AI NETWORKING
This page was developed with material from the article published in ISE magazine in October 2024:
“AI Networking: Training the Unruly Child.”
Introduction
This page covers the intersection between AI Networking and cybersecurity. It will continue to be developed as use and possible abuse of Generative AI matures:
- Updates: RAG emerges to validate data.
- AI Networking Work in Progress at ONUG
- AI Networking Summit (October 23-24 update)
- AI-based Cybersecurity threats (coming shortly)
Practical Solutions, Adaptive AI, RAG and Power.
Enter RAG. Retrieval-Augmented Generation (RAG) – a technique for enhancing the accuracy and reliability of generative AI models with facts retrieved from external sources. This update overcomes the issues of inaccuracies the date and undermine results incidated in the previous paragraph.
The elephant in the AI room is power. No-one is coming up with a viable solultion the is affordable or can be powered without attaching a power station to the cloud data centers. The internal network debate is definitiely heading in the direction of Ethernet.
Always Look on the Bright Side of Life
If I could give you ten actions that would ensure your network meets your business goals, you’d want to know about them, right? However, if I said one of them made no sense but I’m not going to tell you which one—you’d think I was crazy and definitely not intelligent. Imagine if that applied to a critical part of your network infrastructure.
This page is not intended to be a Luddite rant, since I do think that this is an exciting journey for all of us. However, before we get to the positive aspects there’s still the issue of cybersecurity threats penetrating the Large Language Models (LLMs). I.e., how do you know if the data you are basing your decisions on is inaccurate or deliberately misleading. For instance, if a threat actor deliberately distorts data about an asset’s authentication or privilege, then your network and organization is now toast.
Since we just started the AI Networking journey, let’s assume that these challenges will get sorted out quickly, making AI smarter and safer. Protection against the abuse of LLMs is already under way. The questions are what are the most promising candidates to simplify and handle network complexity? Are they cost-justified, and how can we apply some actual human intelligence, our experience and expertise to train this unruly GenAI child?
AI for Network as a Service (NaaS)
Since my article in the May/June issue, NaaS has made rapid progress. At the core is the need for services to be on-demand, available from multiple providers without lock-in, in multiple locations, accessing workloads in multiple clouds with varying performance requirements.
The advantage to any service providers or systems integrators who could offer such a service is huge, since it meets all of the enterprise requirements. An Adaptive GenAI system—one that constantly retrains as data changes—may be the only viable, scalable approach. Such a system could gather and update the information accessed either via APIs or a portal.
Managed Service Providers would be served well too, getting access to the services offered on behalf of their users. For those who are becoming aware of various. The race is on to develop this approach as it addresses the most challenging aspect of Network as a Service.
The use of AI Networking to the Orchestration/Automation of NOC/SOC (Network and Security Operations is one of the projects of the Open Networking User Group Collaborative (ONUG.net), where I participate. An interesting part or this work covers the choices of how to train LLMs or SLMs (industry/context specific Small Language Models) using publicly available services, GenAI augmented tools or custom solutions developed in-house.
Other Promising Areas
- Policy as code : Using GenAI to bring Zero Trust principals to automate access, identity, authentication, privilege control, policy enforcement and continual monitoring.
- In ONUG’s Cloud Secure Notification Framework project, GenAI could oversee and report on large quantities of different network data/monitoring/networking incident reports involving Cloud-based workloads.
There are many more. Some suggest immediate actions and others are currently under investigation. Speaking of which, the publication of this article is timed to alert readers to ONUG’s Fall event where industry luminaries give the very latest on their work and their perspectives on the state-of-the-art of AI Networking. If it’s anything like the ONUG Spring event, you do not want to miss it. Check it out at https://onug.net/.
ONUG Fall: AI Networking Summit October 24th
- This section will be updated following this mindbending event.
- Tsvi Gal, CTO and Head of Enterprise Technology Services (Infrastructure), Memorial Sloan Kettering Cancer Center explained how they are using AI to cure cancers. The majority of AI they use is more traditional AI/ML and automation rather than GenAI (yet.
- Cisco and AD both featured their rapid innovation in their technologies based on doubling GPU performance and role of Ethernet with the challenges of access to power.
- In the panel moderated by Andy Brown, Tsvi emphasized the driver of a legitimated business case measured during the execution. Gene Sun, CVP & CISO, FedEx sees 100 or so applications. He was optimistic looking forward to a robot looking after him in his 80s and nineties, rather than being shipped of to an old folks home! He was also optimistic that based on history humans have always adapted to new innovations.
- The biggest takeaway is the progress being made to augment LLMs with external data using Retrieval-Augmented Generation (RAG).
Conclusion
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[1] https://onug.net/onug-collaborative-blog/evaluating-different-approaches-for-adopting-generative-ai-in-network-and-security-operations/
AI Platforms of note: Pros and cons
Reflection 70B
To quote itself: “Reflection 70B has made headlines for its ability to outperform some of the most well-known proprietary models, including GPT-4 and Claude 3.5 Sonnet. On a wide range of benchmarks, this open-source model has shown remarkable results in reasoning, math, and general knowledge tasks. These performance gains are attributed to its ability to engage in self-correction and fine-tuned reasoning, setting it apart from previous models in the space.” Others have discredited it as a fraud.