Network operations centers spend most of their time on troubleshooting. A single outage can generate thousands of alerts across hundreds of devices. Finding the root cause requires correlating events across multiple vendors, protocols, and time windows.
Traditional troubleshooting is manual and slow. Engineers query multiple systems, build mental models of the topology, and trace failures through cascading effects. Complex incidents take hours to resolve.
The Troubleshooting Challenge
Modern networks generate massive event volumes:
| Source | Events/Day | During Outage |
|---|---|---|
| Routers | 500,000 | 5,000,000+ |
| Switches | 1,000,000 | 10,000,000+ |
| Firewalls | 2,000,000 | 20,000,000+ |
| Load Balancers | 200,000 | 2,000,000+ |
| Wireless | 300,000 | 3,000,000+ |
During an outage, event volumes spike 10x or more. Every device reports problems. Most alerts are symptoms, not causes. Finding the root cause requires filtering signal from noise across millions of events.
AI-Powered Root Cause Analysis
LogZilla AI NetOps transforms troubleshooting. Engineers describe the problem in plain English. The AI correlates events, identifies root causes, and provides remediation commands.
Example prompt: "Analyze all network events from the last 2 hours compared to baseline. Identify the root cause of current connectivity issues and provide remediation steps."
AI response includes:
- Executive summary with severity assessment
- Root cause identification with confidence score
- Cascading failure timeline
- Topology impact map
- Affected devices and services
- Vendor-specific CLI commands for remediation
- Estimated resolution time
Download sample NetOps output (PDF)
Key Capabilities
Multi-Vendor Correlation
LogZilla normalizes events from all network vendors:
- Cisco: IOS, IOS-XE, NX-OS, ASA
- Juniper: Junos, ScreenOS
- Arista: EOS
- Palo Alto: PAN-OS
- Fortinet: FortiOS
- F5: BIG-IP
- Aruba/HPE: ArubaOS, ProCurve
Events correlate across vendor boundaries. A Cisco router failure affecting Juniper switches and Palo Alto firewalls appears as a single incident with traced causality.
Topology Impact Assessment
LogZilla AI maps failures to network topology:
text[Internet] | [Core Router] ← ROOT CAUSE / | \ / | \ [Dist-SW-1] [Dist-SW-2] [Dist-SW-3] | | | [Access] [Access] [Access] | | | [Users] [Servers] [IoT]
Impact assessment shows:
- Primary failure point
- Directly affected devices
- Cascading failures
- End-user impact scope
- Service dependencies
Baseline Comparison
LogZilla AI compares current behavior to historical baselines:
- Normal event rates vs. current rates
- Expected protocol behavior vs. anomalies
- Typical failure patterns vs. new issues
- Seasonal variations and maintenance windows
Baseline comparison distinguishes between normal operations and actual problems, reducing false positive investigations.
Vendor-Specific Remediation
LogZilla generates copy-paste commands for immediate action:
Cisco IOS interface recovery:
textinterface GigabitEthernet0/1 no shutdown description Recovered by NOC exit write memory
Juniper Junos BGP reset:
textrestart routing show bgp summary show route protocol bgp
Arista EOS MLAG recovery:
textshow mlag mlag reload-delay non-mlag 300 show mlag detail
Commands generate based on the specific issue and device type. Engineers copy-paste directly into device CLIs.
Real-World Example
A LogZilla customer experienced a major network outage affecting multiple sites. Traditional troubleshooting would have taken hours.
Prompt: "Analyze network events from the last 2 hours compared to baseline. Identify root cause and provide remediation."
Results (5.06 million events analyzed):
- Root cause: PKI certificate expiration on core router
- 847 devices affected across 3 sites
- Cascading authentication failures traced
- Step-by-step certificate renewal commands provided
- Estimated resolution: 15 minutes
The AI identified the root cause in minutes. Manual correlation across 5 million events would have taken hours.
Protocol Analysis
LogZilla AI understands network protocols and their failure modes:
Routing Protocols
- OSPF: Neighbor state changes, SPF calculations, area issues
- BGP: Peer state transitions, route withdrawals, path changes
- EIGRP: Neighbor relationships, stuck-in-active, topology changes
- IS-IS: Adjacency failures, LSP issues, metric changes
Switching Protocols
- STP/RSTP: Topology changes, root bridge elections, port states
- LACP: Bundle failures, member port issues, load balancing
- VXLAN: VTEP failures, VNI issues, underlay problems
Security Protocols
- IPsec: Tunnel failures, IKE negotiations, SA expirations
- 802.1X: Authentication failures, RADIUS issues, MAB fallback
- MACsec: Key agreement failures, encryption issues
Common Network Failure Patterns
AI NetOps recognizes common failure patterns and their signatures:
Interface Flapping
Interface flapping generates thousands of events as links cycle up and down. Traditional monitoring creates alert storms. AI NetOps consolidates these events and identifies the underlying cause:
| Symptom | Possible Causes | AI Analysis |
|---|---|---|
| Rapid up/down cycles | Cable fault, SFP failure | Check error counters, CRC errors |
| Periodic flapping | Spanning tree issues | Analyze STP topology changes |
| Correlated flaps | Upstream failure | Trace to root cause device |
| Single interface | Hardware failure | Recommend replacement |
BGP Session Instability
BGP issues affect routing across the network. AI NetOps correlates BGP events with underlying causes:
textBGP Analysis Report =================== Symptom: BGP peer 192.168.1.1 flapping every 90 seconds Correlation: Interface Gi0/0 showing CRC errors Root Cause: Fiber patch cable degradation Impact: 47 prefixes affected, traffic rerouting via backup path Remediation: Replace fiber patch cable on Gi0/0
Authentication Failures
Mass authentication failures indicate infrastructure problems, not user errors:
| Pattern | Likely Cause | Verification |
|---|---|---|
| All users, one site | RADIUS server unreachable | Check RADIUS connectivity |
| All users, all sites | Certificate expiration | Verify PKI chain |
| Specific VLAN | DHCP exhaustion | Check DHCP scope |
| Intermittent | Network congestion | Analyze traffic patterns |
Spanning Tree Reconvergence
Unexpected STP topology changes cause network instability. AI NetOps traces reconvergence events to their source:
- Identify the bridge claiming root
- Trace topology change notifications
- Correlate with physical events (link failures, device reboots)
- Recommend STP hardening (root guard, BPDU guard)
Measuring NOC Efficiency
Organizations track specific metrics to quantify AI NetOps improvements:
Resolution Time Metrics
| Metric | Before AI | After AI | Improvement |
|---|---|---|---|
| Mean time to identify (MTTI) | 45 minutes | 3 minutes | 93% |
| Mean time to resolve (MTTR) | 2.5 hours | 25 minutes | 83% |
| Escalation rate | 35% | 8% | 77% reduction |
| After-hours callouts | 12/month | 3/month | 75% reduction |
Operational Metrics
| Metric | Before AI | After AI | Impact |
|---|---|---|---|
| Incidents per engineer/day | 4 | 15 | 275% increase |
| First-call resolution | 45% | 82% | 82% improvement |
| Repeat incidents | 18% | 4% | 78% reduction |
| Customer complaints | 25/month | 5/month | 80% reduction |
These metrics demonstrate that AI NetOps delivers measurable improvements in both efficiency and service quality.
Integration with NOC Workflows
Ticketing Integration
LogZilla AI analysis exports to ticketing systems:
- ServiceNow incident creation
- Jira issue generation
- PagerDuty alert enrichment
- Custom webhook integrations
Runbook Automation
AI-generated remediation commands integrate with automation platforms:
- Ansible playbook triggers
- Terraform state updates
- Custom script execution
- Change management workflows
Escalation Support
AI analysis provides context for escalations:
- Vendor TAC case preparation
- Executive summary for management
- Timeline documentation for post-mortems
- Evidence collection for RCA
Deployment Considerations
Data Collection
Effective AI NetOps requires comprehensive log collection:
- Syslog from all network devices
- SNMP traps and polling data
- NetFlow/sFlow for traffic analysis
- API polling for device state
Retention Requirements
Historical data enables baseline comparison:
- 7 days minimum for immediate troubleshooting
- 30 days recommended for trend analysis
- 90+ days for seasonal pattern recognition
Performance Requirements
Real-time analysis requires adequate infrastructure:
- Sub-second query response for interactive use
- Batch analysis for comprehensive reports
- Scalable storage for high-volume environments
Building Effective Network Prompts
AI NetOps effectiveness depends on well-constructed prompts. Engineers who provide context receive better analysis.
Basic vs. Advanced Prompts
Basic prompt: "What's wrong with the network?"
Advanced prompt: "Analyze all network events from the last 2 hours compared to the same period yesterday. Focus on core and distribution layer devices. Identify any routing protocol instability, interface errors above baseline, or authentication failures. Provide Cisco IOS and Junos remediation commands."
The advanced prompt specifies:
- Time window and comparison baseline
- Device scope (core and distribution)
- Specific concerns (routing, interfaces, authentication)
- Output format (vendor-specific commands)
Prompt Templates for Common Scenarios
Outage investigation:
textAnalyze network events from [time range]. Identify the root cause of connectivity issues affecting [scope]. Trace cascading failures and provide remediation commands for [vendor list].
Performance degradation:
textCompare network performance metrics from [current period] to [baseline period]. Identify interfaces with increased errors, latency, or packet loss. Correlate with any configuration changes or external events.
Proactive health check:
textGenerate a network health report for the last 24 hours. Identify any devices showing warning signs, approaching capacity limits, or deviating from normal behavior patterns. Prioritize findings by risk level.
Micro-FAQ
What is AI NetOps?
AI NetOps uses artificial intelligence to automate network operations tasks including root cause analysis, topology impact assessment, and remediation guidance. It reduces troubleshooting time from hours to minutes.
How does LogZilla identify network root causes?
LogZilla AI correlates events across all network devices, identifies the initial failure point, and traces cascading effects through the topology. Analysis includes timeline reconstruction and confidence scoring.
What network vendors does LogZilla support?
LogZilla generates remediation commands for Cisco, Juniper, Arista, Palo Alto, Fortinet, F5, and 20+ additional vendors. Commands are ready to copy-paste into device CLIs.
Can AI NetOps handle multi-vendor environments?
Yes. LogZilla normalizes events from all vendors into a common format for correlation while preserving vendor-specific details for remediation command generation.
Next Steps
AI-powered network operations reduce troubleshooting time from hours to minutes. LogZilla AI NetOps correlates events across multi-vendor environments, identifies root causes with confidence scoring, and provides vendor-specific remediation commands. Watch the AI NetOps demo to see automated root cause analysis in action.