Hello. Brandon. How's the guy? Good, how are you? Sarah, how are you? Well enough to say hello and listen. So it's good to see you. All the new faces. Avatar's names on lists. We'll give it a few more minutes for people to join. While we are waiting, if you can update your attendance on the meeting, meeting notes. I just dropped a link in the chat for people's convenience. All right, just one more minute and we'll just get started. Thanks Brandon for signing up for Scribes. Anyone else wants to join? Who can do Scribes? Second Scribes. All right, so let's get started. We have a few things that we wanted to cover. We could cover today. Sarah, do you have any updates that you want to add to? No, I just wanted to. I've been on a bit of a leave of absence for about six months. So I wanted to just introduce myself to folks who are new and say hello to old folks. I'm one of the co-chairs of six securities. So Sarah Allen, ultra source on all the things. Good to see you all. Welcome back, Sarah. Welcome back. Yeah, she's instrumental in bootstrapping assessment. People that are new that didn't know. She's been instrumental in so many other things as well. So. So we have some updates that I'm seeing. If anyone else has any updates or anyone else wants to go, please add your updates on the attendance. Emily, you had some updates on. Security day. If you want to go. So, like I said last time, we are going to be doing security day again for North America this year virtual. We had a great discussion on the program committee did with the NCS. So the website is live. I will be posting it in the meeting notes and the call for CFP is also live. So if you have a security cognitive talk that you would like to give, go ahead and submit a CFP for it. It closes October 4th. So keep that in mind. This is a short crunch time to get everybody in and reviewed and get, make sure that you have time to do all your recordings. Yeah. Emily, can you post a link to the CFP to the, in the chat and the notes. It's in the notes, but I will make sure it's in both places. Okay. So, I read the map was helping us with the, who would strap the white paper with this, she was going to join and, but she was going to join through phone and she asked me to give an update about update, but when she joins, I think she'll probably introduce herself. I'll put that as a backlog for the updates. But for the agenda that we have today, Vinay has updates about white paper. And I'll give it to him. Thanks, Jay. So should I just get going on that item? Yeah. Share my screen. See the cloud native security white paper update. Yeah. Great. Yeah. You know, I think we talked about it last week. I think Jay and Emily thought it would be a good idea to provide an update to the team on the progress of the, the white paper. And that's the objective that we'll cover over the next few minutes. Once again, for those of you who don't know me, my name is Vinay Venkatragwan. So I thought it'd be a good idea to start with the, the goals for this paper. And it's been, I really liked it. And it's been stated in the paper, which is provide a framework for securing cloud native applications. So, you know, as we know that this is a very, very complex landscape that's evolving around, you know, cloud native, micro services, large scale, et cetera. But we as security think that it's a really good idea to provide our perspective and a framework for operators. And I'll get into the other audience aspect as well, but generally speaking operators as to what are the different dimensions that they should be thinking about when they're talking about security for cloud native applications. And hopefully we will achieve that using this white paper that will be obviously generically available. The audience that is being, and once again, please feel free to interject and provide comments and ask questions. As we go through this, the whole idea was to provide an overview, but you know, we can always expand it to take questions, answer, clarify any questions or comments. The audience for this paper was meant to be the, the CISOs, the CISOs, CTOs and architects. And to really, really give them a comprehensive as well as a bird's eye view into what are the different dimensions that they need to be thinking about.  And so how do you ensure that you're injecting security? And I think we're all very conscious of the fact that, you know, generally and typically speaking, security is, has always been an afterthought, but that's just not going to work for cloud natives. So the posture that we are providing is to say that, hey, you need to make sure that you're injecting security throughout this pipeline. And as you will see shortly, some of the cloud native layers that we have identified very, very broadly speaking is the develop, distribute, deploy and runtime. And in each of those different broadly speaking phases, if you will, from a cloud native application, development, deployment cycle, how security fits in. So the current status is, you know, for the last six to seven weeks, I forget exactly how long, maybe even eight weeks, there has been some fantastic collaboration going on around the paper. And also Emily has done a fabulous job of keeping everybody informed and on point in terms of soliciting inputs and then assigning stakeholders for various sections. And consequently, what has happened is there's been some phenomenal content that's been developed on the paper. We can even switch to the white paper very, very shortly and maybe we can look at the current status. But thanks, Emily, for doing a phenomenal job of providing constant feedback input, keeping us on track. And what has happened is there's been some incredible content around all the sections that I mentioned just now. So thank you for that. And if anybody would like to collaborate, I'm sure we're still taking input and the paper is available. And what has happened is it's gone from a raw version of the paper where we know, you know, the collaborators are providing their perspectives input to a more, it's starting to get a lot more formalized. So that's very, very exciting. And so there is a link to that as well. I don't know if we can share it or we can share the issue that's tracking all of these activities. And that's the next point that I wanted to mention. It's more from a broad collaborative document or more concrete document and framework. The target is to reconvene and review the paper in a couple of weeks. And so, you know, we still have a couple of weeks where we're trying to make sure that all of the different sections fit in and there is a natural logical progression between all of the different sections of the paper and to make sure that we are highlighting all the important dimensions and attributes for cloud native security. Then there's also been some good progress made on the security structural model. And maybe I can even share that once we're done. There's a link to that. But what we have seen there is, you know, to provide like a bird's eye view into the different phases, if you will, which has started to form into the develop, distribute, deploy, and the runtime phases. And then for each of those different phases, provide a double click into how does an operator think about security and what are the different opportunities to inject security, what are some of the best practices, et cetera. So we have some, and thanks to Jay and his team for a lot of creative work, the diagrams are starting to really, really flesh out very nicely. So there's a link in my PowerPoint for that. I will also share a link to this PowerPoint so that you can have access to those documents. And, you know, the next thing that I wanted to talk about so that just we level set and it's obviously opening it up to more conversation is some of the major sections in this paper that are starting to form are, you know, we're going to start with the goals and objectives and, you know, the personas we identify and what the, our audience can get out of these papers. But once that's been done, the major sections are, you know, we go into a deeper dive into the cloud native layers, I talked about it. And I think even from an industry perspective, you know, everybody is converging on these four broad categories when you talk about the application life cycle, which is develop, distribute, deploy runtime. And as I said, each of these we go dive deeper into each of those sections to articulate what operators should be thinking about and how they can think about incorporating security into each of those layers. And then we also have another section called security assurance. And this is where we are starting to talk about threat modeling and how, what are the different attack vectors, etc. And what are the resources that are available from an open source perspective to help security practitioners as well as different personas think about the threats potentially to their environments. And then there is also a section on compliance. And then there was also some good discussion on the potential personas that came up in the last meeting, I believe. So there is also a section on personas and use cases. So I think when I looked at the paper, you know, the cloud native layers has a lot of great content, security assurance section is also starting to develop pretty well. But I think, you know, we could use with more eyes on the compliance and the personas and use cases, as well as a few other sections that I'll highlight shortly. So before I continue on to the next couple of slides that I have, I'll pause and maybe would love to see if there are any questions that we can answer at this point. I saw some, some comments in the chat, maybe. I guess it's market. Is there anything you feel like is gaps where we, you need some more help or is this on the home stretch and you're just letting us know? Definitely. I think it's the former, but also I think I, I mean, from my perspective, I think we're in the middle stretch, if you will, and getting closer to the home stretch. So I, from my perspective, I feel like I think the compliance section could definitely use a little bit more detail. And even the, and then the personas and use cases are also just to make sure that we are highlighting all, we're not missing any critical personas and the use cases thereof, but that's a good segue into where we need a little bit more content, specifically storage. And I know Emily mentioned that she has reached out to the six storage group for their perspective on storage security and its applicability into, you know, the, the container model and what, what are the threats, what are the aspects that need to be addressed from a storage standpoint. So we definitely could use with more content there. And also in general, I believe, you know, anybody, please feel free to review it and, and maybe comment about the intro and as well as the transitions thereof to the various sections. We could definitely use some help in those areas. Hi there, this is Adna. I wanted to comment on the compliance section. Are we thinking about integrating with the OSCAL continuous compliance framework? I mean, has anyone done any work in that space with NIST? We had a conference earlier this year where there was a lot of emphasis on building those probes into the code itself. So there's no external compliance validation required. It's continuous compliance and auditor can just get a dashboard or some metrics through which we can validate the compliance of any particular application. I'm just curious and I'm happy to help in that section as well as the use cases. Yeah, those are great points Adna. I mean, we can even jump to that section if needed on right now. But yeah, if you could please take a stab at that, that would be fantastic. Another area that we had discussed at that point is forensics and investigations. The whole detection piece, right? I see a lot of enterprises dumping a lot of data from cloud-native applications and platforms into slunk. But then the detection takes so long that the container may not even exist by the time you detect something. So there are multiple layers of detection required. So I think that's an area that we can really add value to the industry by enumerating that in the paper as well. Absolutely. And I think that is one section where we have a section in my mind that would come under the runtime section. And I think, and we should definitely call out, you know, for example, an example that comes to mind is an unsanctioned process, right? If some kind of malware exists and it starts executing, you know, how do you quickly and to your point, these are all very, very ephemeral. Unlike traditional security practices, your asset is no longer there, but to be able to find, detect and alert on that is very, very important. And I think we have done, we have some content there, but would love for it if you could please review it and supplement it. Radhina, while you're online, would you want to introduce yourself? I was going to have you introduce later, but know that you're talking. Okay, so thanks. I did introduce myself, I think a couple of weeks ago. Again, my name is Radhina Chital. I work for TIA organization and it's a financial services entity that I lead the cloud security program for. I've done a lot of work in banking and finance. I've worked closely with NIST and cloud security Alliance on a number of initiatives related to cloud and container security. Thanks, Anand. Thank you very much. Yeah. Yeah, actually a side note here. I know a couple of people also working in Oskar. I think they'd be really interested to have a chat. So if you don't mind, I'll reach out to you after the call. That sounds like a plan. Yeah. I'm happy to do that. Awesome. Thank you. I have a question, Vinay. How does the contribution process looks like? Is the document just a word editable? Or is there like a public request process? So how do you want it to work? Yeah, I'm going to defer to Emily or Jay to take that one. So the folks that are part of the original working group have direct edit access. Everybody else is role viewable, which should give you suggestion options to go ahead and suggest content changes. If you find that it's not working for you, reach out to me or one of the other folks in the working group and we'll make sure that we get your comments included or work on fixing the permissions for you. Thanks. Cool. Thank you for the fall. The questions that those were great. Hopefully it helped everyone else on the call as well. So now I just want to maybe conclude with, with the timeline so that we are tracking for when we want to hopefully converge and have the final draft ready. So we're in this period between September 2nd and September 23rd, where we're still going through this collaborative review, but we're still going through this. We're still going through this. You know, showing up certain sections that we need and making the appropriate changes. Once that's done, I believe we want to spend some time. Talking about and going over the narrative voice review as well as we are ensuring that we are aligning with the goals of the paper. And then between October 7th and 14th that week, we'll do a final group review. And then we're going to be able to see how we can present this to the broader C and CF leadership team. I don't know if that's the TOC for final adjudication and would obviously depend on their schedules, but I believe we would like to have this paper done by KubeCon. Is that right? Yeah. That's the goal. Yeah. And so that's that. And then maybe I don't know why do we want to do that. I was also thinking maybe JJ and Emily, does it make sense for us to also maybe just brainstorm in terms of, you know, we talked about, there was so much of, there's fantastic content in my opinion. But we also thought that maybe there is a possibility for breaking this up into, you know, secondary papers, if you will, or deep dives from a certain practice perspective. Does it make sense for us to talk about that ? Does that make sense? Or should we defer it to a later point? I think it's worth talking about. JJ. Yeah. No, it's worth talking about. I think the group will end up deciding in terms of like, how to break the content down and into smaller chunks. I did have an initial chat with Brandon about that as well in terms of trying to take a lead on that effort to figure out how to break that content and then help through with that. But happy to brainstorm about it and take inputs and see how we can make that better. But it absolutely needs to be broken down with the amount of content that we already have. Right. Awesome. Yeah. No, go ahead, Sarah. Oh, I was kind of having a side conversation with Brendan earlier this week. We were brainstorming. One of the ideas we came up with was like having somebody who's in the target audience review the paper, like maybe at this narrative voice alignment to goal section. And I'm wondering whether people, like we know people in our network who are in that target audience, so we could be say, Hey, what's a white paper you've really loved? How long do you expect to spend reading away? So we can get a sense of what's the target, like, is there a target length? Or is it just as long as it's readable and some of it is in appendixes, that's fine. You know, so we might look out at for similar works. You know, or look at the other white papers for the six, like it'd be great to find like well loved, similar work that our target audience identifies or just say, Hey, could you take a look at this? And, you know, maybe when it's further along and give us some feedback too long, too short, missing things. Wish it said that. Yeah. So the white paper for anybody that hasn't looked at it yet is last time I checked and I might be wrong, I could have gotten longer was 24 pages long. And that is with a lot of really excellent and fantastic content by all of the contributors. And we don't want to lose any of that content, but we understand that 24 pages is very long. And depending on whether or not our target audience is comfortable sitting for that long period of time, they may not be. We want to make sure that we're breaking it down into an appropriate amount of readability for them. So those are all really good points. I've already reached out to Amy to help and to talk to Cheryl to help us identify who those folks would be that would be ideal to help participate in that final group review for us to make that determination of whether or not it's just right, or if it's too much. That way we can, we can continue to move forward. I could even ask the CISO at my organization if he would be willing to possibly review if that helps. Yeah, the more reviewer, the better. Yeah, from a CISO kind of persona. And I was also thinking maybe does it make sense to actually have an executive summary version? And I know this is maybe part of that whole breakup, but does it make sense to have an executive summary kind of a kind of a version of this, which is maybe two pages, which gives the, you know, which captures the essence and the content for the, you know, the CISO and the CTIO kind of an audience. I would say yes to that. Sort of the template for that is the way NIST does it. There's usually a couple of page summary. It's longer than an abstract, but shorter than the introduction section of the paper itself. So, similarly, Forrester, we're a regular consumer of that content. Forrester does the same, but NIST, usually it's two or three pages at the most. Yeah, I also think that there's, there's sort of like two formats for distribution, right? There's, I've heard people, you know, talk to me, people have approached me when we first started the white paper initiative quite some time ago and said, well, they really want a PDF. They can read on a plane. Right. So that speaks to having long format. It's all together. And then there's, you want the website, which you can browse through quickly to know, do you want to read the long thing? And, and there may be some intermediaries. So I think as we get towards the, we have it in its, you know, long form shape, then the CNCF has resource, like people who can help with, how do we get it out there? And, you know, do edit passes on that.  And I think it sounds like Emily's already started to talk to Amy about that. We have good support. Once we get the content, you know, wrangled and the first set of reviews where we've got all the sections plugged in, I think. I mean, it does seem to have quite a lot of very specific detail, like you should action this thing that don't, but I think maybe it could be taken out into an appendix or something that it's like, rather than principle, because it's going to make some principles and actual concrete recommendations for specific situations. And it kind of reads a bit. It's just differently detailed in different places as well. So I think maybe taking some of the specific recommendations out into an appendix or supporting document. Or I mean, in some cases, there are better external recommendations for specific things for what to do on Kubernetes, for example, as there's maybe material we could link to rather than putting it here and just having more general principles of this is why you need to do the X. Yeah, this is fine. I mean, that's a great idea, Justin. So, you know, as I was just kind of going over this quickly, there are some content in there that, you know, going back to, you know, who the audience is, I think the great content, I think it's going to, some of it's going to potentially either be going above some individual sets or they are going to ask that question of, well, why am I, what is this recommendation and why is it that I've maybe seen something different from a more authoritative source, right, whether whoever, whether it's from a vendor or some sort of technology outlet. So I think having taken that bit and pieces out and linking it to a more of an authoritative source for some of those recommendations might be a great recommendation. I think one of the things that Quentin Poole, who was on the TSC and we sort of first started talking about doing a white paper, I wanted to share his perspective, which I thought was quite good, which was we don't have to, as a group, come to an agreement on everything, like if there are things where there are different practices in the field, you know, maybe different resources that offer different ways of doing things, it's welcome to produce a section that says there are different practices in the field. Here are some references to look at our group, you know, we, you know, we think they're different per situation or this one is innovative and not well prodden, whereas this one is, you know, something that's been in practice in 20 years and maybe needs to be revised, right, like, or whatever, like whatever the group consensus is, even if that consensus is, we do different things. I think it wouldn't be a welcome thing because it helps people understand the space. They're not necessarily, in fact, you know, like, they're not necessarily looking for, this is how you do it, if there isn't a really established common practice. That's a good point because I think when one of the goals of this paper is to provide a framework for securing cloud-native applications, and maybe that's the, the what. And so this paper focuses on the what needs to be done and what needs to be thought about from a security practice. And then there is those, maybe those deep dive papers or the separate practice oriented, which is the house and they could be different versions and formats and we could maybe provide some references to, yeah, we're not being, there's no prescription, but there are many different ways of achieving those what's. Yeah, there is one thing I'd also like to remind this, there is a landscape that's in queue. Yeah, I was just going to mention that. Which I think covers like many of what we are talking about. Sorry, Brandon, I'll let you comment on that. Yeah, so I think, I think, when we were going through the, when I was going through the five paper, there were a lot of things that, a lot of good detail that I think that we will map on the landscape. And I think in the initial discussions, when we were looking at it, we were like the five paper and the landscape kind of would go together. So I think the idea was that the five people would kind of be kind of a document which focuses on concepts high level. And then it would link on to a specific part of the landscape. So at least for folks that one, like a really deep dive into the specific effects of it, they will kind of read the five paper and the landscape kind of side by side. I think that's what we were envisioning with that. Yeah. Also, could I suggest perhaps if we could add also a section called non goals in addition to the goals. For example, if this would be if it is specifically excluding edge type of services edge type of applications, for example, for the under the security umbrella or not, I think that might be helpful to the audiences. It's not clear to me whether it's we are including those or we're not. So there is a section for scope and assumptions and we do identify a few things that are considered out of scope. So if something isn't clear, let us know either by commenting on it or dropping a note and the channel. That way we can get it address. Yeah, I realized that there is a scope there, but I thought maybe if there is a separate heading, also is to if there is such a thing as that things that are not under the scope, maybe to point that out more firmly, that might be helpful to the audiences because, you know, rather than reading through all these things and then finding out, no, this is not quite applicable. Just a suggestion. Yeah, that's fair. We should definitely keep that in mind. Thank you. So that's, that's what I had from this update perspective. If there is any discussions around it. Please feel free to jump in. There is a channel. If you're, if you're not already part of the, if you haven't already joined the, we make sure them seeing the channel name. Right. So, the security white paper channel, a lot of discussions are happening there. If you feel like you want to participate in that discussion or you have things to add or just want to listen in. That'll be a good channel to join. So that's most of what we had in the agenda. Gardi is there. Gardi had contributed quite a bit to the white paper as well. So, a robot added one more thing to the. Next meeting. Okay. Mark also had an update on. Yeah, short thing. Those of you missed it last week we were talking about. Oh, the challenges of using GitHub. forward looking use of GitHub in enterprises where we're concerned about leakage, accidental leakage of credentials, intentional leakage of intellectual property happening in parallel to the need to disseminate, for example, e-commerce APIs, which you want to promote adoption for, and what kind of controls you can stand up around that. So we've had some internal, there's a PR about this, so happy to see more thoughts around this. And one of the facets of this that came up as we talked about it recently is the problem of enterprise metadata management, because if you try to, there's a temptation among the security people to jump in and say, well, we know what we need to protect, you know, this kind of data or this kind of code or this kind of API. And it's dangerously moving toward the SME landscape of either the applications or further behind the application lifecycle, the subject matter of what the app itself is supposed to do. And a way around that is, you know, in the model of the enterprise data management council developing taxonomy or just, you know, regulated metadata of some kind, because then the security tooling can inspect for metadata and assign policies based on access to certain kinds of resources that are in GitHub or that are internal that are going to be controlled through multiple tools. And we have multiple tools that are available to control access. And just for clarification purposes, original thinking was we'll block all uploads to it. So that was kind of the severe initial thought that was around this. So that's another facet of this. I don't think that's going to be possible in smaller enterprises. It's hard to do even if you set that as a goal. It tends to be a big company thing, like standing up a separate QA department or having somebody who's there to answer regulator questions 24 seven. But it is a facet of this that's we're talking about, because the ownership of metadata is one of the crosswalks between security teams and the domains that security is supposed to be protecting. So that's just another facet of this multifaceted problem around using GitHub and enterprises. All done. Thanks, Robert. I mean, thanks, Mark. Robert, do you have do you have a link to that PR that you talked about? Yeah, I think we had it in the meeting from last week, but I'll put it out here. Okay. That's in the meeting notes from last week. Robert, did Brandon say you had some some updates that you want to give? I don't see it I think that's for next week. That's for next week. I just want to point it out so that we can And next week, Emily, do you want to give a brief on what's happening next week? Yeah. So next week, we have originally planned to start an initial conversation about all of the issues that have been submitted regarding assessments and the assessment process, because we're coming up on a time where we really need to do a review of it and make some improvements to the process and seeing the amount of tickets that we have and everybody's great ideas. We want to make sure that we can do it collectively and have a good conversation around it. We are going to reach out to Justin tomorrow to confirm whether or not he's able to make tomorrow's meeting. So depending on his availability, that date might change a little bit. But the idea is that we want to start having the dialogue and then potentially move that into a working group because it's sounding like a pretty large effort. Yeah. I also just wanted to chime in about that. We intentionally did not address these issues until we had experienced doing a lot of security assessments and our goal was to complete five. I think we're not, we have at least, we have more than five in process. I'm not sure that we've actually completed them. And so for those new to the group or new in the last six months or so, this may seem like a lot of messy details unattended for a long time. It was uncomfortable to leave those things open, but we didn't want to make decisions on process changes until we had a little experience doing different ones. And it took us a while to get the process to be even a repeatable practice. So just to kind of explain why we've left this open for so long, but yes, I agree with Emily, it's high time. We did our project of the reflection and process improvements for security assessments. I'm very excited. We have gotten to this moment. Yeah. Yeah. A lot of work had gone in from Sarah and team to get an understanding, get the value for all these assessments and then get us to a stage where it's a tremendous value for a lot of people just doing self-assessment. And we've heard a lot of good comments from people in terms of going through this process and understanding their own stuff. And it actually helps the end users as well in terms of the consumption experience for the project. So people who've been involved in assessment, even peripherally, it'll be good to join next week to give your thoughts and inputs in terms of like how we can refine that or formalize the ones that we are doing what work well. Those all will be highly appreciated as we firm up the process on that. Good. So that's mostly what we had for the agenda today. Unless anyone else has anything to add, I think we can call it perhaps anything else Sarah that you want to bring up, nothing. Emily, Brandon, Justin. I'm good. All right. Yeah, I'll go for now. All right. Thanks you all. Thanks for the presentation and thanks Emily for the update on security. Thank you all.


Let's get back our $1,000 cards. This just came out of the package like this, so that's not awesome. Hopefully nothing was dinged. Maybe a little bit of a bend there. Might be a bend. Anything on the service actually looks pretty good, surprisingly. This is not real!  Dude! Near mint? Like if this was damage, that's one thing. Near mint! What in the world are we watching?


Hello. Hello. I'm Harsh Gupta. I work at Nelenso. Nelenso is India's first software cooperative. So I'm from an engineering background and many of my friends are also engineers and of many of these friends they believe that late, they believe in this God of data science, they believe that given enough data any problem is solvable. Of many of those people, they would happily replace lawyers, judges, doctors, pretty much everyone with machine learning. You might be aware of, there are already companies who are using machine learning to determine who should be higher or even who should be put in jail. In the U.S., there are courts which are using machine learning to determine criminal risk assessment and talk. So I'm quite skeptical of their claims and I think these things can backfire a lot. So let's talk, think about the process of machine learning. So the way you do it is you have some data, you put it into machine learning algorithm and you come up with some predictions or output. But the data comes from somewhere and the predictions are used for something and not taking into account how the data was generated or how the process or how your predictions are being used can be fatal. It can have like a lot of hard consequences for society and innocent people. Let's talk about data. So here's an example from World War II. You see a picture of fighter aircraft from World War II.  So the task of Abraim Wald and his team was to determine where to put more shields on the aircraft and they could not put more shields everywhere because that might make aircraft too heavy. There are also cost constraints, right? So they had the data of where there were more damage in aircraft, where there was more damage in the aircraft and their team had this opinion that places which are more damaged are more likely to be hit by the enemy. And I guess a naive machine learning algorithm will also have the same consequence. But Abraim Wald was smart. He realized that not all planes come back and these planes are able to come back even after being damaged. So the places where we see no damage are the places where aircraft were hit and those aircraft didn't return. So Abraim Wald decided to put more shields on the places which are less damaged. What do we learn from this? We learn that the context of the data is really important. And taking the context into account, the same data, you reach different conclusions, even opposite conclusions. Let's take another example. This is from the US presidential election of 1936. The candidates were Franklin D. Roosevelt and Al Flandon. Literary digest was in the business of conducting exit polls. That is to predict who will be the winner of the election even before the election happened. They had a sample size of 2.4 million. There's another person called George Gallup. For a sample size of just 50,000. Literary digest predicted that Roosevelt will win and George Gallup predicted... Sorry, literary digest predicted Landen will win and George Gallup predicted Rosa will win. And Gallup also predicted that literary digest prediction will be wrong. Who do you think was right? Well, Gallup. Even with a 50 times smaller sample. And why was that? It was because literary digest ignored how their data was generated. The way they generated their data was that they were a very popular magazine and they sent out exit poll sample forms with their magazine. People filled out the form and sent them back. At that time, more Republicans were likely to subscribe to their magazines than Democrats. So the sample over represented Republicans instead of Democrats. And even with a way bigger sample, they produced wrong results. Another thing to note here is the wrong bigger sample can be worse because it might lead you to make wrong predictions with more accuracy or more confidence. So data, getting right data can be really hard. And if you talk to any statistician, they will tell you that getting like how hard it is to get the right data. But let's assume somehow you have got the right data. But you are very careful with it. You can do all the complexities of the process and what kind of things which can affect your data quality. But even after you've done that, you are using the data to model some inherent system. And that system can be really, really complex. Okay, so here are some data points. You see a pattern. Do you? So I see two lines diverging. There are some more data points on the same underlying function. There are some more. There are some more. There are some even more. Do you see a pattern now? I see a sine curve. And that was, and the data points were from the same sine curve. But from the amount of data you had, you could not have predicted that it was a sine curve. So the amount of data you need corresponds to the complexity of the problem you are trying to deal with. What does it mean in real life? Well, there are problems for which you will never have enough data. Why do you think cryptographers aren't worried by development and deep learning? Because you'll never have enough data. Solve an ace algorithm of a key size of 256 bit. You'll probably need data of order of 2 to the power of 128. And I guess like the whole humanity doesn't have the processing power to process that amount of data. So you'll never have enough data for this kind of problem. What happens when you don't have enough data corresponding to the problem you are dealing with? So the complexity of the problem increases with the number of variables you have in the process, and also on the interactions they have. Now, when you don't have enough data corresponding to your problem, you'll find patterns which don't exist. So here is a graph of number of variables for a fixed amount of data and number of false patterns you can find. And you see the number of false patterns you can find increases with the number of variables you have. So how does it translate into real life? So I like food and I guess a lot of you also do. My friend Jyoti will tell me that not eating non-veg is unhealthy. But my mom will tell otherwise. A keto appears to be the fat diet at this time, but like 5 years later, 10 years later, there was some other diet which was the most popular diet and they would say scientifically proven that this diet is the best diet. I'm sure 5 years later there will be some other diet which will claim that the current diet is not the right diet. Why does it happen? Because there are so many factors which goes into how your body reacts to the diet you have is that it's very hard to get the right amount of data. So you just you need to take the right things in the right quantity in the right form. For example, taking vitamin pills is not the same as taking vitamins through food sources. It also depends on like what geography you live in, what your body type is, what your genetics are. And you play and all of these diets essentially takes only certain things or certain variables of the equation and claim that they have got the right thing. But when you take only certain things in isolation in complex systems, you'll always you might think that you have got the right thing, but you probably haven't. And diet is not the only complex system. Social systems are complex, finance is complex, genetics is a complex system and in complex systems you cannot take certain variables in isolation. So complex systems can fool you. So let's talk about the output. So like you are using this machine learning to predict some output and predicts and you are like essentially making some like complex matrix, complex things into reducing the complex things into simplistic metrics. For example, health is a very complex, complex phenomena and you often reduce it to BMI. You reduce health of economy to GDP or like knowledge of someone to the grades. And when you like reduce very complex goals to simplistic metrics, these metrics can backfire. Why? Because people respond like they are not rocks. So they respond in the ways you try to judge them on. So there is this law called Godhard's law which says when a measure becomes a target, it sees this to be a good metric. And their history has lots and lots of examples for that. So in 18th century France and England, the government wanted to tax people on their wealth. But it's very hard to determine how much wealth someone has. So some smart person in the government realized that the wealth someone has is probably proportional to the size of their house. And the size of their house is probably proportional to the number of doors and windows they have. So they started counting the numbers of doors and windows someone houses have. And they started taxing people on that. Well, what happened in result? People started having these weird buildings where they would like remove the doors and windows.  And these have like a lot of repercussions in terms of the like amount of exposure to light people have, the health and ventilation and all. And there are like lots of examples like that. So when we believe we will be judged by silicate areas, we adopt in silly ways. So to recap, you need to take into account how your data was generated. Because everything which, your data is the core of the thing. And if your data is flawed and you are not thinking through it, everything which follows might fall apart. Even if your data is right, the inherent system can be very complex and these complex systems can fool you. You also need to be very careful about the metrics you use because they can backfire. So some questions which I think you might ask. Well, do I mean machine learning does not work at all? Well, I do not mean so. But I think like there are so many things which can go wrong that it probably doesn't relate unless you are super careful. And good fit on a test set does not prove anything. Maybe like rigorous field testing does, but yeah, like you cannot just take static data set and say come up with some predictions. They might not make any sense. Why doesn't good fit on test set prove anything? Well, the reason is the way you do testing is usually in machine learning, you have static fixed data set and you divide the data set in training set. But the test set is supposed to be used only once. But what if your algorithm doesn't perform well on that? You change some hyper parameter, train the algorithm again and test it on test set. So if you do that enough number of times, you might end up overfitting on a test set itself. Also, like if your data is bad, a good fit on a bad data does not mean anything. What about machine learning at Google, Facebook, Microsoft and Netflix? Well, the same thing. If it works for you, doesn't mean it will work for you. If it works for them, doesn't mean it will work for you. Also, I guess there's like this survival bias in the machine learning stories we see. So I guess there will be a lot of companies which use machine learning and they failed and we won't hear their stories because well, media has no incentive to popularize them. Even they don't have any incentive to popularize them. Machine learning can work and they have worked in certain scenarios, but you should think about your case. But doesn't it hurt to try? Well, it depends who is hurt, right?   No one else is hurt. But if you are like, say, putting people on jail based on machine learning, you should be like, I'll be very scared because you are putting innocent people's life in danger. All models are wrong, but some are harmful. Some further things like, I really love this guy, Nassim Nicholas Salih. I would recommend you read everything by him. There's this book called Weapons of Math Destruction by Katharine O'Neill. And she talks about like a lot of ways where people have tried to use mathematical models including machine learning in scenarios and have done a lot of damage to people. And there's some other things which you can read. Okay, thank you. Questions? Raise your hands where I can see them. Where? I see that whatever you are telling is like the data should be generated in the right way. And we're also telling that the complicated systems may not work sometimes with machine learning. No, I'm saying that like, you won't know if it is working or not. You'll think it is working, it's probably isn't. Okay, but from the context of this talk, right, like what cannot be done through machine learning, you're telling that it's kind of risky to like be there or like machine learning may not work with many things. But is it not, will it not post challenges that okay, we have to resolve them? So, I hope my question is clear. So like, okay, so to paraphrase your question, like the question is, don't we see these things as challenges to resolve instead of things which we cannot do? So you see the cryptography example, right? So that system, you don't have enough data at all. And you probably have never enough data. They are so, for very complex, so this is a theorem called no freelance theorem, which is like as if you assume anything is possible in the underlying system you are trying to build, like you cannot have any predictor power at all. I guess that also applies to complex systems because the complex systems are so complex and the underlying functions can be so varied that you might not have enough data. So like I think of this in this way that like the problem is you might think that things are working, but like you have no proof that they are. It's unless like at least from just from the data itself. Like so and you can you can do field testing and go there and like actually see if things are working. That's different. But like from the data you can always find patterns which are probably not, which are there and not true. Does that resolve your question somewhere?


The topic for this lecture is Ludwig's angina. Ludwig's angina is basically a type of severe cellulitis involving the flow of the mouth. It is a name given to a massive firm brony cellulitis or induration and acute toxic stage involving simultaneously the submandibular, sublingual and submental spaces bilaterally. So this is the definition of Ludwig's angina. This condition was first described by Willem Frederick von Ludwig in the year 1836. So as the definition suggests, only the bilateral involvement of the above mentioned spaces is considered to be a classical disease of Ludwig's angina. All other types of presentations though massive are not considered as Ludwig's angina. The word angina is derived from the Latin word angere meaning suffocation or choking sensation. And the word Ludwig comes from the person to whom the credit goes for its description. This condition has established its unique identity with three Fs. As it was to be feared, it rarely becomes fluctuating and it was often fatal. Many other terminologies were also used for this condition which include Marbelle's triangulatories. It is also called angina maligna and gadotilo. There are various causes attributed to Ludwig's angina. For example, Audentogenic Infection. These infections in the form of acute dental alveolar abscess, acute pedodontal abscess or acute pericoronal abscess. All these can lead to Ludwig's angina. As already told in the lecture on tergomandibular space infection, pericoronal abscess in relation to erupting third molars can extend into sublingual, submandibular and tergomandibular spaces. Thus resulting in Ludwig's angina. Use of a contaminated needle for giving local anesthesia is an iatrogenic cause. Traumatic injuries to the orophacial region like a compound and communicated mandibular fractures or a deep penetrating wound can also lead to this condition. Ostromylitis secondary to compound mandibular fractures or an acute exacerbation of chronic ostromylitis of mandible may develop into Ludwig's angina. Acute or chronic infections of salivary glands. For example, sublingual and submandibular phyladenitis are also causes of this condition. The malignancies of oral region. It can give rise to secondary infection thus leading to this condition. The miscellaneous causes of Ludwig's angina include infections in the tonsils or pharyngeal area, foreign bodies such as fish bone or oral soft tissue lacerations. So this is regarding the etiology of Ludwig's angina. Here you need to understand that the condition of Ludwig's angina is a cellulitis. Which is a diffuse inflammation of soft tissues which is not circumscribed or confined to one area. But in contrast to the abscess, it tends to spread through tissue spaces and along facial planes. Such type of spreading infection occurs in the presence of organisms that produce significant amounts of hyaluronidase and fibrino license. Which add to breakdown or dissolve the hyaluronic acid and fibrin. A plethora of microorganisms has been implicated as the causative agents of Ludwig's angina. Few of the microorganisms which were able to be isolated from the culture specimen include Staphylococci, Streptococci, E. coli, Bacteroids, anaerobic Streptococci, Peptostreptococcus and Fusospyrotids. The condition of Ludwig's angina usually follows a submandibular space infection caused by a pediapycle infection or pedicoronitis around the mandibular second and third molar. The apices of these teeth are usually placed below the myelo-hyoid line of myelo-hyoid muscle. So that the infection tends to spread primarily to the submandibular spaces. The infection then spread to the sublingual space around the submandibular gland, which is present posterior to the space. The submental space is also involved by lymphatic spread. This condition can also occur in converse manner, that is by spread from the sublingual space to the submandibular space. The signs and symptoms of Ludwig's angina are present with varying degree of severity. Upon general examination, the patient looks toxic, very ill and dehydrated. There is pyrexia, anorexia, chills and malaise. There is dysphagia or difficulty in swallowing. The speech is impaired and there is hoarseness of voice. Upon external examination, there is a firm, hard brownie swelling in the bilateral submandibular and submandible regions, which may extend down to the anterior neck to the clavicle. There is severe muscle spasm, which may lead to Christmas or restricted jaw movements. So here, typically the mouth remains open due to edema of the sublingual tissues, leading to raised tongue, which almost touches the palatal vault. There is airway obstruction and the respiratory rate may seem to be raised, breathing being shallow with accessory muscles of respiration being used. Sinosis may occur due to the progdaseive hypoxia. Ultimately, fatal death may occur in untreated case of Ludwigs angina within 10 to 24 hours due to asphyxia. Intraordinary swelling develops rapidly, which involves the sublingual tissues and raises the flow of mouth. The tongue may be raised against the palate. There is increased salivation, stiffness of tongue movements and difficulty in swallowing. A backwards spread of infection leads to edema of glottis, thus resulting in respiratory obstruction. Strider is a harsh vibrating noise when breathing, which is caused by obstruction of trachea or larynx. Strider is an alarming sign of Ludwigs angina, which needs emergency intervention to keep the airway patent. Some of the sublingual spaces, the infection may spread backwards towards the region of epiglottis. This produces swelling around the laryngeal inlet. Due to anatomical continuity of various spaces with submalubular space, the infection may spread posteriorly to the mastectric spaces and the pedopharyngeal spaces, thus worsening the airway compromise. Infection from the submalubular region can also spread downwards beneath the investing layer of deep cervical fascia towards the clavicle and towards the mediastinum, thus causing mediastinitis. Less commonly, infection can spread below towards the catarotid sheet also, which leads to cavernous sinus thrombosis with subsequent meningitis. Ludwigs angina, if untreated, can be fatal within 12-24 hours, death arising from asphyxia. The cases may become more complicated with involvement of other spaces. Therefore, the condition should be taken as a life-threatening emergency situation and is best treated by aggressive intervention. The treatment is based on the combination of the following factors. Early diagnosis, maintenance of patent airway, intense and prolonged antibiotic therapy, extraction of offending teeth and surgical drainage or decompression of facial spaces. Surgical drainage or decompression is performed under general anesthesia given to the patient by a skilled anesthesiologist. An awake endotracheal intubation is performed using fibroptic laryngeoscope and nasopharyngeal airway is maintained. In case of Ludwigs angina, the death can occur from asphyxia rather than the infection itself. Hence, it is advisable to observe the patient for respiratory obstruction and restlessness. Blind intubation should be always avoided. Naso-endocrine intubation is far more reliable and it is almost predictable. Therefore, it is preferred. Surgical airway may be required in case of severe upper respiratory obstruction. Laryngeotomy or cricothyroidotomy is preferred over tracheostomy considering the complications associated with tracheostomy. The surgical intervention has two main aims to remove the cause and surgical decompression. The advantages of early surgical decompression include the following. It reduces the pressure of edematous tissue on the airway, thus reducing respiratory obstruction. It allows prompt drainage if any separation develops. It also allows obtaining specimens or samples for staining and culture and sensitivity. It allows placement of drains which may be useful to drain the pus collection and also it helps in irrigation of the tissues at regular intervals. Antibiotics play a vital role in managing Ludwigs angina. Usually, IV antibiotics with proper dosage and frequency are necessary. Pencilins are the first line of antibiotics in treating Ludwigs angina as it is a broad spectrum antibiotic. Semi-synthetic derivatives of penicillin like ampicillin or amoxicillin may be also used. In case of allergy to penicillin, the patient is administered erythromycin. Antibiotics should be changed subsequent to the result of bacterial culture and sensitivity testing. The therapy should also be changed if the favorable vessels are not observed after 48 to 72 hours of therapy. Most cases of Ludwigs angina are dehydrated. This is because of two reasons. Diminished liquid intake due to pain and discomfort associated with dysphagia. Due to the toxic nature of the condition, there is usually excessive urination and perspiration, which further diminishes body fluids. For these reasons, patients should be encouraged to have liquids frequently and if required, intravenously fluids can maintain hydration and also calories. A simple protocol for the management of Ludwigs angina include a pre-peri and post-operative protocol. Pre-operative airway assessment includes observing the signs and symptoms of obstruction. Thoroughly examine the mouth opening, the tongue mobility, the region of uvula and fascial pillars. Confirm the etiological findings using an orthopantamogram or a CT scan of head and neck. Consider the risk factors like diabetes and immunodeficiency. Check if the patient is well hydrated by monitoring the BP, pulse and urine output. Check the temperature. Assess the blood and urine cultures for septicemia. Apply cold packs and use antibiotics as required. Obtain chest radiographs to rule out pneumonia. Evaluate the lab data for blood count and protein levels. And also consider emergency airway preparations. The pre-operative protocol includes incubation, removal of the cause and surgical drainage and appropriate antibiotics. The post-operative considerations include extubation after confirming an adequate airway, periodic irrigation of the drains, obtaining culture reports to adjust the antibiotics accordingly, evaluation of lab reports, monitoring the colds of infection and re-exploration and drainage if necessary. And finally, following up the patient regularly. Ludwigsangina continues to be a cause of significant morbidity and mortalities. The various complications associated with Ludwigsangina are osteomyelitis, maxillary sinusitis, localized respiratory tract disturbances as well as digestive tract disturbances. Even more serious complications include life-threatening airway obstruction, septicemia, distant metastatic focus, mediastionitis, pedicarditis, internal jugular vein thrombosis, several neurological complications, involvement of carotid shape and ultimately death. Talking a little more about mediastionitis. The mediastionum is the sexual portion of the thorax. It is bounded bilaterally by the pleura, lengthally by the sternum, dorsally by the vertebrae and inferiorly by the diaphragm. Vital structures located within the mediastionum include the heart, the aortic arch, the lungs, carotid and subclavian arteries, the thoracic duct, vagus nerve, the trachea and esophages. Therefore, autotogenic infection spreading to the mediastionum is a life-threatening complication. And it should be suspected in the patient who exhibits exacerbation of fever associated with substernal pain. Therefore, mediastionitis is an infection involving the connective mediastional tissue that fills the interplural space and surrounds the median thoracic organs. The chest x-ray will show typical widening of the mediastionum. There is also pleural effusion and obliteration of sternal spaces. Treatment includes early recognition, airway management and aggressive surgical intervention. Appropriate antibiotic therapy should be followed along with supportive systemic care and hyperbaric oxygen therapy whenever indicated. Hence, to conclude, Ludwig Sangeina is a life-threatening complication associated with orophacial infections, which can become fatal if not timely diagnosed and intervened. That's all for this lecture. Thank you.


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