Hi Haoyu, 


Thank you for your review. Your discussion are very helpful for improving the 
draft. 



Here are some response: 

1.You are absolutely right that distributed architectures introduce their own 
set of security challenges. Our intention was to highlight that centralized 
approaches have some risks, such as single points of failure during DDoS/APT 
attacks, and data sovereignty concerns in regulated sectors. DIN aims to 
support scenarios where inference stays within a trusted perimeter. We agree 
that the security challenges of distribution need more attention with more 
detailed gap analysis and use cases concerning in the draft. 

2.We view the AI lifecycle broadly as comprising two major phases: training and 
inference. We consider agentic AI as an important category of workloads in 
inference phase. We agree it would be glad to incorporate on agentic AI use 
cases, requirements and gaps in the next revision.

3.This is a fair critique. The mention of physical-layer protection was meant 
to be comprehensive, but we agree it may distract from the IETF focus. We will 
consider modifying or removing that part in the next update.



We appreciate your valuable perspective and would be happy to incorporate 
improving the draft.



BR,

Jack





 ----邮件原文----发件人:Haoyu Song  <[email protected]>收件人:"宋健" 
<[email protected]>,rtgwg  <[email protected]>抄 送: (无)发送时间:2025-11-06 
09:43:16主题:RE: [rtgwg] Re: DIN for AI inference network, all kinds 
ofcontributions arewelcome
    

Hi Jack,


 


I’ve read the draft and here are a few questions.


 

I’m a little confused by the claim that “Enterprise and industrial AI inference 
scenarios present unique security and  compliance requirements that 
fundamentally conflict with centralized architectural approaches.”  On the 
contrary, I think the distributed architecture will introduce more challenges 
in this scenario. Perhaps you mean local inference vs. remote inference?

It’s not explicitly mentioned but how is this related to the agentic AI or are 
you only concerned with the model inference?  There are more things to consider 
in the former (and the more prevalent) case.

In 5.3, why “Cryptographic protection should extend to physical layer 
transmissions”, given there are already network  layer, transport layer, and 
message level security measures? What’s the physical layer security mean 
exactly?


 


 


Regards,


Haoyu


 



From: 宋健 <[email protected]> Sent: Wednesday, November 5, 2025 8:32 
AM To: rtgwg <[email protected]> Subject: [rtgwg] Re: DIN for AI inference 
network, all kinds of contributions arewelcome



 

add link 

https://datatracker.ietf.org/doc/draft-song-rtgwg-din-usecases-requirements/

 

 


----邮件原文---- 发件人:"宋健" <[email protected]> 收件人:rtgwg  <[email protected]> 
抄 送: (无) 发送时间:2025-11-05 23:32:06 主题:DIN for AI inference network, all kinds of 
contributions arewelcome

Hi guys,

 

AI is one of the most critical application today and will domains in the 
future. At the heart of AI lie two  key processes: AI training and AI 
inference. In the era of AI inference, the Internet should take on a key role.  
Based on this vision, we have submitted a draft to describe the Distributed 
Inference Network (DIN) Problem Statement, Use Cases, and Requirements 
[draft-song-rtgwg-din-usecases-requirements-00].

 


It is not enough to cover all related problems, use cases or requirements. Any 
comment and collaboration  are welcome.

 

Best regards,

 

Jian Song (Jack)

China Mobile




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