SheikShaib

[Music] all right good afternoon Calgary let's try again good afternoon Calgary the Flames are going to win the Stanley Cup all right oh somebody tough crowd so it's always fun presenting on the last day of the conference and I'm just two sessions or last session before everyone goes goes home right and even more importantly it's always fun presenting after a guy who just showed you how to hack the nail outs of everything so I'm just going to make sure my iPhone is not hacked yeah looks looks as though it's good all right so um next slide next slide so I got oh I'm I can see that there oh yeah right so in terms of an

agenda today um so first of all I work for IBM I'm a cyber security architect um I work with customers in western Canada as well as Eastern Canada I like to euphemistically say I'm from Eastern Alberta which is in Markham montario don't hold it against me um and in particular what I do for IBM is that I I speak obviously IBM Blingo and some our foreign languages like Microsoft and those gifts that keep giving right so what I want to do today is to touch on a topic that everyone and their brother is is talking about which is AI um the good and the bad that's the title of the presentation it is not about hacking so

if you guys are expecting hacking this is the wrong session this is about how do you defend against the bad guys right uh we're going to talk about initially if you uh you know I had a nice video to show you but we're not going to show you that today we're going to talk about the threat landscape as it exists today we're going to talk about security for AI and then AI for security and this will become a bit clearer as we go through the presentation um we're going to talk about how IBM is addressing this problem that we're going to you know that we'll get into in a few minutes um and then what's next what are we doing to really

move the ball forward to address the problems that I'm about to show you so again next slide um this is a great video uh I don't know if you've ever seen it I showed this at the bsides Edmonton conference and one or two people have seen it you seen it sir this is a fantastic video because it shows you the power of AI in terms of just basically compromising someone's identity so how many of you have something like this you know where it's locked and I do this and it opens how many of you have one of those yeah you feel very confident after watching that video when you see this so I need not go further because if

you were here in the previous presentation which I caught the tail end of I want to throw this damn thing in the the garbage now because it's it just oh it's actually working yeah can't get the audio you can't get the audio all right never mind let's skip to the next one okay so U look it up on Google and now watch it because it is extremely compelling video right even Morgan Freedman is older gentleman you know he's got some little things on his face and so on even the Shadows is awesome and why that's a problem is this the next slide right so deep fake voice this is something you can go on the

internet um speechify it you know it's got this thing um you next click in the next click you'll see um actually this one is free it's a free deep fake voice generator and it's from um the you URL I can't see it here but you can maybe you can see it URL is there you can actually try it out for yourselves um yeah and this thing is free right so think about that right we have you know with respect to the Biometrics we've got you know deep fakes in terms of video we got deep fakes in terms of voice you know they can pretty much say whatever you want them to say uh all it takes is 100 words so how many

of you have Facebook where you've told everyone and the and their brother you know that you've had a great uh vacation with your girlfriend your wife or whatever and you've you know have audio and video and explaining how wonderful it was um I was speaking to a colleague earlier and she's going to Cuba and she may put the stuff on Facebook cuz it's going to be a great vacation 100 words that's it and they can clone your voice next click all right and so this is the problem the problem is that in some institutions like in this example here the bad guys were able to clone a director's voice and they got $35 million right cuz Biometrics is voice

face you know fingerprint um there's a certain president in the United States ex-president I should say former president who does a lot of this waves his hands how confident you think after watching some of this stuff you are that you couldn't get a high powerered camera and get a copy of this fingerprint yeah you can do it you've done it BR okay so president likes to do this and he's the guy that has the his finger on the nuclear nuclear button so scary stuff so that is the problem we're trying to address so go to the next slide all right so this is some stats from IBM uh we do a study of about 500

00 uh customers around the world and these are some of the numbers that you see here in terms of the problem from a Global Perspective that we're that customers around the world including right here in Canada is is uh is experiencing so ransomware is still a is still a thing hasn't gone away it's just getting better and better right just this Tuesday yeah this Tuesday anyone heard of a thing called Patch Tuesday yeah Patch Tuesday did did you read the latest one what they did they patched three vulnerabilities that were in my opinion those were zero days and then the point here is that each one of those vulnerabilities allowed the Bad Guys by the way Microsoft admitted that it was

exploited in the wild right so it's not a an academic thing each one of those allowed system and Men access if you if you don't believe me I'll show you the article right happened this past Tuesday so that's that's the problem right fishing is still a problem and by the way the AI guy the guys are using AI right now to develop very very very very convincing um fishing attacks spare fishing attacks so it's no longer your brother's friend cousin who had a you know who was a former Prince of Nigeria that has $100 million that stuff is all stuff now it's a very sophisticated multi-stage attack the bad guys are going to send you an email introduce

themselves they don't want anything right it's just establish a conversation make sure you're comfortable with it they'll tell you something interesting you don't have to click on anything yeah you get an email okay fine next email next email by the fourth or fifth email you're done you don't even have to click on it right the fact that your your browser not your browser your outlook or whatever you use for your email uh does preview that's the vulnerability they're exploiting and you're dead don't believe me Google something called should I tell them what it is it's an Israeli company that has this thing um yeah so so it's it's a it's a real it's a real

problem all right so those are the numbers that you see there um the next slide here is sorry yeah go to the next number um we we did we surveyed um this is IBM again research we surveyed 200 uh customers cios and we asked them some questions about Ai and you see those numbers there right 64% of customers out of the 200 are facing a lot of pressure to adopt AI 64% in other words everybody and their brother to use that euphemism is approaching AI in some shape way or form right in their business operations um but they're concerned about the risks 84% are and so AI has been it's it's a great opportunity right from from a usability

from uh you know speed to new new new function new new uh business uh uh utilities new business uh um products it's a great thing that everybody is using but it there's some problems with it and these 200 uh customers that we s surveyed identified that they need some defenses because they're worried about this they're really really worried about this and this this research is uh 2023 you got the link below in the in the slide they're going to get a copy of these slides right yes okay perfect all right next slide okay so what then is the real problem from a uh an attacker's perspective or sorry an defense perspective the last session was about

how you break things in how you break into things this session is about how do you protect protect things so the attackers will do two things two main things they will Target the AI and they will use AI to Target you in a nutshell right that's what's happening so if you if you how many of you have done cyber security training you know you heard about the CIA Triad right confidentiality availability uh Integrity great um the new attack surface is the AI itself AI models you know whether it's Google's what's it called uh Bart Bard Bard Bard d right or it's Microsoft Coop pilot or whatever uh anthropic um for AWS they they have something called

um just having a have it's late in the day uh any who um every single model is is at risk is what I'm saying and so the way they're using it and we'll go through in some details you see some examples they're really they're using it to do prompt injection so there's a kind of a there a there's a thing called prompt engineer you guys heard about that you actually can become a prompt engineer to figure out how to use these things more effectively all right looks like I said something so and then all right so in terms of how they're going to use it um to to defeat you or they're going to actually use AI

to generate as we discussed earlier here better and better fishing attacks better and better uh code right using AI to generate code um attackers will actually use AI to you know evade your defenses so those are the two perspectives I wanted to share with you so on the next slide then sorry you got a

question

fishing it's not coming from inside right okay so if I understand the question correctly it's about using AI not not for nefarious reasons but using AI internally for yeah like for good use good use as well as like you know people are learning about like day today yeah yeah okay all right we didn't plant she's not a plant so that's the next slide all right sorry before we get there so IBM has two approaches right there's security for AI and AI for security so let's just jump to the next slide all right so here's how the bad guys are misusing Ai and we're going to get get into some examples next slide so if you think about it right an

AI model the way it's built is that you have a data collection I'll get to your question in a sec uh they they've got an AI model that's developed right usually you know the data scientists they're very smart guys but they tend to use a lot of Open Source open source models that um are widely available and used by the data science Community around the world right so that's kind of how they start off and then of course they tune it and they add more stuff to it they use the internet stuff they use open source stuff that's kind of how they they they do things then they start to train a models based on the training

data you know with some live data right and then they um you know they put it to Live use right now the attackers will Target that sensitive data that's being uh unwittingly usually unwittingly present in the training data right and you'll see an example of that in a sec um they're also building so we're also building new applications right here at IBM for example we don't really have an HR department anymore we have a HR AI system you talk to it by and you know you ask it questions and response is it name car no it's that's not the name his name is not car it's a typical IBM name uh IBM chat box IBM chat box very you know HR

chat HR CH yeah it it it is a real thing right so IBM um I know this call is being recorded IBM uh basically um encouraged the employees who are HR people to find a new job um right and so uh the next piece of course is that what we what we call model inference So based on the uh the way the AI is you know we we can actually um start to to based on the training data you know it it will respond to um live data right live questions and so you can use model inferencing to to hijack that the model so let's go into the next slides you see an example of each one of these all

right so this was something that happened uh a few months ago 38 terab of data was accidentally exposed and in the fine print it says there all caused by one misconfigured SAS token a SAS application one misconfiguration and look what happened on the other side right um basically the bad guys were able to or we presume the bad guys they were able to essentially get personal information passwords private Keys 30,000 internal Microsoft teams messages that's what they got right from this one misconfigured SAS token all right and so the problem here is that because of this exposure right if you're a bad guy you are able now to essentially inject stuff into this model into this code how

many of you are using co-pilot today and really feel it's a great solution you are you sure yeah it's it's really good right okay let's go all right let's go to the next slide I work o sorry I work obl oh yes okay no this is not a Microsoft you know dising session this is this is just telling you what what the facts are right so yeah you know about this right all right so here's the problem then if you're using and and just hopefully I'm trying to answer your question now right if you're using AI applications that are built insecurely you know based typically on these two models right hugging face and tensor flow right

that's kind of a lot a lot of AI models tend to use those things uh as as as a starting point right um and they're publicly available the problem is that bad guys could essentially use you know these as back doors into the organization So you you're using it for let's say you're a cybercity analyst right and you're using it to say tell me about this URL or this hash um and that hash happens to be the you know I'm a bad guy the thing that I developed you really think it might say oh that's a that's a very bad URL don't you know take that offline use your Ed system to kill it and you know block the firewalls

this one you really think it's going to do that oops oh crap I did all right so so this is this is called a supply chain attack right and here's the example right dark reading that example um is what we're showing there and let's go to the next slide here's another example of when you so this is about when you build applications in security um this particular one here is how how you can trick the AI model even though it has defenses even though it has proper defenses and and these guys from um I think it's the uh Carnegie melan University and they AI you know gurus what they did they appended a long suffix of characters to the prompts and

they basically bypassed the protections within the AI model so you really think do you really feel confident now that this AI solution you're using to learn and to do stuff is really secure it's a starting point yeah

yeah okay yeah so so the attack is called a multi-stage attack right I get you to think that it's very trustworthy and then eventually I kill you yeah all right I you know this this is not IBM saying this this is publicly available you know stuff I mean what we kind of know interally is even more nefarious than this stuff okay next slide all right so what's the solution and how do you protect this you know what the solution is it's kind of the Going Back to Basics right you got to protect the data remember the CIA Triad you got to secure a model you got to secure a usage sounds familiar data security all over again identity and

access management all over again right um you know just just making sure you do proper hygiene so IBM uh last week we announced a governance AI governance solution because that's the problem right the problem is that there it's the Wild West it's using these open source models it's using collaboration from University ities and stuff like that is using the internet to to essentially build these things and then they become business enablers right and so what we have to do we have to go back to basics secure the data secure the model secur the usage it's as simple as that well it's simple in terms of the concept but how you do it the devil's in the details

right okay next next slide so let's now pivot for AI for security now I apologize I work for IBM you're going to see some IBM stuff here but you know I believe other vendors are also thinking the same way so next slide all right so what we have done is that we're using the robot to H or defeat the robot right so it's really our AI versus their Ai and we're trying very very hard um to to to deal with this problem um because and unfortunately you can't see uh my screen but um I'm going to give you some some some statistics here we as an IBM company we we had no choice but to but

to choose different uh a different approach so IBM has 11 Global socks Security operation centers around the world every day we process 150 plus billion events that's with a B billion security events every day right from that we get about 11,000 security alerts every day it's a large company but there's no way we can deal with 11,000 security alerts no way so we had to essentially use our own cooking home cooking dog food whatever you want to call it to address this problem right and so what we did is that we have ai the tools the tools that you see here on on this slide um we're using those same those same tools actually it's the other way around we built the

systems in internally and then we took the learnings and so on and the models and we made them into put them into our commercial products so just to give you some idea about the stats I said 11,000 alerts about 74% of those is handled by the AI false positive we've seen it before it's a duplicate blah blah blah and we just deal with it right by the AI the 26 27% right we put that to a cue for manual analysis but the AI is also assisting in that process okay and so out of that when you when you look at it from from at the bottom end is that about 85% is closed with a combination

of AI versus AI assisted Med intervention and the rest is are are things that we have to escalate you know manually right we got to figure out what really went wrong there and so what we're doing is every time we see an event or an alert that is a problem we go back and ask ourselves a question why is why did the AI you know triaging and and alerting and so on why did that not work and so we take the learnings and we feed that back into the model okay so it's it's constantly learning and constantly getting better and better and better so we we had to do this because as I said these these these

alerts are just increasing um our budgets are not increasing you know we we we can't hire enough people and so that's that's what we had to do all right so just to show you a little bit more detail and in what I'm talking about next slide so we are using um AI in pretty much our entire threat management Suite right um so from an attacker perspective we have something called randori randori is a is a company is a bunch of EX hackers that decided to come good and so they made they they formed a company so the way the way a hacker looks at a an organization um you know from a URL a single URL or an email address they will

go to tongue and that right and they will actually penetrate your organization so that process that thinking has been automated and the AI that we built in it is is able to look at the services look at the you know the environment as we understand it from an external perspective summarize that and then present it to a customer using pretty much most of it automated right artificial intelligence and then from an EDR perspective yes IBM has an EDR we also do the similar things we look at behaviors we're not looking at signatures right can I say that antivirus how many of you feel that antivirus is a really good thing you do I think Microsoft yeah

sure you just were you here for the previous session all right yes you're right it the some of the EDR systems like crowd Strike Defender now is is is is pretty good carbon block um anything that relies on signatures garbage absolutely garbage don't even bother right it's it's it's Behavior models that we're looking for right because a zero day will easily bypass an antivirus solution like it there's no tomorrow that's what that's is what we' have seen in the field so those three vulnerabilities I talked about Patch Tuesday right you think your EV would have been effective against those probably not so you got to look at different tools you got to look at the Sim right to kind of figure out

how things are are are are are being collected and correlated and so on so we have a SIM we have uh um you know in terms of the AI this is what the presentation is all about we're using the AI in terms of the detection the triage the investigations itself and the response right that's what we're doing so I told you you know 11,000 alerts every day we're using these tools behind the scenes to to handle that right so it's not vaporware it's real stuff and it's getting better and better and better yes

sir ah okay yes yes well let me let me answer it this way we have our own threat intelligence it's called xforce and xforce is really about 8,000 um researchers around the world and what they do on a daily basis is just collect threats and they put that into the gigantic database they kind of figure out what's you know from a CVS score you know which where they are in the in in the grand scheme of things and then we push those learnings to these tools that you see here that's one but we also have a thing called Granite which is a large language model built by IBM so I was just dissing these L these llms for a

while and I'm about to tell you that we have one of those as well right what the hell what's the guy talking about okay all right let's skip skip uh what it's kind of late in the day and I I realize uh you you know where I'm standing between you and end of the day let's go to the next slide um from a data perspective I talked about securing the data right we have uh tools that are looking at the actual um you know the data itself who has access to that data we're able to figure out um how the data is being used and the AI is looking for anomalous Behavior right um one of the

problems with with these large language models anyone even including ours is explainability right how do you know that that model is free from code injection how do you know because by definition right remember your AI 101 there are there AI then there's machine learning then you've got deep learning which is a subset of machine learning right then you've got the large language models which or generative a generative AI which is essentially a subset of deep learning learning deep learning by definition is you may get the right answer but you can't really explain it so how does an llm guarantee that the the data you know the data the models the the learnings the you know the

answers is correct hard problem right so data security is how we use to address some of that not all of it but some of that next slide and then finally from an identity perspective right who has access to these models and it's it's it's again it's it's basic hygiene right it's making sure that the the data scientists you know the users uh are are they they've got the proper credentials the proper it's actually not that important to do any DNS uh spoofing it because there are certain destinations and domains and websites that you might be going to that actually fund or or result we got hacked okay I I I did something I don't know

what I did you might just you might have picked up the channel from downstair oh all right keep hands to the side don't touch okay I'm going to speak like this okay next slide that was that was frauding in terms of how it got so make long story short uh it's an IBM presentation we've got AI models embedded in a whole bunch of our security products um the goal here is not to sell you and I b m but just to show you that we are using AI you know right across the board right from protection detection and response perspective um did I were you about to answer me ask me a question and we got

interrupted sorry no the gentleman at the back okay all right so next slide okay and yeah next slide okay um where I want to go with this is how we are about to use AI to actually you know take a next Leap Forward and to do this we developed uh a large language model called Granite so finally IBM for you know we we we have these product names IBM blah blah blah blah blah now we have a product called Granite so Granite if you understand the meaning of the word it implies something solid something you know rock solid that that is unbreakable and so on so we've developed over the years a granite Foundation Model A large language model

and what makes it special is that we did not train it with internet data we did not we did not use these open- Source data that hug and face and tsor flow and other you know other kinds of open training data we did not what we did is that we Ed it we used the training data that we have developed from a large organization like ourselves and we also used customer data of course course we analyzed the data to enhance the model so right now the model has about 3 billion parameters um we're also leveraging to some degree uh metal llama right um yeah so that's what we've done and the other thing that we did is that we do not have

a version of granite that's exposed to the internet right so we we made sure that it's completely uh isolated from the internet um I gave this talk in bsides Calgary no bsides Edmonton a few months ago and I said it's secured and one customer one person said how is it secured I said air gapped oh well air apis and everybody laughed why because there was a woman who was a hacker this in the morning she gave a very good presentation on how to hack apis so what I'm about to tell you is that we're using you know internal security controls I don't really don't know what they are to preserve the Integrity of the model all right and

we're using it uh we our products you know that we've just announced we've got a code generator built on on granite we've got our security tools that is using granite and so how we're going to use it we're going to use it for um as it was really intended to be right interpreting machine data it's a bunch of a boatload of data coming towards you wouldn't it be great to say tell me what the problem is in simple English I don't want to see 59 million log entries and pointing me left right and Center just tell me what the problem is and also tell me what the fix is right and do it before I come into work on Monday

morning right if you have to take the CEO's machine offline do it in a nice way so that I can keep my job that's what you know we're trying to achieve with this AI okay so um accelerate the threat hunting all that stuff we're using it but it hasn't really mature to the point now where we can talk to it by natural language that's the next step okay next slide okay um and there's some of the details right so anything that is about um automating uh routine tasks mundane task that's how we're using the AI um we want to go from reactive to proactive right so something bad is happening in Asia does it affect me as a

customer right as a an oil company in in

Calgary um I'm going to say yes and no you here's here's why I'm saying that because the AI is getting better and better and better is it perfect absolutely not this guy the presented before me you want him to be doing the pr pen testing not every day but maybe once a year right but you can use the AI to do automated testing or automated pen testing on a weekly basis on a daily basis if you've got sensitive uh operations you're you're upstream or Downstream gas oper operator right you want to make sure that nobody's really accessing your your your infrastructure your OT infrastructure right so you can use these these tools and aut at way to

test for for any exposures people come people go things change right new equipment is added right one misconfigured SAS token resulted in 38 terabyt of data right so that's why you would want to use the automation for more regular assessment and then hire Henrik or somebody like him to do that deep you know type of pentesting make sense like at this point in time I would not recommend you stop stop that make sure you get somebody like hreo he's awesome okay all right so finally then um these are just you know final slides here I do want to leave some time for questions yes ma'am yes

sir MH right great question so question if I understood correctly was about um operational technology OT right especially in this town so scater IC type controls I uh systems I should say U that are commonly overlooked well we one of the reasons why it's commonly overlooked is because we can't get the right Telemetry or if we do we're getting an avalanche of telemetry right so we have to use machine learning you know some type of automation artificial intelligence to make sense of that right what's the normal behavior what's the anomaly what's that needle among the Hast stack of needles because that's really what the problem is right it's a the data the Telemetry is just so vast

so great question um I wish I had a better answer than that but I don't think at this point in time we have an AI solution that addresses that unique problem but I would want to say this with some caution that there's a bunch of researchers that are working on this doesn't mean to say we have a solution but they're working on this yes sir

sorry logging and getting that information

so oh um so if I understand the question correctly it's about using logs um from OT devices um and I think you're kind of wondering how effective that is how is it bit used is it yeah yeah I think that yeah so and being able to use yes so there there's a company called noomi there's another one um that actually have built some solutions with respect to OT devices right I think last time I looked at Nomi I think they had about 130 something different OT uh um you know switches and so on within the Electrical uh generation utilities and so on they they had some capability there to us to ingest those logs and so

uh from a q radar perspective which is IBM solution Naomi can send us the alerts not the data the alerts that they may find as part of that log ingestion but a more generic solution would be for example look at the network right look at the network because at the end of the day the locks could be Aus skated correct I mean if I'm a bad guy I would try to remove my you know hide my tracks but the network usually Henrik had me thinking by the way the network should not or does not lie so what I want to do is to look for the patterns of the network behavior that indicate something bad is happening and we have that

available today now is it scaling to an OT environment I got to go research that I I'm not sure but I know it works uh for a large Bel as I said before IBM we use our own home cooking we've got 350,000 employees in 100 and something countries around the world World we're using it internally so I know it works yes sir how do you envision the future of the security operations center

yeah so it's a great question I've been speaking to students every day um when I was in Edmonton and I was only here today and the the first advice I want to give uh students is if you're applying for your job at IBM or Microsoft or wherever don't just tell me you've done this course and that course tell me how you're using or what knowledge you have about our Solutions because at the end of the day I work for IBM right you work for Microsoft so you got to go and learn about our stuff right but what you what we want to hear is um you know you have played with co-pilot or granite or

whatever and uh this is what I understand about it and this is how I think I can use it and this by the way here's where some things I I think you need to be more cognizant of right from a you know next steps perspective if I got a resume like that from a student I want to talk to that person make sense because it shows you know my stuff you have some knowledge you know where the future is going and you may you have some ideas to make it better we don't know everything right you guys do so with respect to what the future is for the sock operations is going to to be

using these tools intelligently right I've got this problem I've seen ransomware I've seen a data exfiltration attack help me understand you know what's the prompts that you need to ask the model right help me understand the nature of this attack how pervasive it is where else have you seen this right what do I need to do to fix it right help me generate a response A playbook response to address this particular problem is it a zero Z day is it a one of those three Microsoft problems that we talked about right does that make sense yes

sir so the question is what we have agent in company that we will activate but notate that's a great question I I really don't know and able to

into yes so as I said IBM we have 11 Global stocks and they're around the world right so for example um something bad happens that incident that alert right is um there you know the guys in Australia austral would look at it and if they're you know the end of the day it gets moved to Europe and and so on so this is not just secur during the

life

MH yes so our EDR models um I didn't explain this to you but the way our EDR works I'll use that as an example the agent lives on end points right and then the five which is where the AI you know smart logic is is happening that's also an AI system that lives in the cloud so these these particular um agents as they report they can function by themselves they don't need the internet right yeah so they will they will learn the behavor to td.com something yeah whatever the agents discover um they will when internet Conn activity is is goes back or gets reactivated it will tell the hive this is what happened and then so the the the

the models again are dynamically adjusted some of the models are supervised and so we then we will push um you know in near real time the the uh augmented models so Mak sense you talk about offine kind ofi they all offine yes the the the agents the agents operate autonomously the remote L it could be or it could be on a plane or it could be on the beach in monteo Bay right so I can inst L on mono Bay yes yes yes it operates independently yes independently and as soon as it gets connected back to the hive that's when the learnings from that particular agent gets reflected there and then the high will adjust its models

and say oh I found something new or what whatever and then we'll push it to the other agents okay thank

you oh sorry uh no this is um yeah because we you're okay um we do have a demo guys thank you so much for waiting it's Friday afternoon but um we do have some prizes um and so please give uh the lady there um Starbucks ticket she asked good question uh gentleman at the back there and the lady at the back there come and see come and see the guy with the prizes um yeah so we do have a demo is it on December 8th yeah okay and uh we have um where is it okay um come and see me or we have these in our in the real presentation you're going to get there's a QR code

here and we're going to give you a demo of the of what I was trying to show you earlier okay um I know it's a QR code but it's IBM so if you trust us trust me it's it's good yeah after watching that guy's presentation I'm I'm really worrying about my phone now I know no no you're right there's a thing called is it called quing quing quing yeah it's a real thing so don't click on QR codes especially when you go to a restaurant and they say click here to access our menu yeah right someone else yeah exct yeah use some yeah the server yeah let's use your phone all right thanks so much guys appreciate

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