indicate slower or less effective performance. However, deeper investigation revealed the complete picture and exposed why this initial interpretation was entirely wrong.
This analyst was identifying genuine incidents from low-severity alerts within ten minutes of starting their shift. Rather than working through the queue sequentially like most analysts, they were “plucking out the bad things really quickly”—demonstrating exceptional threat detection capability by recognizing malicious activity in alerts that others might dismiss or delay examining.
The longer mean time to respond reflected the total handling time for these early-identified incidents, not inefficiency in the response process. By catching threats early in the lifecycle, before they escalated or spread, this analyst was actually preventing much more time-consuming investigations and response efforts. The metric made them appear slow when they were actually demonstrating advanced analytical skills.
This example illustrates a fundamental principle about metrics: when any measurement becomes a target or evaluation criteria, it changes behavior in ways that can undermine the actual outcomes you’re trying to achieve. Publishing individual performance scorecards based on mean time to respond would incentivize analysts to avoid complex early-stage detections that might negatively impact their numbers—exactly the opposite of what effective SOC operations require.
The mentoring and training contributions that SOC performance metrics miss entirely
Another highly respected analyst demonstrated similarly counterintuitive metrics when examining total alert volume. This person’s alert count appeared surprisingly low compared to peers—again raising questions about performance until the full context emerged through investigation.
The explanation revealed actual value that statistics couldn’t capture. This analyst consistently arrived “first to the scene of the crime” for incidents, working the most complex security events by far. Their lower alert processing count reflected time investment in high-value incident response rather than routine alert triage.
Complex incidents demand intensive investigation, coordination with multiple teams, comprehensive documentation, and careful remediation planning. An analyst handling several major incidents during a shift might process fewer routine alerts than someone working only straightforward triage, but their contribution to organizational security is substantially greater.
Additional factors exist completely outside traditional SOC performance metrics. Mentoring and training activities represent enormous value for overall team capability but appear nowhere in alert processing statistics. An analyst spending significant time helping teammates develop their skills, reviewing complex investigations together, or providing guidance on challenging technical problems produces compounding benefits that spreadsheets cannot capture.
These contributions matter particularly in SOC environments where thought diversity, varied skill sets, and different experience levels mean individual contributions to collective success take many forms. The team accomplishes security goals together, but each person’s contribution looks different based on their role, experience, and the specific threats they encounter.
Understanding why metrics become problematic targets
The transformation of metrics into targets creates predictable distortions in behavior and performance. When analysts know their work is being evaluated based on specific numbers, they naturally optimize for those numbers—even when doing so conflicts with actual operational effectiveness.
When SOC performance metrics like mean time to respond become evaluation criteria, analysts might rush through complex investigations to close alerts faster, sacrificing thoroughness for speed. If alert volume becomes a target, analysts might avoid time-consuming incident investigations that would reduce their processed alert count. If false positive rates become the primary measure, analysts might become overly conservative in their threat declarations, missing genuine attacks to protect their accuracy statistics.
These behavioral distortions don’t reflect character flaws or lack of commitment. They represent entirely predictable human responses to incentive structures. People naturally focus effort on activities that get measured and rewarded, even when those activities don’t align perfectly with broader organizational objectives.
This is why mature SOC programs carefully distinguish between internal operational SOC performance metrics used for process improvement and individual performance metrics used for evaluation. Examining detailed individual performance data to understand operational patterns and identify training opportunities differs fundamentally from using those same metrics to rank analysts or determine compensation.
The essential practice of inspecting SOC performance metrics deeply before drawing conclusions
Effective SOC performance metrics analysis requires moving beyond surface-level observation to understand all variables influencing the numbers. When something appears anomalous, the appropriate response involves deeper investigation to understand root causes rather than immediate judgment based on incomplete information.
This investigative approach should apply universally, regardless of preconceptions about individual analysts. The examples discussed involved analysts believed to be high performers whose metrics appeared concerning—but the same thorough analysis should occur for any analyst whose numbers seem unusual, whether positively or negatively.
Sometimes anomalies do indicate problems requiring attention. An analyst with consistently slow response times might need additional training on investigation techniques, better tool access, or help managing workload. But confirming this requires understanding what’s actually causing the slow times rather than assuming the metric tells the complete story.
Other times anomalies reveal valuable contributions that wouldn’t be apparent from aggregate statistics alone. The only way to distinguish between these scenarios involves examining the specific work being done, understanding the context around individual cases, and cross-referencing multiple data sources to build comprehensive understanding.
Cross-referencing multiple data sources for reliable insights
No single metric provides sufficient visibility into analyst performance or operational effectiveness. Comprehensive assessment requires examining multiple dimensions simultaneously and looking for patterns, contradictions, or gaps that suggest areas requiring deeper investigation.
Alert handling statistics should be considered alongside incident contributions, peer feedback from collaborative investigations, mentoring activities documented in team communications, and subjective evaluation by experienced managers familiar with the work’s nuances. Each data source provides partial visibility; together they create more complete pictures.
Technical metrics reveal what happened but often fail to explain why. An analyst might have high false positive rates because they’re working low-fidelity alert types that inherently generate more noise, not because their analytical skills are weak. Understanding this requires context that pure statistics don’t provide.
Qualitative data from manager conversations and peer observations adds the human dimension that quantitative metrics miss. Team members often recognize excellence or struggle in colleagues before it becomes apparent in statistics. This qualitative intelligence should inform but not replace objective measurement—both perspectives together provide the most reliable foundation for understanding performance.
The cross-referencing process itself often reveals limitations in current measurement approaches. If metrics consistently conflict with direct observation, that suggests either measurement methodology needs refinement or that important factors aren’t being captured. This discovery process drives continuous improvement in both what you measure and how you interpret those measurements.
Building metrics programs that inform rather than distort
Successful SOC performance metrics programs design measurement approaches that minimize behavioral distortion while maximizing operational insight. This requires careful consideration of what gets measured, how results are communicated, and what consequences follow from metric results.
Internal operational analysis should focus on aggregate team patterns rather than individual performance rankings. Examining which alert types consume disproportionate time, which investigation steps repeatedly create bottlenecks, or which incident categories take longer than expected provides actionable insights for process improvement without creating incentives for counterproductive individual behavior.
When individual metrics are examined—and sometimes this is necessary to identify training needs or workload distribution issues—the analysis should remain internal to management and should always involve investigation of context before drawing conclusions. Publishing scorecards or creating competitive dynamics around individual metrics almost inevitably creates more problems than it solves.
The metrics that do get emphasized broadly should align with actual desired outcomes. If thorough investigation matters more than speed, emphasize quality metrics over pure timing measurements. If collaborative problem-solving represents core cultural values, track and celebrate knowledge sharing rather than just individual alert counts.
Transparency about measurement limitations helps teams interpret metrics appropriately. Explicitly acknowledging what metrics capture well and what they miss entirely sets appropriate expectations and reduces the risk of overconfidence in statistical analysis. This intellectual humility creates space for the qualitative judgment that remains essential regardless of measurement sophistication.
The role of organizational culture in metric interpretation
The cultural environment surrounding metrics powerfully influences whether measurement programs support or undermine operational effectiveness. Organizations that create psychologically safe environments where discussing metric anomalies feels collaborative rather than punitive enable much more productive use of performance data.
When analysts trust that metric discussions focus on learning and improvement rather than punishment or ranking, they engage more openly in investigating what the numbers reveal. This openness accelerates both individual development and organizational learning about what works and what doesn’t in SOC operations.
Conversely, cultures where metrics become weapons for criticism or comparison create defensive behaviors that prevent honest assessment. Analysts in such environments learn to protect themselves by gaming metrics, hiding struggles that could benefit from coaching, or avoiding challenging work that might negatively impact their statistics.
Building cultures that use metrics productively requires leadership commitment to principles over convenience. It’s easier to create simple ranking systems based on available metrics than to do the hard work of comprehensive performance assessment. But the easier path typically produces worse outcomes for both individual development and operational effectiveness.
Resources for SOC analytics and SOC performance metrics interpretation
Organizations developing sophisticated approaches to SOC metrics and analytics can benefit from additional resources and industry guidance:
- Performance metrics, part 1: Measuring SOC efficiency provides foundational knowledge for understanding capacity, utilization, and decision fatigue in SOC operations
- Performance metrics, part 3: Success stories demonstrates how metrics analysis uncovers improvement opportunities while avoiding common pitfalls
- How to measure SOC quality explores quality control methodologies including check sheets, inspection processes, and balancing quality with efficiency
- How to measure SOC efficiency offers practical guidance on measuring both speed and accuracy while avoiding metrics that drive counterproductive behavior
- Cybersecurity metrics examples for measuring automation impact addresses the importance of combining quantitative and qualitative measures for holistic performance assessment
- Achieve world-class security operations metrics examines how automation and AI enhance security program metrics while reducing analyst workload
- 7 habits of highly effective SOCs explores foundational practices that support both strong culture and effective performance measurement
The success of SOC performance metrics analytics ultimately depends on maintaining appropriate humility about what metrics can and cannot reveal. Numbers provide valuable visibility but never tell complete stories. Organizations that combine quantitative measurement with qualitative assessment, investigate anomalies thoroughly before drawing conclusions, and design measurement systems that minimize behavioral distortion develop the most reliable understanding of their operational effectiveness and team capabilities.