Скачать или смотреть Cisco U. Theater Sessions | Cisco Live Melbourne 2025 | Day 1

00:00 - AI Gone Rogue: How Attackers are Breaking LLMs
As large language models (LLMs) become core to enterprise systems, new threats - such as those in the OWASP Top 10 for LLMs - are emerging fast. This session explores real-world case studies of attacks like prompt injection, jailbreaks, and model poisoning, showing where traditional security fails. Attendees will gain practical mitigation tactics, including prompt validation, output filtering, and identity-aware controls, and discover how Cisco AI Defense detects and responds to LLM abuse. Security professionals, DevSecOps, and architects will leave better equipped to defend AI deployments.

35:00 - Running on-prem AI agents for NOC environments
In this session we will showcase how to build an AI agent in n8n that provides a chat interface to interact with Syslog messages received on network devices and stored in a central location in Google Drive. An introduction to n8n, an automation platform that is low code and provides an easy way to connect different applications is also provided. We'll also look at vector databases, running LLMs locally and how to use RAG to provide additional training data for LLMs. By the end of the session we'll have a fully working AI agent bringing all these different technologies together.

1:07:00 - 3 Clicks to Release: Publish Your Automation Tools with GitHub Workflows
Continuous Integration / Continuous Delivery (CI/CD) pipelines are often thought of as an advanced topic that is beyond the skill level of NetDevOps engineers. You need special skills, new programming languages, and a full team to help you - right? Wrong! I have news for you - GitHub workflows are nothing more than a series of files, written in a standard format known as YAML. In this session, you will see a live update of a Python project that launches a GitHub workflow to perform syntax and code tests. If all tests pass and the code is suitable, a merge event triggers a release workflow that includes publishing the built project to PyPi so others can use the code! Join me to remove the mystery and complexity of CI/CD!

1:40:27 - CCNP Prep: OSPF LSAs and Special Areas
Is it stubby, totally stubby or not so stubby? What’s the difference between a Type 3 and Type 5 LSA? Get ready for your ENCOR and ENARSI exams with this review of OSPFv2 LSAs and special areas.

2:14:00 - GenAI Your Way: Reimagine Network Troubleshooting
Join us as we reimagine network troubleshooting with AI-powered solutions using Model Context Protocol (MCP) servers and open-source GenAI models.
Traditional troubleshooting can be a time-consuming and complex process, requiring deep expertise and manual effort. What if you could simplify this by building your own AI-powered troubleshooting assistant using open-source models? We'll review the core concepts for building MCP servers that work with multiple network platforms: Catalyst Center and Meraki. You'll discover MCP development options for both imperative workflows and declarative AI flows, and use natural language queries with your network infrastructure.
Prior experience with GenAI, LLMs, MCP is beneficial, but not required. Demos and complete open-source sample code will be included.

2:46:00 - Using Terraform to Automate your Meraki Deployment
The Meraki Dashboard is a wonder tool to easily deploy a large scale network including SD-WAN, Wireless, and many features and functionality. The challenge when you have many networks in on organization or multiple organizations to manage, configure and monitor is that even with the ability to use templates and clone networks - you might not want to do just that. Using Terraform you can leverage the best of both worlds, configure and deploy through the Dashboard API to get each and every network exactly the way you want it while also maintaining the ease of use and operational simplicity of the Meraki Dashboard. In this session we'll show benefits of automation via terraform vs the standard capabilities in the Meraki Dashboard.

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