Frontier AI models have upended the cybersecurity calculus: if it’s reachable, it’s breachable. The implicit trust inherent to traditional SD-WAN makes it all too easy for AI to scan & compromise your crown jewel assets. In this session, discover how to future-proof branch security by moving beyond legacy stacks. Learn how a direct-to-cloud Zero Trust architecture cloaks your assets, eliminates lateral movement risk, and simplifies branch operations—delivering superior security and performance for users, IoT, and AI agents.
In 2026, vendors rapidly released AI-agent-driven network operations tools. Yet relying on AI in operations demands clarity: what logic and which knowledge led to each recommendation? Our customers in Japan want to know how to detect and correct an agent’s wrong judgment, how to teach it, and how to roll a mis-trained agent back to a previous state. We propose applying practices established in AI coding agents to network operations, illustrated with a demo.
Agents autonomously access data, AI models, services and other agents in unpredictable patterns, making the communication paradigm for agentic applications non-prescriptive and difficult to secure and govern. This talk explores the imperatives for effective governance of agentic applications and the critical role of the network infrastructure in enforcing governance policies across a constantly evolving ecosystem of agents and software frameworks. The infrastructure-centered Gemini Enterprise Agent Platform governance model is proposed as the vehicle to shift down the highly complex operational environment required to securely deploy…
AI is changing network automation in two ways: how we build automation platforms, and how we use them to operate infrastructure. AI makes it easier to build software, integrations, and automation, but it does not remove the long-term cost of owning and maintaining them. At the same time, AI agents need clean data, clear interfaces, controlled change processes, and auditability to operate infrastructure safely. This session explores what to build, buy, or assemble, and how to create an automation foundation that works for both humans…
The industry’s current answer to every network-operations problem is “put an LLM on it.” And then the LLM chokes on a million rows of telemetry, reasons differently about the same incident twice, and takes a different action on Tuesday than it did on Monday. That is not a model problem — it is an engineering problem, and it has an engineering answer. In this session CodiLime shows how to build an agent that operators can genuinely trust: treat the LLM as an intelligent abstraction layer…
The cryptography protecting your network today is already on a clock. Adversaries know it, and they are collecting encrypted traffic now to decrypt once quantum computing matures. WWT helps enterprises move from awareness to action: inventory every algorithm, key, and certificate in use, prioritize by data sensitivity and shelf life, build a crypto-agility roadmap aligned to NIST-approved standards, and validate the transition in the Advanced Technology Center before it reaches production. Organizations that start now will migrate on their own timeline rather than under deadline…
Most enterprises don’t manage their ISP and data estate — they divide it. Engineers own the technology, procurement owns the contracts, AP pays the bills, and nobody owns the whole. This session lays out a maturity curve for enterprise ISP management based on benchmarking with 150+ peers. Attendees will place their own organization on the curve, see what separates each tier from the next, and leave with the model plus a practical checklist for moving up a level to support the AI-driven networks of the…
Enterprise AI is moving beyond helping operators investigate incidents to taking on operational work itself. This session explores the shift from AI-assisted operations to trusted agentic operations—and why it requires a fundamentally different architecture. Learn how persistent memory, adaptive learning, explainable reasoning, and governance distinguish true agentic systems from copilots and enable enterprises to progressively delegate operational work to AI. Together, these capabilities form an Operational Metacortex: a persistent intelligence layer that learns, reasons, and acts across operations, creating a practical path from human-in-the-loop workflows…
As enterprises operationalize AI, managing data governance, sovereignty, and movement costs across distributed environments has become the primary bottleneck to scalable deployment. Establishing AI data platforms, lakehouses, and federated data functions at vendor-neutral interconnection hubs decouples critical enterprise data assets from specific compute venues. This session explores how housing the AI data control plane where enterprise networks, public clouds, and specialized neoclouds intersect enables zero-copy federation, real-time RAG, and edge caching—feeding any GPU environment while preserving governance, minimizing latency, and eliminating data gravity lock-in.
