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Explore how artificial intelligence is revolutionizing packet analysis in this 27-minute conference presentation that examines the practical applications and current limitations of AI-driven network troubleshooting. Learn about comprehensive testing of popular Large Language Models (LLMs) including Claude, GPT, Copilot, and Gemini when analyzing PCAP data, with detailed findings on their varying capabilities in identifying network anomalies such as server response delays. Discover practical use cases for AI in packet analysis, including gaining contextual insights on filtered captures, assistance with complex T-shark commands and regular expressions, and identifying overlooked issues in network conversations. Understand the significant challenges facing current AI implementations, particularly data processing limitations where most LLMs struggle beyond 200 packets, and the critical importance of sanitizing PCAP data before uploading to cloud-based AI services due to sensitive network traffic concerns. Examine purpose-built AI tools like Selector.ai's Packet Copilot and Packet Safari Copilot that show promising developments in the field. Gain insights into effective prompt engineering techniques for extracting meaningful analysis from LLMs and understand where AI proves reliable versus where human validation remains essential. Walk away with practical guidance on integrating AI into existing troubleshooting workflows to streamline manual tasks, improve accuracy, and gain deeper insights into network behavior patterns that might otherwise go unnoticed.
