Dinis Cruz - 00:02 Hi. Welcome to the Open Security Summit session in April 2023. This is a very topical topic because it’s about Chatgpt impact on cyber and application security. We have a great set of panels. Dennis. I run the open security summit. I’m the Cecil of Honor Barrett and the chief scientist of Glasswall. Sam, we can do by you.
Sam Stepanyan - 00:25 Yeah, I’m Sam Stepanyan. By day. I’m an independent application security consultant, but also volunteer as OAS planted chapter leader for the past seven years. I also a project co leader on two of OAS projects, OAS Network and Oasp WAF evaluation criteria. I made numerous contributions to OAS, including Oasp Zap and Oasp Top Ten. So that’s me.
Dinis Cruz - 00:50 Maybe. Nathan.
Nathan Case - 00:52 Hi, I’m Nathan Case. I am a security advocate for datadog. I spend most of my day talking about security geeky stuff. This is about as geeky as it gets right now. I’m really excited to have this talk. I am decidedly far more on the security side than the AI side, so I’m excited to see where this goes. Obviously, views I express are my own, not data dogs.
Dinis Cruz - 01:12 Exactly. Scott?
Scott Katie - 01:15 Scott katie. Longtime application security practitioner, enterprise architect. Just starting the journey into, like, AI type activities, but been following kind of Gary McGraw with machine learning. Been part of OS for quite a while. That’s an interesting topic, taking Open and pulling it into Chat GPT to kind of help provide more information to people and how you validate that.
Sam Stepanyan - 01:45 Right.
Scott Katie - 01:47 So I’m real interested in the topic. Happy to contribute. Have to drop it half past.
Dinis Cruz - 01:54 That’S. All right. I think what we could do is let’s stay away from the whole privacy feeding data to the models thing, because let’s kind of assume that these versions and the next versions, for example, are the models will have solved that people will be able to create models on top of private data. People will create models on top of things that let’s not look at even again, some of the poisoning of the models, because I do feel that there’s an area here which is the people who control the models can actually do a lot of stuff, including to identify malicious access, et cetera. And we know part of that. Right. Why don’t we start with, in a way, what is the big difference with, I would say, the GPT, the Chat GPT like technologies and the AIS that came before. Right. I’ll add that for me, it was context.
Dinis Cruz - 02:52 It was the ability to have a dialogue, was the ability to express what I want instead of reverse engineering what I want into a Google search and then go and hunt for articles that kind of have what I want and then find almost like 80% of what I want and then start to start again. Right. So, for me, it was that Iron Man Jarvis kind of dialogue that I never really had before with anything that was AI specific. What about you guys say?
Scott Katie - 03:26 Yeah, it was getting back, asking a question, getting a single answer composed from multiple search results instead of a list of search results that then you had to dig into, because then you dug into them and you went on long trails, so you didn’t get the, I would say, context and conciseness.
Nathan Case - 03:44 Well, and I would go so far as to posit. This is one of the first times I’m going to go back and give you a memory that I have of five years ago, speaking to a security vendor that was telling me about his wonderful AI product. We talked about it and the more discussion we got into it, I was like, well, you’re just doing this huge if then else a thing that’s not really like machine learning or AI. That’s just a really big if then statement, which cool, but not really what we’re talking about here. I think this is the first time we’ve actually been able to say it’s contextual, it understands, it’s able to search on general words and sentiment that you give it and then give you back. Reasonable results, though how reasonable becomes a question of how well it’s been trained.
Sam Stepanyan - 04:32 Yeah, and from my side, I think I was mostly impressed, of course, with the fact that it understands code, it writes code, and of course, one of the topics we’re going to discuss today, it actually remediates the vulnerabilities in the code. This was my big revelation, kind of exactly with a pinch of salt. The fact is that, again, because we created a lot of content over the past 20 years and a lot of people complain it’s quite difficult to navigate this content. If you just go to a search engine like Google and you ask about vulnerability, you’re going to get thousands, if not hundreds of thousands of responses. Depending on what you click and you read those pages, you will still not understand what’s going on. I think that’s the ability to get this summary. There’s even a way to ask Chat GPT to explain a vulnerability like you are a five year old or if you are an eight year old, which I think is quite unique.
Sam Stepanyan - 05:32 This is something what we didn’t have even though AI was in security for a long time. But, yeah, I think every single SaaS vendor which claimed that they have AI, I’ve never actually seen it successfully applied until now, that we’re seeing in Chat GPD, which I think is probably going to affect a lot of SaaS vendors well, because of the topics we’re going to discuss next.
Dinis Cruz - 05:55 Well, I think every single SaaS vendor should be on the work processing because their whole business could disappear, right. As soon as somebody integrates equivalent Chat TPT like models. Because if I can go to the point, in fact, I haven’t fully tested yet. The latest version of Copilots, I don’t know if you guys used it, but the latest version of Copilot actually claims to actually look at the whole code. Have you guys seen that one? GitHub copilot, right. The first version was which is already pretty spectacular, I have to say, right. It already was really good at creating really good recommendations. The new version apparently I haven’t tested because I don’t think they have fully released it actually claims to look at the whole code, including writing unit tests. With the whole coding context now from there to actually finding all class of security vulnerabilities and connect all sorts of stuff, that far, right?
Dinis Cruz - 06:55 It’s almost a darker heavy lifting to be able to do that, man.
Nathan Case - 06:59 It’s an interesting question. Can you ask your chat GPT to create you a framework by which you can complete your code quicker? I mean, dude, I can think of all of the things that I’ve had to code in my lifetime and the amount of code that I’ve written that was just, gee, does this work? To get past that section and go right to the, well, this is perfect. We have a hard enough time getting people to evaluate should I actually do this, that or the other, or evaluate variables as they go into code. It’s a little scary because there’s some level of discipline that you have to make sure that is actually enacted here.
Dinis Cruz - 07:34 Think of the questions you can ask, right? If I go back to my old two platform days, right, I still feel that I was doing stuff that today people still don’t do, right? The way I got crazy success was by imagine this on a SaaS engine, right? I got to the point where I would delete all the rules from the database. I had access to one of the engines, right? I would create a call graph of everything. I would taint every entry point and external point. I would get millions of traces, literally millions of traces. The beauty of that is that I will spend some time mapping the inputs and the outputs, and then I have this almost real time view where the data arrived and the ability I then have to really very quickly say, I care about that. I care about that. I don’t trust this and even to say, okay, so this XML file contains the glue between these web service and now web service there.
Dinis Cruz - 08:25 So you go like that. You go to the database and you come like that. So create those trees. That context, which is really what advanced static analysis can actually give you. That’s kind of how you really can do this. My problem was I couldn’t describe the model right now, I still am getting to the point where I could start to describe the behavior that I want for the Web services, the behavior that you want for all these things. And that’s a game changer.
Nathan Case - 08:51 I think it’s interesting to be able to say like this, well, if you.
Scott Katie - 08:56 Can create the pattern, then you can take a large code base and boil it down to what fits the pattern and what doesn’t, and then focus on what doesn’t, right? So you can apply that to MIT. You can apply it to testing security, testing even stuff like code, like checking in comments and having a consistent framework for that where people might not even put that in. If you could generate comments from code that’s written, hey, that’s a really big game changer, because every developer, that saves, like, a half hour of time, right? If you have a security researcher looking through that, instead of maybe I have to switch from one code to another and learn, I can just reach through the comments and find the pattern that doesn’t fit, that’s a really good use of it. It’ll save time.
Dinis Cruz - 09:45 What I really like is that it really now aligns security with engineering. I think there was a trend with engineering where we kind of threw with the baby with the bathwater by saying, you don’t need architecture diagrams. What you need is quick iteration. Documentation is not that important. Yeah, everybody wants it, but nobody seems to have the time to do it, right? To be able to start to describe the code, like, for example, a threat modeling session, 90% of the threat modeling is just getting a freaking decent architectural diagram. Once you have it, you find the threads left, right, and center, right? Imagine being able to be programmatically created. Imagine taking a code based and saying, hey, give me an architecture diagram of this. Describe me this code in, UML, give me a dot version of this code at this level of abstraction, right? That would be massive.
Nathan Case - 10:31 But that’s the issue, though, right? You have to trust the process by which it was created. That’s really what I’m getting at, is how do we evaluate that trust?
Dinis Cruz - 10:41 Because real world comes into play. I don’t have a problem with changing between mistakes because I catch them, right? Like, the dude f****** makes up s***, right? That’s fine. In a way, I just love the fact when he makes a mistake in the first answer and you correct and you go, oh, yeah, you’re right.
Nathan Case - 10:57 I think that’s the key point right there.
Dinis Cruz - 11:01 This is an existing vacuum. So, for example, even those recommendations, even that stuff, you have feedback loops, right? In fact, you can even have another engine mapping up the feedback loop, so you can even use the output of one as a prompt to the next one, right? We’re now operating at a much more efficient level. Before you had to find the people who understand the code to describe what the h*** that meant, right. Even, like, the legacy stuff, right? Most developers didn’t write the original code, so they don’t f****** understand it. Right. To be able to say, this is how we believe it works, then reality come into play. You can even say, Here are the logs, right? Can you match these logs with the architecture? Or Find me all the places where these logs that I know for real are being invoked? Match this architecture.
Dinis Cruz - 11:48 That was a question that before would be impossible on us to ask, right? Yeah. Now we’re not far off to Nathan.
Scott Katie - 11:56 Your point about creating a framework and then you go code, maybe take the code and put it in a framework.
Nathan Case - 12:02 You can evaluate, which is cool, which is just d*** awesome. Right. I feel like we’ve been talking over you, man.
Sam Stepanyan - 12:17 It’s just a couple of things to add. Basically, the ability to add comments to the code, I think what Dennis has mentioned, I think that’s revolutionary by itself. Obviously, if we just walk away from us being security people, right. It’s the amount of productivity that it’s going to add when you are trying to do reverse engineering and triaging of vulnerability in a legacy code is absolutely huge. Right. Because obviously we have two types of problems, right. In application security, we have problem number one, how to make sure that the software which is being built right now is secure, and how do we remediate vulnerabilities in the legacy software which was built years ago by someone else? So there’s two sides to the problem. The comment writing feature, I think it’s very good in the same way that it can actually generate documentation. Again, you can ask it to generate documentation as if it was read by a five year old, for example, or to a different audience.
Sam Stepanyan - 13:13 And the same thing with the vulnerabilities. What I found very interesting is that you can ask it because obviously, when it finds vulnerabilities, you can say, explain this vulnerabilities, like, I’m a CEO, I’m a non technical person, and it will still do it. I think evaluate, because we are all techies, right? We have a problem of speaking to the management, speaking to the board, and actually explaining what is the actual risk, trying to say, oh, there’s a deserialization issue, or the words like cross site scripting or server side request forgery, that just sounds like Chinese to any CEO and some CTOs as well. I think the fact that it’s a language model and it can actually utilize the language to make things simpler, I think is quite important.
Nathan Case - 13:57 You’ve touched on something that I want to get into here, if you don’t mind. You touched on reverse engineering, so there’s always the forensics or the reverse engineering of malware that I don’t know if I’ve seen anybody use chat GPT for. You have obfuscated code that someone obfuscated because they don’t want you to figure out how my malware is working. I think it would be really interesting to begin to look at, can we actually reverse engineer active malware with a Chat GPT like system that actually lets us figure out real quick what was the trigger and how do we fix it.
Sam Stepanyan - 15:07 Not just the WAFF rules. The most amazing thing is, again, going back to SAS, right? Mostly SAS people, it writes the SAS rules and it drives them because there’s now there’s a SEM grab and Code QL. And there’s an example. Obviously my waste on the chapter called Leader Sharifman. He actually spent, I think, a week in Chat GPT and he actually managed to write something very useful for his team. He shared actually I know, obviously were in a chat bone, but Dennis, would you be able to allow me to share my screen? I think I should be able to, yeah, you can, yeah, because I can share the GitHub repo where that’s created called Infosec open AI examples. Here you can see an example of CWE explanation which is written by Chat GPT and it is written for developer and it explains what the code is vulnerable and it also remediates the code.
Sam Stepanyan - 16:08 After it remediates the code, you can ask Open AI to write the Semgrap rule which will detect it and prevent it in the future. And then a code QL rule. I haven’t tried it, but we should be able to also write a mod security CRS rule for the WAF as well. Also to stop it on WAF if it is a vulnerability which can be stopped on the WAF. Here it is doing sanitization and input validation. So there you go. You can see these are all the examples. This is all available in this repo. There’s also Python code test python code that you can play with. I think this is absolutely fantastic and let me just switch back to the actual code.
Nathan Case - 17:00 I would love to play with that after this.
Dinis Cruz - 17:54 The built in check is then we can review that and we can make sure that it’s right, but also we can adjust it, right? We can say, okay, this is actually all well explained, or this thing here, and then it might not be perfect the first time around. It doesn’t need to be perfect, but think about it after 510, 20, 5000, those examples, the next one is going to be much better, right? Absolutely. Even more interestingly, then you can even say, okay, now write the Http request to trigger that exploit, right? You can even think about, okay, now deploy that rule in front of one and then verify that rule actually works. Right.
Nathan Case - 18:28 I think that’s the thing, though, and the reason I bring it up is because this is the FUD that people are going to try to throw at this. When you look at it, what we’re looking at is way better than anything we have today. I think it’s really hard to argue that, oh, an error here or there is going to really make this okay. Yeah, I get it. Mistakes are going to happen. We have the way to correct, move forward.
Sam Stepanyan - 18:53 That’s why we’re humans, right? It still requires humans to review and confirm. Dennis, as you said, it still sometimes doesn’t get it right on the first time, but you can ask it to correct itself and it will do.
Nathan Case - 19:06 At the moment, get it right the first time at a college.
Dinis Cruz - 19:11 Exactly.
Sam Stepanyan - 19:13 This is what someone saying, that Chat GPD is really like an intern, right? Basically it’s an intern who you can ask and go and do some research and do some googling and bring the information back together as a project. So that’s essentially what it does. The thing that we’re missing, this is a very early stage, right? Because Chanjibiti really entered our lives in the past four or five months, right, and it’s very early and I’m looking forward to evolution. It’s going to get significantly better very soon. Organizations who are already utilizing the power of it, I think they’re understanding how much further they can take it. Obviously it’s particularly the learning bit, because if we can basically feedback the human learning. It was actually for our AppSec purpose, right. We ask you to find vulnerabilities in the code. And let’s say I had this issue.
Sam Stepanyan - 20:05 For example, there are five vulnerabilities in the code. Chat GPT found four out of five and explained this four out of five. I asked myself, hang on a minute, but there is another deserialization on line 55 of this file and it said, oh yes, I apologize, I missed that. There is a deserialization and here is how you should be remediating this. And it gave me the example. The thing is it didn’t actually use the fact that I corrected it to learn and enhance its own knowledge base. It doesn’t make the same mistake next time, but I think it’s coming right. That’s the next phase.
Scott Katie - 20:40 Security is trust the verify, right?
Dinis Cruz - 20:43 Yeah.
Scott Katie - 20:44 Learning is you learn when you make mistakes. Because if you do it right the first time and you don’t know, you.
Nathan Case - 20:51 Don’T know.
Scott Katie - 20:57 That’S at least the way I’m approaching Chat AI is if I can trust and then verify something, then I could probably automate it. I know I have some known good, but you have to iterate that I think.
Nathan Case - 21:14 We’Re moving to a place here, too, where the number of known vulnerabilities, the number of known issues in code is reaching a we don’t have enough humans on the planet to deal with this point. As we look at something like this, we’re going to have to figure out how to automate our way out of the mess that we’re creating because there aren’t enough fingers left in the planet to hunt and peck for each of these vulnerabilities.
Scott Katie - 21:38 How do we correct this or time?
Dinis Cruz - 21:42 To be honest, that is something that I feel keep element here. I think for a long time my main worry was I couldn’t see how we could scale, right. The only thing that didn’t happen, I thought it would happen, I thought the attackers would get a lot more efficient and effective than they were. I still feel that we still have the pace where for the amount of vulnerabilities that exist and for the complexity of the exploits of the ones that do exist, you still assume that apart in some places, most attacks are not leveraging highly advanced things, right? Of course there’s a couple of places where they are, but I feel that this is the first time that I see a scale solution. Because in the past, most of our solutions I would go, this will never scale at large. If we really need to run this up, if we really need to start protecting all the NPM registries and all the dependencies that we’ve got and the dependencies this finally, I think gives us a good way to do that.
Dinis Cruz - 22:45 The scalability of security practices. I think the difference now, and it’s interesting because we are all of a generation, right? The difference now is that are we going to be able to adapt, right? Or is the new generation going to completely stream all this where for them. GBT is what Google was for us. Also the difference is the time scales are much shorter. Right. Google took ten years, 15 years, right. To really gain this thing, where this is going to happen in a couple of years.
Nathan Case - 23:18 I think it’s more like a couple.
Dinis Cruz - 23:19 The question will be, how do you protect against the AI agent itself? That’s the next evolution, right, of this. I think in going back to the topic of this, if we can now start to leverage these technologies, we can actually make a huge amount of the security industry a lot more relevant because as you guys just mentioned, that these might make mistakes. Come on, our industry is a crazy offender of producing bad things, bad data, bad information, not connecting, not talking to each other all that, right?
Nathan Case - 23:52 Well, just thinking about I mean, like, we had talked about mist before we got started. It’s not something that’s horribly used in the States, at least to my knowledge right now. At the same time, you look at some of the things that you could do with, hey, I’m going to go ahead and shove a whole bunch of information into this model and come up with potential issues. That’s insane.
Dinis Cruz - 24:18 Yeah, we in a nice place where I think we have a lot of the foundations in place. Right. If you look in terms of Nest, in terms of all our information, there’s a lot of good thinking that has been done in the last 20 years of security that I think is right for really taking to the next level. ASVs is a good example. It’s great, but until you can create the 20 page of ASVs that matters to that application, it is never going to take off effectively. And I think that’s amazing. Imagine feeding ASCs, feeding the source code and saying, okay, which ones are relevant? In fact, imagine feeding it the policies, feeding, you see compliance, feeding you the requirements, feeding you the risk, feeding you the thread factors, right? And then say, okay, now take this. Now give me what I should care about, where should focus, right.
Nathan Case - 25:10 Especially on a privately trained model. I mean, yes, I can spin you all sorts of scary stuff about a publicly trained model and an evil insider doing horrible things to you, but are you telling me that evil insider isn’t going to find an easier way to deal with your stuff? Come on.
Dinis Cruz - 25:27 Exactly.
Sam Stepanyan - 25:32 Yeah. I’ve noticed Timo Pagle joined, and Timo, for those who don’t know, is a project leader of a fantastic Ovas project called Devsep Cops Maturity Model. I think it would be great to bring him in and hear his opinion on how chat GPD can help in development of maturity models.
Dinis Cruz - 25:55 Yeah, if you can join in.
Speaker 5 - 26:06 You directly ask me.
Dinis Cruz - 26:09 Yeah.
Speaker 5 - 26:09 I’m not having answer. I have to think about answer, right? It’s not something where we can directly have a good answer. What do you think might be very able to ask GBT? A problem which I’m having is to identify at which level should an activity be? Right? I estimate kind of expert judgment, how much effort is it, how is the value? There are things which would be nice to ask Chat GBT about the opinion, but currently I would think it doesn’t have a good opinion. I didn’t ask, but I assume it doesn’t have a good opinion.
Dinis Cruz - 26:52 You should try. You’d be surprised.
Nathan Case - 26:57 As we get into this, I think looking at Chat GPT and what it’s going to be good for is the first step. Right? If we look at a process, let’s pretend that we all own a company and we’re going to go ahead and use Chat GP to do a thing. I like Timo’s concept of what is the actual output and what money can we make, what is the driving force for doing a what do we get out the back of it? I think as we look at code review, as we look at some of the things that we could probably have it do, there’s a pretty substantial benefit coming out the back of that could probably be monetized from the CFO and say, hey, without this many errors, we will have this much more money because we’ve saved this much time. I think as we get into it, that’s probably the way to go, though, if that’s your point, Timo.
Dinis Cruz - 27:44 Ish yeah.
Speaker 5 - 27:48 That’S how you could express it.
Dinis Cruz - 27:52 Let’s look at your example. You have the Devse maturity model, right? I think models like those, I think the real value is they set up the best practices. The problem is always how do you connect that with reality and actionable items. What’s interesting is to say, here’s the latest version and you think about it. Some of these things you need that ingestion, right? Here’s the latest version that has been modified. This is this week’s version, whatever. This is the latest version of a particular maturity model. Here is now the code, or the organization structure, or the systems, or the things, or the practice or the CI pipeline that the organization has. What are the next things that should happen? Right. What is the maturity model of the organization based on the standard? Those are the kind of questions that you’ll be able to ask something like Chat TPT once you can feed some of these data in there.
Dinis Cruz - 28:47 In fact, I think it’s interesting because the latest version already allows you, I think it’s 10,000 words or something like that, to feed data in. We also need to start thinking about how can you package things in a way that you can actually feed the model right. Ask the question on top of the model. Yeah.
Speaker 5 - 29:07 It would be very nice to. Give all the project source code to it and then it tells you where you are, right, that would be amazing. You don’t need to sing, it just says that is done, that’s not done, that’s the next step. Yeah, that would be very cool. I think a lot of questions can be answered when you just give your code, right? Because you do infrastructure as code nowadays. Actually every point, not every point, but most of the points in such a maturity model you could probably get a good answer.
Dinis Cruz - 29:45 One of the things I like about where Microsoft is going with this bing, if you go up some keep showing that’s great. One of the things I like about this evolution is that they start to show the sources and I think that’s going to be an evolution here. Right. I think the whole idea that it’s also black magic will move before because we want to know that, right? Because you can even want to have one model kind of analyzing the other model answers to say how much of that is actually true, how much of that is actually made up. Because there is things called now the auto AI. If you guys seen those, which is when they actually try to automate and create and basically have this really nice workflow where you almost start to think about here are the tasks you want, here are the models you’re going to use, here’s the structure, here are the tasks.
Dinis Cruz - 30:32 At the end and when you do that, you start to be able to feed it a lot more complex data. Timothy, to your point, right, you should say here’s my app, he’s my architecture, here’s my environment, he’s my AWS environment, right. Here’s my logs. Here is 2GB of cloud trail logs right now apply. Now the devsecos principles or the DevSecOps thing here. Those are the kind of questions that before would be crazy to answer, right? Just think about it.
Nathan Case - 31:03 I mean, I don’t have to write IAM ever again. No more IAM rules. Off you go.
Dinis Cruz - 31:10 You now start to write intent, which is different. So you start to write rules. So your rules should be the intent. This is where it gets really cool because now you start to hit the business requirements. You start to say what I want is for these users to do this that thing. What I want is for this project to have these properties.
Sam Stepanyan - 31:29 Dennis, can I also add one very important clarification? You can also ask make the suggestions based on the assumption that the business type is medical or health, government, utility.
Nathan Case - 31:44 And all the regulations.
Sam Stepanyan - 31:45 And this is all the regulations. That’s where I found very good value, right? For example, we can take any of the requirements and just say okay, what do I need to address if I am a medical organization? What do I need to address if I’m a bank and please prioritize this for me if I’m an automotive manufacturer or airplane manufacturer, I think that’s a very great value of large language model context.
Dinis Cruz - 32:14 Right, and the whole point of the maturity model is exactly that. Right. If you look at, again, maturity model as an example, you should say where are we and what are the next steps and where should be the priorities? Again, you can still verify that, but the ability for that to scale is enormous.
Nathan Case - 32:33 Well, just looking at the I think the regulation point that Sam made is just horribly pertinent, especially with EU and GDPR and all of the crazy stuff that we have to start looking at is what is actually PII. Okay, we’re going to go ahead and write this regulation body, that’s great. This corpus of text can be sucked into and I get that this can’t happen yet sucked into Chat GPT and go ahead and look at my code and tell me if any of this code is actually putting to logs something that is PII. That’s been one of the gremlins for privacy for years. All of a sudden we have PII going to logs accidentally and oh my God, what the h***? To be able to pull that out and say, well, look, we’ve seen all of this stuff and this function right here is going to do that bad thing.
Nathan Case - 33:19 I mean, even just a highlight over three or four of those places as opposed to having to read millions of lines of code to find that huge benefit, makes high trust a h*** of a lot more achievable.
Dinis Cruz - 33:31 Yeah. If there’s no describing intent, this is like the worldly maps. This is an evolutionary step, right? This is one of those where the way I think about this is like the thing about really cool the worldly maps is that you got the genesis commodity, right? Now we don’t know which one will jump from product to commodity. Right. Like you think about it before charge GPT, who’s going to get that first? If you make bets, you probably say Google, right? Maybe even Apple or Tesla or other organization or some of the AI things. What these guys did with the GDP four was they made an evolutionary jump. Now we can predict what will happen next because the next jumps are not that great. They’re just little evolutionary little things. The big jump has been done. If you look at, for example, like we talk about Google, I stop using Google for now because I prefer to describe the intent that I have.
Dinis Cruz - 34:27 Unless it’s an esoteric thing, I now spend my time describing what the intent I have and refining the intent that I have, which is a lot more efficient way of doing it.
Sam Stepanyan - 34:36 Yeah, but are you actually using Chat GPT? Because obviously, again, very interesting point about Google losing in this race because as I just shared as my screen from Microsoft Bing search engine, because Bing actually combined the chat GPT with live internet search results. Even though it takes a while because it thinks about answer, what it comes back with is actually probably exactly what you describe in terms of what you want to achieve. So there you go. Here just ask about again, since Timo is here about OS devseco’s maturity model and it gave me the precise answer and it suggested me the next question, which is how can I implement this model in my organization? Again, it provided me some information, what needs to be done to implement in my organization. Itself suggests the question what are the benefits of using Fseco ops maturity model? I think these additional prompts and the fact that it kind of leads you down a path.
Sam Stepanyan - 35:38 Probably we’ve all seen this answer suggestions from Outlook or from Microsoft teams or whatever, even from LinkedIn when it suggests you some answers. They were completely nonsensical up until now, because now it actually makes sense. For example, there you go, it says, well, you need to carry out a self assessment of your organization security practice and say well, how do I do this? And it tells me more. Only Microsoft Bing has it at the moment. Google thing still needs to catch up.
Dinis Cruz - 36:21 Jtbg UI will give you this, right? Again, this is just on the Bing environment, right? But it comes from there. It seems that’s the thing, it’s the feeding of the model, right? What gets interesting is when you can give it data, when you say here’s my organization. To be honest, this is where the next discussion, for example, again, let’s go back to DevSecOps model. I think we now need to think about how can you create, how can you feed data that is relevant to the DevSecOps model, that allows good basically not just the review, but the analysis. Because think about it, in the past you create a model, then you go how do evaluate this? That would be a massive manual effort to do it, right? Because how do you review it? Where now we can say, okay, if you are on this, let’s say if you have Google cloud, right, then you need to get this data feed.
Dinis Cruz - 37:13 If you have AWS, then you get that data feed. If you have GitHub, then you get GitLab, you get this feed, right? That can then be the feeding of the data to the model, right? You can have the actions, and then you start to have the feedback loop, right? As time changes, you can then say okay, and then you say okay, here was the last analysis, here’s where we are now. How much have we progressed and what are the next steps?
Nathan Case - 37:44 I think as we get into it. I mean, beyond all of that even, let’s look at it from an operationalist point of view. Let’s pretend now that we’re all operationalists and we have to keep our company alive for the next. Take your pick. How cool would it be from a DevSec Ops model to be able to say code one side, camera here, code one side, and monitoring an Ops on the other. Now not only can I tell you that both things actually are being evaluated, like I’m looking at your code and I have the monitors for operations to hand them and say this is actually getting monitored all the time. The observability for this is right here. Being able to click on it and go back to the actual function that monitor is testing. I mean, the ability so geeky incident responder in me goes, oh my God, I can actually look at all of these things and say this goes back to this set of code and this is why this is happening.
Dinis Cruz - 38:37 It’s a game changer. This is what I’m saying. I think the security vendors, which are critical part of infrastructure, they really need to step out because most of the tools now for me started too primitive. Really. I cannot ask this kind of question. Really, I’m going to have to SQL or this kind of complex queries. I want context, right? A lot of the times, a lot of the engines, a lot of the business build engines on top of it. They need to restructure to say they need to be using a chat DBT like environment. They need to start creating their graphs in a way that is consumable by that. I think that’s the biggest transformation is that now graphs will really scale because that’s why these engines can consume very well. We can now use graphs for context, but we don’t have to create them.
Dinis Cruz - 39:25 We just have to almost start thinking about the instructions for how to create a model around it. There’s already a nice marketplace for models, so we need to start have models again for the bits that we use, right? You could even argue that every framework should eventually come up with a model that understands how that framework behaves because every framework has things that are unique to them. The Spring framework, the Express framework, the Fast API framework, whatever, the Net Rest API framework, all those frameworks have behaviors that then once you have a model that understands it, you can ask so much better questions on it. That’s why I think that’s a big game changer. I think in the security world, what I will say is that we are well poised to leverage this. I think the good security team should not be driving a lot of innovation because we should be on a forefront with all these transformations.
Dinis Cruz - 40:31 The next thing is how can we change the business with it? Right. Because a lot of because I got to the point for me where I feel I need to understand the business in order to protect the business, right. Most of the changes that we want to do are changes to the business. I see that the real interesting opportunity here is help the business to understand itself, drive change there so we can push the security changes with it.
Nathan Case - 40:56 I think there’s that and I think that’s the if you look at how, the overarching, thing that’s going to drive this into business is the fact, hey, we can make more money on that. The reality of being able to look at these things and be able to say from just the stupid simple things that it could do. I was talking to a red teamer that got shoved into incident response and he had to make a choice and he decided that he was going to clear out all of the AWS API credentials for a service when he realized his company had been hacked. I expressed to him that was probably a bad choice. And he said, well, but why? I said, well, because you just caused an eight hour outage for your website. That was bad. As an incident responder, we can get that, we can understand that.
Nathan Case - 41:50 If you could build out a model that allows you to ask questions like that as you’re going and be able to say, hey, what’s the right answer here? Be able to have it give you a contextual response of, well, if you do that, these are the things that are going to proceed from that thing. Now I don’t expect it to be able to say, no, don’t do that’s a bit much, but just give you a contextual response about, hey, this is what’s happening right now, it would be really nice to know that I shouldn’t delete the AWS API credentials. These are the things that are going to come out of that choice context.
Dinis Cruz - 42:22 Right, yeah.
Sam Stepanyan - 42:24 What you’re talking about, Nathan, I think it’s also decision trees, right. In the context of security incident Response, that’s actually one of the advantage, that it can actually give you a decision trees. As we just seen in my live demo, it can take you down a specific path so it can go deeper and if necessary, go down to the line of code or particular change. I know some companies already started doing things like feeding change requests that they made to the configuration of the systems into it. That makes interest of what change affected this incident.
Dinis Cruz - 43:00 Absolutely. This makes a decision tree scale. The challenge I always have in decision Tree path is that to be really useful, it has to be very specific. In order to be very specific, it becomes massive. Right. And then people get overwhelmed, right? Because you need the detail. What this allows us to do is to have context. You have a massive object model, a mass decision tree, but you only see this bit. That bit, more importantly, not just a small bit, is in context. In fact, this is an example where some of the next generation of chat DBT like technologies, we don’t want deviation. I think at the moment with this first experimentation, we still at the mode where we almost again, it’s fun that image mistakes and it’s fun that’s 100% correct. We can get to a point where we says we don’t want that.
Dinis Cruz - 43:51 We want to say this is your body of knowledge, don’t use anything else, just use that. That’s the current decision tree. What you need to understand is what those words mean in the action of that. From a decision point of view or the secrets of events, this should be your truth. Again, we’re not that far off on it. This is just a question of what data you feed them on versus the model having. In fact, I think sometimes the cool models are too powerful. We actually need less powerful. Some of these engines that don’t have all the emotion and don’t have all the ambiguity, don’t have all the ability to come up with things, just have the ability to understand and execute on certain things.
Nathan Case - 44:30 Well, something stupid simple like let’s give it the corpus of Windows Server code, just that thing. We’re going to give it all of it manuals for the last 20 years. I, as a new person coming out of college, need to do an ad set up. How do I do an ad set up? Done.
Dinis Cruz - 44:53 More importantly, how do I do an ad set up? With this configuration? Correct. This configuration is going to be different than that one.
Nathan Case - 45:03 Or even to ask me the questions about do you want to do this, that or the other? These are the outcomes to your point about decision trees, in many cases, the decision tree is actually the thing that the new person doesn’t have yet. They don’t even know that. They don’t know. How do I even begin to ask that question?
Dinis Cruz - 45:19 I think that’s the sweet spot now, right? I think companies who can address that in security, we are such a complex world now that we are ripe for that innovation. And I think that’s what gets interesting. And it’s like Sam was showing it. You now can start some really lovely feedback loops between the tools where you feed here and you feed that and then you almost let reality validate it, if you think about it. If I’m saying that I have a last rule, I have a rule here, I have a thingy here that’s supposed to lock this down. The next time you get the data, it should confirm that, right? It’s not a zero sum game. I think that’s where it gets interesting is when you feed models to models, when you chain them, which eventually it can go south very fast. I think we also have a massive potential to accelerate a lot of these things.
Dinis Cruz - 46:10 Yeah, cool. Right, guys? Oh, Sam’s gonna give us another good example.
Nathan Case - 46:16 Sam’s gonna do one more thing.
Dinis Cruz - 46:20 Yeah.
Nathan Case - 46:24 Sam’S on mute.
Dinis Cruz - 46:26 Sam, you’re mute.
Nathan Case - 46:34 What Sam’s showing us here is the summary of reporting on incidents, which is interesting because it actually gives us the source code that it came up with.
Sam Stepanyan - 46:41 The question here was, how can I use Chat GPD for security incident response? But very interestingly. Right. It contains an ad. Can you see this? It actually advertises Microsoft Sentinel and Defender.
Dinis Cruz - 46:58 Yeah, that’s interesting. It was the answer above. Influenced by Microsoft. I think there’s a really cool tool that should be built, which is to understand the bias of the engines. I actually think that tool is important because we talk about these engines being sentiment one day, right? Hopefully not that far. I think we need to start detecting that. I think we need to start having almost like we need to start almost like pen testing a GBT engine to see, for example, like that. Does he have bias? Does he have bias on gender? Also, does he have buyers on data points? Does he have bias against companies? Does he have buyers against well, it’s.
Sam Stepanyan - 47:43 Owned by Microsoft and I just showed you an example how it was biased.
Nathan Case - 47:47 Obviously it’s a little biased.
Dinis Cruz - 47:49 Exactly. Right. But that’s an important point. Right? It’s an important point to understand the bias of this. Right. I think it’s one of those cases that as the technology matures, it’s again, we need to understand the framework that we’re dealing with because this is going to become all new very fast. We’re going to expect to talk to our engines, we’re going to expect to talk to this going back to the Microsoft copilot. If you look at there’s a whole ratchet of copilot products and they’re launching out for Word, for PowerPoint, for all these things, right? So they’re really raising the level. We need to start framing, but also identifying the buys and limitations of the engine. Pen testing is a good cooler, the engines themselves, right?
Nathan Case - 48:34 Different sort of pen test.
Dinis Cruz - 48:35 Yeah, different sort of Pen test. Right. But understand those blind spots. Right. I feel that because you can feed one to the other, we will basically ratch up quite effectively a lot of the stuff that we’re doing.
Nathan Case - 48:48 We teach them to feed one to the other and then we teach it to feed one to the other, what happens? We make skynet.
Dinis Cruz - 48:56 That’s the thing. It’s when you have actions. But that’s an interesting concern. Right? I think it’s a valid concern that these pads leads to that. Billy, may I ask a question?
Speaker 6 - 49:15 Yeah. Can you guys hear me?
Dinis Cruz - 49:17 Good. Perfect.
Speaker 6 - 49:21 I was just going to chime in about the bias thing. I actually seen people test that to see if it has a certain bias based on different views or things like that. What they were able to determine was that it will have a bias based on how you frame a conversation. If you ask it I don’t want to start a warrant here or anything, but if you ask it something like point out the ways that this political leader is a tyrant and it will post exactly that. It’ll post all of the things against that particular leader. But if you post a question.
Dinis Cruz - 50:18 Where.
Speaker 6 - 50:18 It’S more political or more of an equal footing, then it will try to answer it based on that. I think what it really boils down to is people are going to get the answers that they really want until they can make it more of a megaworth checks for that kind of context.
Dinis Cruz - 50:49 That’s why if you think that almost, I guess, to our world, it’s interesting to think that we could have a world where we have a security bot that is used by developers, by engineers, by practitioners that we have already fed our BIOS. In that way the bias could be a good thing because we want specific things to occur. We want specific frameworks to be used. We want security practices to be used. Right? If you imagine, like, if you have a developer running a web service and using a bot to help it, we want the bot to already think about the best way to deploy the web service, the best way to protect it, the best way to authenticate it, to validate all that jazz. Right? It’s an interesting way once you understand how we can influence an engine. I agree that the way you ask the questions, you can determine the output, but it’s also a way to understand your audience, right?
Dinis Cruz - 51:46 I’m assuming now this is in an environment where you can see the feedback loops. So cool, guys. We only are look, I’ve learned a lot of good stuff, Sam. Thanks for sharing those. Those are really cool, right? I think we need to do that one of these every two months because things are going to change so fast, right. We need to start showing some good examples of this going on.
Sam Stepanyan - 52:10 Going back to your favorite topic for people who haven’t seen Dennis’s talk at OAS London about using Jira for managing your security program. So, of course, one of the things that you can use, and I’ve seen people started using Chatgpt Impact on advice, for example, publication security, just create Jira tickets. With a text in this, Jira tickets will be context specific and very relevant and in a language which can be understood by software developers find who’s doing that.
Dinis Cruz - 52:39 I haven’t seen good examples of that. Let me know who’s doing it. Let’s invite them to do a session. Right? I would love to see that. Cool. Any final words on this topic?
Sam Stepanyan - 52:52 I think it’s very important for us to understand that this is all very new and we’re already seeing some very exciting developments. I had conversation with a few vendors and saying, well, are you threatened by this? They said, no, we actually embrace it. That’s where I see probably in the next year, we will see security vendors, SaaS vendors, and just cybersecurity vendors actually embracing this technology. Finally, the AI on the data sheet is going to mean something. Not just we use AI and machine learning, so please buy us. And we also use blockchain.
Dinis Cruz - 53:25 Right.
Sam Stepanyan - 53:25 The usual buzzword. So I think this evolution is coming. Again, we have a responsibility from security engineering perspective. Just make sure that it’s also secure. The use of it is secure. I know we haven’t touched on topics like privacy and giving it personal data, company data, and company intellectual property, such as source code. That’s probably a topic for next conversation.
Nathan Case - 53:51 Hacking GPT is a different thing.
Dinis Cruz - 53:56 Any final thoughts, Nathan?
Nathan Case - 53:59 I think we need to approach chat GPT and frankly, any of the other chat bots. Very much the same way we approached Cloud and very much the way we approached, I don’t know, databases back in the 80s.
Dinis Cruz - 54:12 Right.
Nathan Case - 54:12 I mean, this is a change to the way that things work, and that’s okay. It’s not the end of the world. There are going to be things that we need to work through and there are going to be errors and we’re going to screw up and we’re going to fix it and it’ll be okay.
Speaker 5 - 54:34 Yeah, I think it will be amazing. In the future, maybe only the person who can ask the right question will get paid in a good way.
Dinis Cruz - 54:46 Yeah. Well, it’s more reality now than it was four months ago. Right. I think it’s like the Internet, right? It changed a lot, but then we evolve and we go to the next level. Right. I think the difference is going to between the people who use it and people who don’t use it. I think that’s why we need to learn how to use this very effectively. Cool. All right, guys, thanks a lot for this one. We starting to move the needle and I’ll see you guys in the next one.
Sam Stepanyan - 55:13 Thank you.
Dinis Cruz - 55:14 Thanks, guys.