AI Is Now Entering Second Phase, Cisco's Patel Says

Key Concepts

• AI Agents: Autonomous systems conducting tasks and jobs.
• Infrastructure Constraints: Limitations in power, compute, networking, and bandwidth hindering AI growth.
• Trust Deficit: Lack of confidence in AI systems due to safety and security concerns.
• Data Gap: Insufficient use of machine data for AI model training.
• Machine Data: Time-series data generated by machines, crucial for AI training.
• Telemetry: Data automatically collected from remote sources (networks, devices) and transmitted to a receiving station for monitoring.
• Lateral Movement: The ability of a cyber attacker to move between systems within a network.
• Beta Next Better: Internal Cisco rule emphasizing solutions that are significantly better (an order of magnitude) than existing options.
• Open Source Model: A publicly available model for time-series data.
• Hyperscalers, Neo Clouds, Service Providers, Enterprise: Different types of organizations utilizing data centers.
• Securing AI: Protecting AI systems from threats, not just using AI for security.

AI's Second Phase and Cisco's Role

The speaker discusses the "seismic shift" occurring with the move to the second phase of AI, characterized by AI agents that autonomously conduct tasks and automate workflows. Cisco aims to address three key constraints hindering AI growth: infrastructure limitations, a trust deficit, and a data gap.

Addressing Infrastructure Constraints

The first constraint is the lack of sufficient infrastructure (power, compute, networking, bandwidth) to meet the growing demand for AI. Cisco is helping companies with massive data center buildouts, serving hyperscalers, neo clouds, service providers, and enterprises. Their core value proposition includes low latency, high-performance, and energy-efficient networking.

Overcoming the Trust Deficit

The second constraint is the "trust deficit." Users are hesitant to adopt AI systems if they don't trust their safety and security. Cisco is focused on ensuring AI is safe and secure, not just using AI for security but also securing AI itself. They have offerings to help customers in this area.

Bridging the Data Gap with Splunk

The third constraint is the "data gap," specifically the underutilization of machine data for AI model training. While AI models have been trained on human-generated data, they haven't effectively leveraged time-series machine data. Cisco, in collaboration with Splunk, aims to solve this problem.

Cisco and Splunk Partnership

Cisco and Splunk are working together to leverage machine data for AI. Splunk traditionally excels at correlating data across multiple sources. Cisco provides telemetry data from its network infrastructure (users, devices, security firewalls) to Splunk, enhancing Splunk's insights. This combined approach allows customers to derive insights that were previously unattainable and use resources more economically. Cisco is ingesting firewall telemetry into Splunk for free, providing financial incentives for customers.

Technological Innovation and "Beta Next Better"

Cisco emphasizes relentless innovation and customer focus. Their internal rule, "Beta Next Better," dictates that solutions must be at least an order of magnitude better than existing options to drive adoption. This approach was evident in the positive customer response to the machine data leak announcement and the open-source model for time-series data.

Evidence of Cisco-Splunk Synergy

The partnership between Cisco and Splunk is already yielding results. A large financial services organization, a Cisco customer, replaced a competitor with Splunk due to its ability to correlate data across multiple sources. Cisco's network telemetry, when integrated with Splunk, helps prevent lateral movement by attackers within the network.

The Importance of Machine Data

The speaker highlights that 55% of data growth comes from machine data, yet the market hasn't effectively utilized it for AI. Cisco aims to solve this problem by combining its technologies with Splunk's on a unified platform.

Cisco's Future Vision

Cisco aims to be a leader in the second phase of AI, helping companies with data center buildouts, ensuring AI safety and security, and enabling the effective use of machine data for AI. The combination of these three areas, supported by Cisco's breadth and depth of technology, positions the company for success.

Conclusion

Cisco is positioning itself as a key player in the evolving AI landscape by addressing critical infrastructure, security, and data challenges. Their partnership with Splunk, focus on machine data, and commitment to innovation are central to their strategy. The company aims to empower organizations to harness the full potential of AI through a unified platform that leverages network telemetry, advanced analytics, and robust security measures.