theheavenlyd3mon Building SOC Metrics and KPI Tracking
When to Use
Use this skill when:
- SOC leadership needs data-driven visibility into operational performance
- Continuous improvement programs require baseline measurements and trend tracking
- Executive reporting demands quantified security posture and ROI metrics
- Staffing decisions need objective workload and capacity data
- Compliance audits require documented SOC performance evidence
Do not use metrics as punitive measures against analysts — metrics should drive process improvement, not individual performance management.
Prerequisites
- SIEM with 90+ days of incident and alert disposition data
- Incident ticketing system (ServiceNow, Jira) with timestamp data for incident lifecycle
- Analyst shift schedules and staffing data
- ATT&CK Navigator for detection coverage tracking
- Dashboard platform (Splunk, Grafana, or Power BI)
Workflow
Step 1: Define Core SOC Metrics Framework
Establish the key metrics aligned to NIST CSF functions:
| Metric | Definition | Target | NIST CSF |
|---|---|---|---|
| MTTD | Time from threat occurrence to SOC detection | <15 min | Detect |
| MTTA | Time from alert to analyst acknowledgment | <5 min | Respond |
| MTTI | Time from acknowledgment to investigation start | <10 min | Respond |
| MTTC | Time from investigation to containment | <1 hour | Respond |
| MTTR | Time from detection to full resolution | <4 hours | Recover |
| FP Rate | Percentage of false positive alerts | <30% | Detect |
| TP Rate | Percentage of true positive alerts | >40% | Detect |
| Coverage | ATT&CK techniques with active detection | >60% | Detect |
| Dwell Time | Attacker time in network before detection | <24 hours | Detect |
| Escalation Rate | % of Tier 1 alerts escalated to Tier 2/3 | 15-25% | Respond |
Step 2: Implement MTTD/MTTR Measurement
Mean Time to Detect (MTTD):
index=notable earliest=-30d status_label="Resolved*"
| eval mttd_seconds = _time - orig_time
| where mttd_seconds > 0 AND mttd_seconds < 86400 --- Exclude data quality issues
| stats avg(mttd_seconds) AS avg_mttd,
median(mttd_seconds) AS med_mttd,
perc90(mttd_seconds) AS p90_mttd,
perc95(mttd_seconds) AS p95_mttd
by urgency
| eval avg_mttd_min = round(avg_mttd / 60, 1)
| eval med_mttd_min = round(med_mttd / 60, 1)
| eval p90_mttd_min = round(p90_mttd / 60, 1)
| table urgency, avg_mttd_min, med_mttd_min, p90_mttd_min
Mean Time to Respond (MTTR):
index=notable earliest=-30d status_label="Resolved*"
| eval mttr_seconds = status_end - _time
| where mttr_seconds > 0 AND mttr_seconds < 604800 --- <7 days
| stats avg(mttr_seconds) AS avg_mttr,
median(mttr_seconds) AS med_mttr,
perc90(mttr_seconds) AS p90_mttr
by urgency
| eval avg_mttr_hours = round(avg_mttr / 3600, 1)
| eval med_mttr_hours = round(med_mttr / 3600, 1)
| eval p90_mttr_hours = round(p90_mttr / 3600, 1)
| table urgency, avg_mttr_hours, med_mttr_hours, p90_mttr_hours
MTTD/MTTR Trend Over Time:
index=notable earliest=-90d status_label="Resolved*"
| eval mttd_min = (_time - orig_time) / 60
| eval mttr_hours = (status_end - _time) / 3600
| bin _time span=1w
| stats avg(mttd_min) AS avg_mttd_min, avg(mttr_hours) AS avg_mttr_hours,
count AS incidents by _time
| table _time, incidents, avg_mttd_min, avg_mttr_hours
Step 3: Measure Alert Quality and Analyst Productivity
Alert Disposition Analysis:
index=notable earliest=-30d
| stats count AS total,
sum(eval(if(status_label="Resolved - True Positive", 1, 0))) AS tp,
sum(eval(if(status_label="Resolved - False Positive", 1, 0))) AS fp,
sum(eval(if(status_label="Resolved - Benign", 1, 0))) AS benign,
sum(eval(if(status_label="New" OR status_label="In Progress", 1, 0))) AS pending
| eval tp_rate = round(tp / total * 100, 1)
| eval fp_rate = round(fp / total * 100, 1)
| eval signal_noise = round(tp / (fp + 0.01), 2)
| table total, tp, fp, benign, pending, tp_rate, fp_rate, signal_noise
Analyst Productivity Metrics:
index=notable earliest=-30d status_label="Resolved*"
| stats count AS alerts_resolved,
avg(eval((status_end - status_transition_time) / 60)) AS avg_triage_min,
dc(rule_name) AS unique_rule_types
by owner
| eval alerts_per_day = round(alerts_resolved / 30, 1)
| sort - alerts_resolved
| table owner, alerts_resolved, alerts_per_day, avg_triage_min, unique_rule_types
Shift-Based Workload Distribution:
index=notable earliest=-30d
| eval hour = strftime(_time, "%H")
| eval shift = case(
hour >= 6 AND hour < 14, "Day (06-14)",
hour >= 14 AND hour < 22, "Swing (14-22)",
1=1, "Night (22-06)"
)
| stats count AS alerts, dc(owner) AS analysts by shift
| eval alerts_per_analyst = round(alerts / analysts / 30, 1)
| table shift, alerts, analysts, alerts_per_analyst
Step 4: Track Detection Coverage
ATT&CK Coverage Score:
| inputlookup detection_rules_attack_mapping.csv
| stats dc(technique_id) AS covered_techniques by tactic
| join tactic type=left [
| inputlookup attack_techniques_total.csv
| stats dc(technique_id) AS total_techniques by tactic
]
| eval coverage_pct = round(covered_techniques / total_techniques * 100, 1)
| sort tactic
| table tactic, covered_techniques, total_techniques, coverage_pct
Data Source Coverage:
| inputlookup expected_data_sources.csv
| join data_source type=left [
| tstats count where index=* by sourcetype
| rename sourcetype AS data_source
| eval status = "Active"
]
| eval source_status = if(isnotnull(status), "Collecting", "MISSING")
| stats count by source_status
| table source_status, count
Step 5: Build Executive Reporting Dashboard
Monthly SOC Executive Summary:
--- Incident summary by category
index=notable earliest=-30d status_label="Resolved*"
| stats count by urgency
| eval order = case(urgency="critical", 1, urgency="high", 2, urgency="medium", 3,
urgency="low", 4, urgency="informational", 5)
| sort order
--- Month-over-month comparison
index=notable earliest=-60d
| eval period = if(_time > relative_time(now(), "-30d"), "This Month", "Last Month")
| stats count by period, urgency
| chart sum(count) AS incidents by urgency, period
--- Top 5 incident categories
index=notable earliest=-30d status_label="Resolved - True Positive"
| top rule_name limit=5
| table rule_name, count, percent
Security Posture Scorecard:
| makeresults
| eval metrics = mvappend(
"MTTD: 8.3 min (Target: <15 min) | STATUS: GREEN",
"MTTR: 3.2 hours (Target: <4 hours) | STATUS: GREEN",
"FP Rate: 27% (Target: <30%) | STATUS: GREEN",
"Detection Coverage: 64% (Target: >60%) | STATUS: GREEN",
"Analyst Utilization: 78% (Target: 60-80%) | STATUS: GREEN",
"Incident Backlog: 12 (Target: <20) | STATUS: GREEN"
)
| mvexpand metrics
| table metrics
Step 6: Implement Continuous Improvement Tracking
Track improvement initiatives and their impact:
--- Improvement initiative tracking
| inputlookup soc_improvement_initiatives.csv
| eval status_color = case(
status="Completed", "green",
status="In Progress", "yellow",
status="Planned", "gray"
)
| table initiative, start_date, target_date, status, metric_impact, baseline, current
Example initiatives:
initiative,start_date,target_date,status,metric_impact,baseline,current
Risk-Based Alerting,2024-01-15,2024-03-15,Completed,Alert Volume,-84%,287/day
Sigma Rule Library,2024-02-01,2024-04-01,In Progress,ATT&CK Coverage,61%,64%
SOAR Phishing Playbook,2024-02-15,2024-03-30,In Progress,Phishing MTTR,45min,18min
Analyst Training Program,2024-01-01,2024-06-30,In Progress,TP Rate,31%,41%
Key Concepts
| Term | Definition |
|---|---|
| MTTD | Mean Time to Detect — average time from threat occurrence to SOC alert generation |
| MTTR | Mean Time to Respond — average time from detection to incident resolution |
| MTTA | Mean Time to Acknowledge — average time from alert generation to analyst assignment |
| Signal-to-Noise Ratio | Ratio of true positive alerts to total alerts — higher is better |
| Dwell Time | Duration an attacker remains undetected in the environment — key indicator of detection effectiveness |
| Analyst Utilization | Percentage of analyst time spent on productive investigation vs. overhead tasks |
Tools & Systems
- Splunk Dashboard Studio: Advanced visualization framework for building interactive SOC metric dashboards
- Grafana: Open-source analytics and visualization platform supporting multiple data sources
- Power BI: Microsoft business intelligence tool for executive-level reporting and trend analysis
- ATT&CK Navigator: MITRE tool for visualizing detection coverage as layered heatmaps
- ServiceNow Performance Analytics: ITSM analytics module for tracking incident lifecycle metrics
Common Scenarios
- Quarterly Business Review: Present MTTD/MTTR trends, detection coverage growth, and alert quality improvements
- Staffing Justification: Use workload metrics to justify additional analyst headcount or shift adjustments
- Tool ROI Assessment: Compare alert quality and response times before and after new tool deployment
- Compliance Evidence: Provide documented SOC performance metrics for ISO 27001 or SOC 2 audits
- Vendor Comparison: Benchmark SOC metrics against industry peers using surveys (SANS, Ponemon)
Output Format
SOC PERFORMANCE REPORT — March 2024
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
KEY METRICS:
Metric Current Target Trend Status
MTTD 8.3 min <15 min -12% GREEN
MTTR 3.2 hrs <4 hrs -18% GREEN
FP Rate 27% <30% -5% GREEN
TP Rate 41% >40% +3% GREEN
ATT&CK Coverage 64% >60% +3% GREEN
Alerts/Analyst/Day 24 <50 -84% GREEN
INCIDENT SUMMARY:
Total Incidents: 147 (Critical: 3, High: 23, Medium: 78, Low: 43)
Avg Resolution: 3.2 hours (Critical: 1.8h, High: 2.9h, Medium: 4.1h)
SLA Compliance: 94% (Target: >90%)
IMPROVEMENT HIGHLIGHTS:
[1] RBA deployment reduced daily alerts from 1,847 to 287 (-84%)
[2] New Sigma rules added 12 ATT&CK techniques to coverage
[3] SOAR phishing playbook reduced phishing MTTR by 60%
AREAS FOR IMPROVEMENT:
[1] Lateral movement detection coverage at 58% (below 60% target)
[2] Night shift MTTD 23% slower than day shift
[3] 4 critical vulnerability scan tickets overdue on SLA