date,alerts_received,alerts_triaged,true_positives,false_positives,incidents_opened,mttd_minutes,mttr_minutes 2026-02-14,187,182,23,159,2,142,385 2026-02-15,203,195,19,176,1,156,410 2026-02-16,145,140,12,128,1,138,365 2026-02-17,132,127,10,117,0,0,0 2026-02-18,198,190,21,169,2,149,392 2026-02-19,211,200,25,175,3,161,425 2026-02-20,224,210,28,182,2,155,415 2026-02-21,195,188,22,166,1,133,370 2026-02-22,167,160,15,145,1,145,355 2026-02-23,142,138,11,127,0,0,0 2026-02-24,138,135,9,126,0,0,0 2026-02-25,205,198,24,174,2,158,402 2026-02-26,218,205,27,178,3,162,430 2026-02-27,231,215,31,184,2,148,388 2026-02-28,209,200,26,174,2,152,395 2026-03-01,192,185,20,165,1,139,375 2026-03-02,158,152,14,138,1,141,360 2026-03-03,148,143,12,131,0,0,0 2026-03-04,212,198,26,172,2,165,445 2026-03-05,228,210,30,180,3,170,460 2026-03-06,245,220,35,185,4,178,485 2026-03-07,239,218,33,185,3,168,470 2026-03-08,215,200,27,173,2,160,440 2026-03-09,178,170,18,152,1,150,400 2026-03-10,165,158,15,143,1,155,410 2026-03-11,250,225,38,187,4,182,498 2026-03-12,262,230,42,188,5,190,520 2026-03-13,258,228,40,188,4,185,510 2026-03-14,270,235,45,190,5,195,535 2026-03-15,310,240,65,175,7,88,312 # ============================================================================ # SOC METRICS DATA — GLOBEX CORPORATION # Period: 2026-02-14 through 2026-03-15 (30 days) # Source: Splunk SOAR automated metric collection # ============================================================================ # # COLUMN DEFINITIONS: # date - Calendar date (YYYY-MM-DD) # alerts_received - Total alerts ingested by SIEM from all sources # alerts_triaged - Alerts reviewed by an analyst (Tier 1+) # true_positives - Alerts confirmed as genuine security events # false_positives - Alerts confirmed as benign / not actionable # incidents_opened - Alerts escalated to formal incident tickets # mttd_minutes - Mean Time to Detect (avg minutes from compromise # to first alert). Value is 0 on days with no incidents. # mttr_minutes - Mean Time to Respond (avg minutes from detection # to containment). Value is 0 on days with no incidents. # # NOTES: # - Weekends (Sat/Sun) show reduced alert volume due to lower user activity # - The SOC operates 24/7 with reduced weekend staffing (2 analysts vs 5) # - A new Suricata rule set was deployed on 2026-03-04 # - The March 15 spike corresponds to the capstone incident # - alerts_triaged < alerts_received indicates triage backlog # # ============================================================================ # # STUDENT TASKS: # # 1. TREND ANALYSIS # - Calculate the average daily alert volume for weeks 1-2 vs weeks 3-4 # - Is the alert volume increasing? What might explain the trend? # - Calculate the triage backlog (alerts_received - alerts_triaged) trend # - On which days was the backlog worst? What are the staffing implications? # # 2. FALSE POSITIVE ANALYSIS # - Calculate the daily false positive rate (false_positives / alerts_triaged) # - What is the 30-day average FP rate? # - Is the FP rate improving or worsening after the March 4 rule deployment? # - Estimate how many analyst-hours per day are spent on false positives # (assume 8 minutes average triage time per alert) # # 3. MTTD / MTTR ANALYSIS # - Calculate average MTTD and MTTR for the full 30-day period # (exclude zero-incident days) # - Is MTTD trending up or down? What does this mean for the SOC? # - Is MTTR trending up or down? What contributing factors might explain this? # - On March 15, MTTD dropped to 88 min — why? (Hint: check the alert feed) # - On March 15, MTTR dropped to 312 min — is this actually good? # # 4. IMPROVEMENT RECOMMENDATIONS # - Identify the top 3 metrics that need improvement # - For each metric, propose a specific, actionable improvement plan # - Estimate the expected metric improvement if your plan is implemented # - Create a 30/60/90 day roadmap for SOC improvement # - What additional metrics should the SOC start tracking? # # 5. EXECUTIVE SUMMARY # - Write a 1-page executive summary of SOC performance for the past month # - Include 3 key findings, 3 risks, and 3 recommendations # - Present the data visually (describe charts/graphs you would create) # # ============================================================================