Project

LLM-Decoder for SNMP Traps and Syslogs

Objective

Project Overview Network operation teams often face thousands of cryptic traps and syslogs per day. This Watsonx.ai-based solution will use LLMs enhanced with Retrieval-Augmented Generation (RAG) to translate raw logs into plain English, classify their severity, and recommend next steps. Business Problem Complexity: Logs are encoded with device-specific MIB data, hex values, and vendor-specific messages. Volume: Large networks overwhelm L1 teams with alerts. Delay in Response: Manual interpretation extends Mean Time to Resolution (MTTR). Knowledge Gaps: Specialized expertise isn’t available 24/7. Inconsistency: Different analysts interpret the same alert differently. Implementation Roadmap Proof of Concept: Limited to key device families. Pilot Deployment: With selected network segments and user validation. Full Deployment: Integrated with monitoring and/or ticketing tools. Continuous Learning: Feedback loop to improve accuracy and coverage.

Outcome

Proposed Solution --- Core Components LLM Engine: Watsonx.ai model fine-tuned for network logs. RAG Layer: Ingest MIBs, past alerts, and vendor documentation for contextual grounding. Processing Pipeline: Log normalization and enrichment with device metadata LLM-driven plain-language decoding and severity classification Output to dashboards or ticketing systems Key Features Human-Readable Explanations: Reduce ambiguity in alerts. Severity Ranking: Critical, Major, Minor, Informational. Root Cause Hints: Based on past patterns. Recommended Actions: Guided troubleshooting steps. Easy Integration: REST APIs or connectors to NMS/OSS platforms. Expected Outcomes Reduced MTTR by 40–60% through faster interpretation. Improved Operational Efficiency: L1 teams can handle more alerts without escalation. Consistency & Knowledge Sharing: Same interpretation across shifts and regions. Path to Automation: Foundation