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Assurance Investigation

DEEP NETWORK MODEL ALERT INVESTIGATION

Investigation Summary

Incident: Deep Network Model detected anomalous CPU utilization pattern on Mumbai Floor 3 access switch, predicting device failure within 72 hours.

Detection: Catalyst Center AI anomaly alert triggered when CPU deviation exceeded 4.2 standard deviations from baseline.

Impact: Proactive intervention prevented unplanned outage affecting 48 users and 2 critical applications (ERP, CRM).

Outcome: Faulty linecard identified and replaced during maintenance window; DNM prediction accuracy validated.

Step 1: Incident Detection and AI Alert Analysis

Detection Timestamp: 2026-01-21 08:15:42 UTC

Alert Source: Catalyst Center AI Network Analytics (Deep Network Model)

AI Anomaly Alert: Device Failure Prediction
Device: Mumbai-FL3-C9300-IDF1 (10.252.10.45)
Severity: High
Prediction: Device CPU failure predicted in 72 hours
Confidence: 89%
Anomaly Type: CPU Utilization Pattern Deviation
Baseline: 12% ± 3% CPU utilization
Observed: 58% CPU utilization (4.2σ deviation)
Root Cause Hypothesis: Hardware degradation (linecard)
Recommended Action: Schedule preventive maintenance

Initial Investigation:

CASE_ID="CASE-2026-004-DNM"
INVESTIGATION_TYPE="dnm_anomaly_device_failure"

## ServiceNow incident
curl -X POST https://abhavtech.service-now.com/api/now/table/incident \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Basic <base64-creds>' \
  -d '{
    "short_description": "DNM Alert - Mumbai FL3 Switch CPU Anomaly",
    "description": "Deep Network Model predicts device failure in 72h due to CPU anomaly",
    "urgency": "2",
    "impact": "2",
    "category": "Proactive",
    "assignment_group": "Network-Engineering"
  }'

## Returns: INC0012347

Splunk Correlation:

index=dnac sourcetype=dnac:assurance earliest=-7d
| search device_hostname="Mumbai-FL3-C9300-IDF1"
| search severity IN (high, critical)
| table _time severity issue_name issue_description
| sort _time

Result:
_time                  severity  issue_name           issue_description
2026-01-14 12:00:00   medium    High CPU             CPU spike to 28% (brief)
2026-01-16 09:15:00   high      High CPU             CPU at 35% sustained
2026-01-18 14:30:00   high      High CPU             CPU at 42% sustained
2026-01-20 18:45:00   high      High CPU             CPU at 51% sustained
2026-01-21 08:15:42   high      AI Anomaly Detected  CPU pattern deviation (DNM)

## Clear upward trend over 7 days

Step 2: Collect Deep Network Model Analytics

2.1 Export DNM Anomaly Details:

## Authenticate to Catalyst Center
curl -k -X POST \
  https://dnac.abhavtech.com/dna/system/api/v1/auth/token \
  -u 'forensics-api:<password>' | jq -r '.Token' > /tmp/dnac-token.txt

DNAC_TOKEN=$(cat /tmp/dnac-token.txt)

## Query AI Network Analytics API
curl -k -X GET \
  "https://dnac.abhavtech.com/dna/data/api/v1/aiNetworkAnalytics/anomaly?deviceId=10.252.10.45&startTime=$(date -u -d '7 days ago' +%s)000&endTime=$(date -u +%s)000" \
  -H "X-Auth-Token: $DNAC_TOKEN" \
  -H "Content-Type: application/json" \
  > /mnt/evidence_vault/EVD-20260121-001-dnm-anomaly.json

## Register on blockchain
sha256sum /mnt/evidence_vault/EVD-20260121-001-dnm-anomaly.json
## 9e0f1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d7e8f9a0b1c2d3e4f5a6b7c8d9e0f

peer chaincode invoke \
  -n evidence-contract \
  -C evidence-channel \
  -c '{
    "Args":[
      "CollectEvidence",
      "EVD-20260121-001",
      "CASE-2026-004-DNM",
      "dnm_anomaly",
      "EVD-20260121-001-dnm-anomaly.json",
      "28473",
      "9e0f1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d7e8f9a0b1c2d3e4f5a6b7c8d9e0f",
      "forensics-ws01.abhavtech.com",
      "Network-Engineer-Suresh-Patel",
      "Catalyst-Center-DNM",
      "365",
      "[\"Network-Team\",\"SOC-Team\"]"
    ]
  }'

2.2 Parse DNM Output:

## Extract anomaly details
jq '.response.anomalies[]' \
  /mnt/evidence_vault/EVD-20260121-001-dnm-anomaly.json

## Output (formatted):
{
  "anomaly_id": "ANM-2026-004-CPU-001",
  "timestamp": "2026-01-21T08:15:42Z",
  "device_id": "10.252.10.45",
  "device_hostname": "Mumbai-FL3-C9300-IDF1",
  "anomaly_type": "cpu_utilization_pattern",
  "metric": {
    "name": "cpu_utilization",
    "current_value": 58.3,
    "unit": "percent",
    "baseline_mean": 12.1,
    "baseline_stddev": 2.8,
    "deviation": 4.2,          # Standard deviations from mean
    "threshold": 3.0           # Alert threshold
  },
  "prediction": {
    "predicted_event": "device_failure",
    "predicted_component": "linecard_slot_2",
    "time_to_failure_hours": 72,
    "confidence": 0.89,
    "model_name": "XGBoost-Device-Failure-v1.2",
    "model_version": "1.2.0",
    "training_date": "2025-12-15",
    "training_samples": 287493
  },
  "evidence": [
    {
      "metric": "cpu_utilization",
      "trend": "increasing",
      "rate_of_change": "+0.8% per hour",
      "historical_pattern": "7-day upward trend"
    },
    {
      "metric": "memory_utilization",
      "value": 42.1,
      "status": "normal"
    },
    {
      "metric": "temperature",
      "value": 52,
      "unit": "celsius",
      "status": "normal"
    },
    {
      "metric": "fan_speed",
      "value": 4800,
      "unit": "rpm",
      "status": "normal"
    },
    {
      "metric": "power_consumption",
      "value": 145,
      "unit": "watts",
      "status": "elevated"
    }
  ],
  "affected_interfaces": [
    {
      "interface": "GigabitEthernet2/0/12",
      "status": "errors_increasing",
      "input_errors": 4829,
      "output_errors": 3921
    },
    {
      "interface": "GigabitEthernet2/0/13",
      "status": "errors_increasing",
      "input_errors": 5192,
      "output_errors": 4238
    }
  ],
  "recommended_actions": [
    "Schedule maintenance window within 48 hours",
    "Replace linecard in slot 2",
    "Verify slot 2 interfaces (Gi2/0/1 through Gi2/0/24)",
    "Migrate critical connections to slot 1 interfaces"
  ]
}

Key Findings: - CPU at 58% (baseline: 12%, threshold: 3σ = 20.5%) - 4.2 standard deviations from normal - DNM predicts failure in 72 hours with 89% confidence - Root cause: Linecard 2 hardware degradation - Affected ports: Gi2/0/12, Gi2/0/13 showing errors

Step 3: Validate DNM Prediction with Device Telemetry

3.1 Retrieve Device Health Metrics:

## Query Catalyst Center for detailed device health
curl -k -X GET \
  "https://dnac.abhavtech.com/dna/intent/api/v1/network-device/10.252.10.45/health" \
  -H "X-Auth-Token: $DNAC_TOKEN" \
  > /mnt/evidence_vault/EVD-20260121-002-device-health.json

## Extract relevant metrics
jq '.response | {
  overall_health: .overallHealth,
  cpu_score: .cpu.cpuUsage,
  memory_score: .memory.memoryUsage,
  client_count: .clientCount,
  interface_health: .interfaceStats
}' /mnt/evidence_vault/EVD-20260121-002-device-health.json

## Output:
{
  "overall_health": 6,  # Out of 10 (degraded)
  "cpu_score": 58.3,
  "memory_score": 42.1,
  "client_count": 48,
  "interface_health": {
    "good": 44,
    "degraded": 2,     # Gi2/0/12, Gi2/0/13
    "down": 0
  }
}

3.2 Query Historical CPU Trend:

## Export CPU utilization time series (7 days)
curl -k -X GET \
  "https://dnac.abhavtech.com/dna/data/api/v1/metrics/device/10.252.10.45/cpu?startTime=$(date -u -d '7 days ago' +%s)000&endTime=$(date -u +%s)000&interval=3600" \
  -H "X-Auth-Token: $DNAC_TOKEN" \
  > /mnt/evidence_vault/EVD-20260121-003-cpu-timeseries.json

## Visualize trend (using Python)
python3 << 'EOF'
import json
import matplotlib.pyplot as plt
from datetime import datetime

## Load data
with open('/mnt/evidence_vault/EVD-20260121-003-cpu-timeseries.json') as f:
    data = json.load(f)

## Extract timestamps and values
timestamps = [datetime.fromtimestamp(d['timestamp']/1000) for d in data['response']]
cpu_values = [d['value'] for d in data['response']]

## Calculate baseline (first 72 hours)
baseline_mean = sum(cpu_values[:72]) / 72
baseline_stddev = (sum((x - baseline_mean)**2 for x in cpu_values[:72]) / 72) ** 0.5

## Plot
plt.figure(figsize=(12, 6))
plt.plot(timestamps, cpu_values, label='CPU Utilization', linewidth=2)
plt.axhline(y=baseline_mean, color='green', linestyle='--', label=f'Baseline Mean ({baseline_mean:.1f}%)')
plt.axhline(y=baseline_mean + 3*baseline_stddev, color='orange', linestyle='--', label='3σ Threshold')
plt.axhline(y=baseline_mean + 4*baseline_stddev, color='red', linestyle='--', label='4σ Alert')
plt.xlabel('Time')
plt.ylabel('CPU Utilization (%)')
plt.title('Mumbai-FL3-C9300-IDF1 CPU Trend (7 Days)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('/mnt/evidence_vault/EVD-20260121-004-cpu-trend-chart.png', dpi=150)
print("✅ Chart saved: EVD-20260121-004-cpu-trend-chart.png")
EOF

## Register chart on blockchain
sha256sum /mnt/evidence_vault/EVD-20260121-004-cpu-trend-chart.png
peer chaincode invoke -n evidence-contract -C evidence-channel \
  -c '{"Args":["CollectEvidence","EVD-20260121-004","CASE-2026-004-DNM",...]}'

Chart Analysis: - Clear upward trend starting Jan 14 - Crossed 3σ threshold on Jan 18 - Crossed 4σ threshold on Jan 21 - Rate of increase: ~0.8% per hour - Projection: 100% CPU in ~52 hours (Jan 23, 12:00 UTC)

Step 4: Correlate with Splunk Telemetry

4.1 Query Switch Syslog for Hardware Errors:

index=network sourcetype=cisco:ios host="Mumbai-FL3-C9300-IDF1" earliest=-7d
| search ("IOSD" OR "PLATFORM" OR "HULC" OR "linecard" OR "asic")
| rex field=_raw "%(?\w+)-(?<severity>\d)-(?<mnemonic>\w+): (?<message>.*)"
| table _time facility severity mnemonic message
| sort _time

Results:
_time                  facility   severity  mnemonic          message
2026-01-14 11:58:23   IOSD       4         PACKET_INFRA      Packet buffer allocation failure, slot 2
2026-01-16 09:12:45   PLATFORM   3         ASIC_ERROR        ASIC error detected on linecard 2, port 12
2026-01-18 14:28:11   IOSD       4         PACKET_INFRA      Excessive buffer errors on slot 2
2026-01-19 16:45:33   PLATFORM   3         ASIC_ERROR        ASIC error detected on linecard 2, port 13
2026-01-20 18:42:09   IOSD       5         MEMORY_ERROR      Memory parity error, linecard 2 ASIC
2026-01-21 08:10:15   PLATFORM   2         HARDWARE_ERROR    Critical: Linecard 2 hardware failure imminent

## Syslog confirms DNM prediction: Linecard 2 hardware failure

4.2 Analyze Port Error Counters:

## SSH to switch and capture interface errors
ssh admin@10.252.10.45 "show interfaces Gi2/0/12 | include error"

## Output:
  Input errors: 4829, CRC: 1247, frame: 892, overrun: 0, ignored: 0
  Output errors: 3921, collisions: 0, interface resets: 0, babbles: 0

ssh admin@10.252.10.45 "show interfaces Gi2/0/13 | include error"

## Output:
  Input errors: 5192, CRC: 1389, frame: 945, overrun: 0, ignored: 0
  Output errors: 4238, collisions: 0, interface resets: 0, babbles: 0

## Normal interfaces for comparison:
ssh admin@10.252.10.45 "show interfaces Gi1/0/12 | include error"

## Output:
  Input errors: 0, CRC: 0, frame: 0, overrun: 0, ignored: 0
  Output errors: 0, collisions: 0, interface resets: 0, babbles: 0

## Linecard 2 ports have significant errors vs. linecard 1 (normal)

Step 5: Identify Affected Users and Applications

5.1 Query Connected Clients:

## Get clients connected to affected switch
curl -k -X GET \
  "https://dnac.abhavtech.com/dna/intent/api/v1/client-detail?macAddress=*&connectedNetworkDeviceIpAddress=10.252.10.45" \
  -H "X-Auth-Token: $DNAC_TOKEN" \
  | jq '.detail[] | {
      username: .userId,
      mac: .hostMac,
      ip: .hostIpV4,
      connected_interface: .clientConnection,
      vlan: .vlanId,
      health_score: .healthScore[]
    }'

## Results show 48 clients, 2 on problematic interfaces:
{
  "username": "finance-user-ravi",
  "mac": "00:50:56:89:AB:CD",
  "ip": "10.252.80.45",
  "connected_interface": "GigabitEthernet2/0/12",  # ← Affected port
  "vlan": "80",  # Finance VLAN
  "health_score": {
    "onboarding": 10,
    "connected": 6,  # Degraded
    "overall": 7
  }
}

{
  "username": "erp-server-01",
  "mac": "00:1A:2B:3C:4D:5E",
  "ip": "10.252.80.12",
  "connected_interface": "GigabitEthernet2/0/13",  # ← Affected port
  "vlan": "80",
  "health_score": {
    "onboarding": 10,
    "connected": 5,  # Degraded
    "overall": 6
  }
}

Critical Finding: ERP server connected to Gi2/0/13 (degraded interface)

5.2 Check Application Impact:

## Query AppDynamics for ERP server connectivity
curl -k -X GET \
  "https://abhavtech.saas.appdynamics.com/controller/rest/applications/ERP/metric-data?metric-path=Business%20Transaction%20Performance|Business%20Transactions|Database-Connect|Errors%20per%20Minute&time-range-type=BEFORE_NOW&duration-in-mins=10080" \
  -H "Authorization: Bearer $APPD_TOKEN" \
  | jq '.[] | select(.metricValues[].startTimeInMillis > 1642680000000)'

## Results show error spike:
## Jan 18-21: Average 2.3 database connection errors/min (normal: 0.1/min)
## Correlated with linecard degradation timeline

Step 6: DNM Model Explainability

6.1 Retrieve Model Training Data:

## Query DNM for model features and weights
curl -k -X GET \
  "https://dnac.abhavtech.com/dna/data/api/v1/aiNetworkAnalytics/model/XGBoost-Device-Failure-v1.2/explainability?anomalyId=ANM-2026-004-CPU-001" \
  -H "X-Auth-Token: $DNAC_TOKEN" \
  > /mnt/evidence_vault/EVD-20260121-005-dnm-explainability.json

## Parse feature importance
jq '.response.feature_importance | sort_by(-.importance) | .[:10]' \
  /mnt/evidence_vault/EVD-20260121-005-dnm-explainability.json

## Output (top 10 features contributing to prediction):
[
  {"feature": "cpu_utilization_trend_7d", "importance": 0.342, "value": 0.8},
  {"feature": "interface_error_rate_linecard", "importance": 0.287, "value": 4.2},
  {"feature": "cpu_stddev_deviation", "importance": 0.198, "value": 4.2},
  {"feature": "syslog_hardware_error_count", "importance": 0.089, "value": 6},
  {"feature": "power_consumption_increase", "importance": 0.042, "value": 1.15},
  {"feature": "temperature_delta", "importance": 0.018, "value": 0.05},
  {"feature": "memory_utilization", "importance": 0.012, "value": 0.42},
  {"feature": "fan_speed_variation", "importance": 0.007, "value": 0.03},
  {"feature": "uptime_days", "importance": 0.003, "value": 847},
  {"feature": "ios_version_age_days", "importance": 0.002, "value": 123}
]

Model Interpretation: - 34.2% weight: CPU trend over 7 days (most important) - 28.7% weight: Interface error rate on specific linecard - 19.8% weight: CPU deviation from baseline - 8.9% weight: Hardware error syslog count

Why DNM is Confident (89%): - All 4 top features are strongly positive - Historical patterns match training data for linecard failures - Similar failures: 147 instances in training set (287K samples) - Average time-to-failure for this pattern: 68 hours (DNM predicted 72h)

Step 7: Physical Layer Verification

7.1 Inspect Hardware Logs:

## SSH to switch and check hardware diagnostics
ssh admin@10.252.10.45

## View linecard status
Mumbai-FL3-C9300-IDF1# show platform software fed switch active ifm interfaces linecard 2

Interface  Linecard  Port  Admin  Oper   Errors    Drops     Comments
---------  --------  ----  -----  ----   ------    -----     --------
Gi2/0/12   2         12    up     up     4829      127       ASIC errors detected
Gi2/0/13   2         13    up     up     5192      149       ASIC errors detected
...

## Check linecard hardware errors
Mumbai-FL3-C9300-IDF1# show platform software fed switch active punt cause summary

Punt Cause                                      Packets   Rate (pps)
----------------------------------------------  -------   ----------
ASIC_ERROR_LINECARD_2                          8492      12.3
PACKET_BUFFER_ERROR                            4729      6.8
MEMORY_PARITY_ERROR                            147       0.2

## View environmental status
Mumbai-FL3-C9300-IDF1# show environment status

Sensor          Location       Current  Threshold  Status
--------------- -------------- -------- ---------- ------
Temp-Linecard-1 Linecard 1     48C      75C        Normal
Temp-Linecard-2 Linecard 2     62C      75C        High ⚠️
Temp-Supervisor Supervisor     45C      75C        Normal
Fan-1           Fan Tray       4800 RPM 3000 RPM   Normal
Fan-2           Fan Tray       4900 RPM 3000 RPM   Normal
Power-Supply-1  PS Bay 1       145W     180W       Normal

## Linecard 2 temperature elevated (62C vs. 48C for linecard 1)

7.2 Collect Hardware Logs for Evidence:

## Save all diagnostic output to evidence
ssh admin@10.252.10.45 "show tech-support" \
  > /mnt/evidence_vault/EVD-20260121-006-show-tech.txt

## This file is large (~15MB), compress and hash
gzip /mnt/evidence_vault/EVD-20260121-006-show-tech.txt
sha256sum /mnt/evidence_vault/EVD-20260121-006-show-tech.txt.gz
## b7c8d9e0f1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d7e8f9a0b1c2d3e4f5a6b7c8

## Register on blockchain
peer chaincode invoke -n evidence-contract -C evidence-channel \
  -c '{"Args":["CollectEvidence","EVD-20260121-006","CASE-2026-004-DNM",...]}'

Step 8: Proactive Remediation

8.1 Schedule Maintenance Window:

## Create change request in ServiceNow
curl -X POST https://abhavtech.service-now.com/api/now/table/change_request \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Basic <base64-creds>' \
  -d '{
    "short_description": "Replace linecard on Mumbai-FL3-C9300-IDF1",
    "description": "DNM predicted hardware failure in 72h. Proactive replacement of linecard slot 2 based on AI analysis.",
    "type": "standard",
    "risk": "low",
    "impact": "2",
    "urgency": "2",
    "planned_start_date": "2026-01-22 22:00:00",
    "planned_end_date": "2026-01-23 01:00:00",
    "assignment_group": "Network-Engineering",
    "justification": "Prevent unplanned outage affecting 48 users and critical ERP server"
  }'

## Returns: CHG0012345

8.2 Migrate Critical Connections:

## Identify critical devices on linecard 2
ssh admin@10.252.10.45 "show cdp neighbors Gi2/0/12 detail"

## Output shows:
## Device: erp-server-01
## IP: 10.252.80.12
## Platform: VMware ESXi host

## Plan migration:
## 1. Provision new port on linecard 1: Gi1/0/24
## 2. Configure identical VLAN/QoS
## 3. Cable move during maintenance window

## Pre-configure new port
ssh admin@10.252.10.45

Mumbai-FL3-C9300-IDF1# config t
Mumbai-FL3-C9300-IDF1(config)# interface Gi1/0/24
Mumbai-FL3-C9300-IDF1(config-if)# description ERP-Server-01-New-Port
Mumbai-FL3-C9300-IDF1(config-if)# switchport mode access
Mumbai-FL3-C9300-IDF1(config-if)# switchport access vlan 80
Mumbai-FL3-C9300-IDF1(config-if)# spanning-tree portfast
Mumbai-FL3-C9300-IDF1(config-if)# no shutdown
Mumbai-FL3-C9300-IDF1(config-if)# end
Mumbai-FL3-C9300-IDF1# write mem

## Backup port ready for cable move

8.3 Verify ISE Policy:

## Ensure ISE will authorize on new port
curl -k -X GET \
  "https://ise.abhavtech.com/ers/config/networkdevice/name/Mumbai-FL3-C9300-IDF1" \
  -H "Accept: application/json" \
  -u "forensics-api:$ISE_PASSWORD" \
  | jq '.NetworkDevice | {name: .name, ipAddress: .NetworkDeviceIPList[].ipaddress}'

## Verify port config consistent
## ISE will apply same policy on Gi1/0/24

Step 9: Execute Maintenance and Validate

Maintenance Execution: 2026-01-22 22:00 to 23:45 UTC

9.1 Pre-Maintenance Verification:

## Record baseline metrics before maintenance
curl -k -X GET \
  "https://dnac.abhavtech.com/dna/intent/api/v1/network-device/10.252.10.45/health" \
  -H "X-Auth-Token: $DNAC_TOKEN" \
  > /mnt/evidence_vault/EVD-20260122-001-pre-maintenance-health.json

## Record client count
CLIENTS_BEFORE=$(curl -k -s -X GET \
  "https://dnac.abhavtech.com/dna/intent/api/v1/client-detail?connectedNetworkDeviceIpAddress=10.252.10.45" \
  -H "X-Auth-Token: $DNAC_TOKEN" | jq '.detail | length')

echo "Clients before maintenance: $CLIENTS_BEFORE"
## Output: 48 clients

9.2 Perform Linecard Replacement:

22:05 - Cable ERP server from Gi2/0/13 to Gi1/0/24
22:07 - Verify ERP server connectivity (ping, ssh)
22:10 - Cable finance-user workstation from Gi2/0/12 to Gi1/0/23
22:12 - Power down switch
22:15 - Remove linecard from slot 2
22:20 - Install new linecard in slot 2
22:25 - Power on switch
22:30 - Wait for boot and fabric join
22:35 - Verify linecard online
22:40 - Test all slot 2 ports (loopback cables)
22:50 - Return ERP server to Gi2/0/13 (new linecard)
22:55 - Return finance-user to Gi2/0/12 (new linecard)
23:00 - Verify all clients online
23:10 - Monitor for 30 minutes
23:40 - Close change request

9.3 Post-Maintenance Validation:

## Verify device health improved
curl -k -X GET \
  "https://dnac.abhavtech.com/dna/intent/api/v1/network-device/10.252.10.45/health" \
  -H "X-Auth-Token: $DNAC_TOKEN" \
  > /mnt/evidence_vault/EVD-20260122-002-post-maintenance-health.json

## Compare health scores
jq '{
  before: .overallHealth,
  cpu_before: .cpu.cpuUsage
}' /mnt/evidence_vault/EVD-20260122-001-pre-maintenance-health.json

## Output:
{"before": 6, "cpu_before": 58.3}

jq '{
  after: .overallHealth,
  cpu_after: .cpu.cpuUsage
}' /mnt/evidence_vault/EVD-20260122-002-post-maintenance-health.json

## Output:
{"after": 10, "cpu_after": 11.8}

## Health restored: 6 → 10, CPU: 58.3% → 11.8%

9.4 Verify Client Impact:

## Check client count post-maintenance
CLIENTS_AFTER=$(curl -k -s -X GET \
  "https://dnac.abhavtech.com/dna/intent/api/v1/client-detail?connectedNetworkDeviceIpAddress=10.252.10.45" \
  -H "X-Auth-Token: $DNAC_TOKEN" | jq '.detail | length')

echo "Clients after maintenance: $CLIENTS_AFTER"
## Output: 48 clients (same as before)

## Zero client impact - seamless maintenance

Step 10: DNM Prediction Validation and Reporting

10.1 Analyze Failed Linecard:

## Send failed linecard to Cisco TAC for RMA
## Record serial number and failure details
ssh admin@10.252.10.45 "show inventory"

## Failed linecard details:
NAME: "Linecard 2", DESCR: "Catalyst 9300 24-port 10G Network Module"
PID: C9300-NM-8X             , VID: V01  , SN: JAE2347A8BG

## TAC Case: 612345678
## RMA Number: 84729374

10.2 Validate DNM Accuracy:

## Calculate prediction accuracy
prediction = {
    "predicted_time_to_failure_hours": 72,
    "actual_time_to_failure_hours": None,  # Prevented by replacement
    "confidence": 0.89,
    "outcome": "prevented_by_proactive_action"
}

## DNM prediction timeline:
## Alert: 2026-01-21 08:15 UTC
## Predicted failure: 2026-01-24 08:15 UTC (72h)
## Actual intervention: 2026-01-22 22:00 UTC (37.75h before predicted failure)

## Post-replacement analysis:
## CPU returned to baseline (11.8%) immediately after linecard replacement
## Interface errors on new linecard: 0
## Temperature on new linecard: 47C (vs 62C on failed linecard)

## Conclusion: DNM prediction ACCURATE
## Proactive intervention prevented unplanned outage

10.3 Generate Forensics Report:

report = {
    "case_id": "CASE-2026-004-DNM",
    "investigation_type": "Deep Network Model Alert Investigation",
    "incident_date": "2026-01-21",
    "analyst": "Network-Engineer-Suresh-Patel",

    "executive_summary": """
    Catalyst Center Deep Network Model detected anomalous CPU pattern on
    Mumbai Floor 3 access switch on 2026-01-21, predicting hardware failure
    within 72 hours with 89% confidence. Investigation confirmed linecard 2
    hardware degradation with ASIC errors, elevated temperature (62C), and
    interface errors on Gi2/0/12 and Gi2/0/13.

    Proactive maintenance performed on 2026-01-22 (37 hours before predicted
    failure), replacing failed linecard during scheduled window. Zero client
    impact achieved through pre-migration of critical ERP server connection.

    Outcome: SUCCESSFUL PREVENTION
    - Prevented unplanned outage affecting 48 users
    - Protected critical ERP and CRM applications
    - Validated DNM prediction accuracy (89% confidence justified)
    - Zero downtime during planned maintenance

    Value: ~$45,000 (avoided outage cost + productivity loss prevention)
    """,

    "dnm_analysis": """
    Deep Network Model Analysis:
    - Alert triggered: CPU deviation 4.2σ from baseline
    - Prediction: Hardware failure in 72 hours
    - Confidence: 89%
    - Root cause: Linecard 2 hardware degradation

    Model Feature Importance:
    1. CPU utilization trend (7d): 34.2% weight
    2. Interface error rate: 28.7% weight
    3. CPU stddev deviation: 19.8% weight
    4. Hardware error syslog: 8.9% weight

    Validation:
    ✅ CPU returned to baseline (58.3% → 11.8%) after replacement
    ✅ Interface errors eliminated (4829 → 0) on new linecard
    ✅ Temperature normalized (62C → 47C)
    ✅ All client connections stable post-maintenance

    DNM Accuracy: CONFIRMED (prediction would have been accurate)
    """,

    "recommendations": [
        "Continue using DNM for proactive device monitoring",
        "Implement DNM alerts for all critical infrastructure",
        "Schedule quarterly hardware health reviews based on DNM trends",
        "Create automated playbook for DNM-triggered maintenance",
        "Train NOC team on DNM alert interpretation",
        "Integrate DNM predictions with ServiceNow change management"
    ]
}

## Save report
with open('/mnt/evidence_vault/REPORT-CASE-2026-004-DNM.json', 'w') as f:
    json.dump(report, f, indent=2)

## Register on blockchain
sha256sum /mnt/evidence_vault/REPORT-CASE-2026-004-DNM.json
peer chaincode invoke -n evidence-contract -C evidence-channel \
  -c '{"Args":["CollectEvidence","EVD-20260122-REPORT","CASE-2026-004-DNM",...]}'