1 00:00:01,200 --> 00:00:03,320 Welcome to Business Analysis Live. 2 00:00:03,440 --> 00:00:06,560 I'm your host, Susan Moore, community engagement manager 3 00:00:06,560 --> 00:00:09,480 with the International Institute of Business Analysis. 4 00:00:09,600 --> 00:00:12,680 I explore topics with industry guests about the work of 5 00:00:12,680 --> 00:00:15,600 business analysis professionals and how their work helps 6 00:00:15,600 --> 00:00:17,840 organizations achieve better outcomes. 7 00:00:17,840 --> 00:00:19,760 And Yep, we take your questions too. 8 00:00:19,800 --> 00:00:21,760 Thanks for listening. Let's get started. 9 00:00:25,760 --> 00:00:30,520 We have been hearing a lot about AI these days. 10 00:00:30,520 --> 00:00:35,560 It seems like it is everywhere from fast food restaurants to 11 00:00:36,080 --> 00:00:40,400 online shopping to just about anything that's happening. 12 00:00:41,520 --> 00:00:44,800 One of the things that you may not be thinking about as you are 13 00:00:44,800 --> 00:00:51,280 using AI is the impact of cybersecurity on AI. 14 00:00:51,800 --> 00:00:56,040 And that's those two things are kind of colliding in the world 15 00:00:56,040 --> 00:00:58,560 at the moment. And I know that it's been 16 00:00:58,560 --> 00:01:01,440 something that our business analysis community has been 17 00:01:01,440 --> 00:01:05,440 talking about because our next guest is getting asked a lot of 18 00:01:05,440 --> 00:01:08,640 questions about how those two things work together. 19 00:01:09,040 --> 00:01:15,160 So on today's episode, AI and Cyber security, I am super happy 20 00:01:15,160 --> 00:01:17,800 to welcome to the stage Bindu Chanovarapa. 21 00:01:18,200 --> 00:01:21,320 Hey Bindu. Hello Susan, How are you doing? 22 00:01:21,960 --> 00:01:25,120 I'm doing really well. Thanks for coming to join us 23 00:01:25,120 --> 00:01:29,840 today. I I always enjoy chatting with 24 00:01:29,840 --> 00:01:34,080 you because it it just seems like we kind of vibe naturally. 25 00:01:34,080 --> 00:01:38,080 And so when you and I caught up recently, I said, tell me you've 26 00:01:38,080 --> 00:01:41,680 been around the world speaking. What are people asking you 27 00:01:41,680 --> 00:01:43,280 about? And before you answer that 28 00:01:43,280 --> 00:01:47,000 question, you've been our guest a couple of times on here. 29 00:01:47,000 --> 00:01:49,440 But first, I'd like for you to introduce yourself to the 30 00:01:49,440 --> 00:01:51,240 audience. Tell people a little bit about 31 00:01:51,240 --> 00:01:56,160 you and why they are asking you about AI and cybersecurity. 32 00:01:57,320 --> 00:02:01,240 First of all, thank you, Susan, once again for inviting me to be 33 00:02:01,240 --> 00:02:04,600 the guest here. And it's always great to speak 34 00:02:04,600 --> 00:02:08,280 with the community. So for those of you who don't 35 00:02:08,280 --> 00:02:09,960 know me, I've been the China member. 36 00:02:09,960 --> 00:02:11,800 I'm a business analysis consultant. 37 00:02:12,600 --> 00:02:15,840 I think if I look back, I've been a BA all my life, 38 00:02:16,040 --> 00:02:21,960 consciously and unconsciously. So in the last 7 to 8 years I've 39 00:02:21,960 --> 00:02:25,680 been focusing more on cyber security for business analysts. 40 00:02:26,520 --> 00:02:31,360 As part of that work, I've been a co-author on the CCA 41 00:02:31,360 --> 00:02:35,560 certification as well and currently the founder of 42 00:02:35,560 --> 00:02:40,120 Cybersecurity for Business Analysts, which is a space that 43 00:02:40,120 --> 00:02:46,320 provides cybersecurity education and services and of course with 44 00:02:46,320 --> 00:02:50,080 that, business analysis services and consultation and training as 45 00:02:50,080 --> 00:02:53,640 well. So yeah, along with that, I love 46 00:02:53,640 --> 00:02:58,800 working with the community. So I do take every opportunity 47 00:02:58,800 --> 00:03:03,280 to talk about this to share because as I'm sure all of you 48 00:03:03,280 --> 00:03:08,320 might have, our experiencing that the landscape is shifting 49 00:03:08,440 --> 00:03:11,560 very, very fast than we could imagine. 50 00:03:11,880 --> 00:03:15,400 So which means we have to adapt as business analysts. 51 00:03:15,400 --> 00:03:19,840 And when we talk about adapting, what is that we are adapting to 52 00:03:19,840 --> 00:03:22,760 is technology because technology is becoming pervasive. 53 00:03:23,160 --> 00:03:26,320 So when there is technology, there has to be cyber security. 54 00:03:26,320 --> 00:03:29,120 It's like 2 ends of the state. That's that's how I call it. 55 00:03:29,480 --> 00:03:32,120 So you can't pick up one end and not pick up the other. 56 00:03:32,360 --> 00:03:37,600 So cyber security is no more or security holistically is no more 57 00:03:37,600 --> 00:03:42,800 something an NFR or an add on. So it is very much should be 58 00:03:42,880 --> 00:03:46,280 part of our day-to-day business analysis and that's exactly what 59 00:03:46,680 --> 00:03:50,320 I am advocating. And I'm also on a mission to 60 00:03:50,760 --> 00:03:56,120 probably educate almost every BA to be aware of cyber security 61 00:03:56,120 --> 00:04:00,240 and make that part of their day-to-day work within 2030. 62 00:04:00,240 --> 00:04:03,080 So that's about me. Thank you, Susan. 63 00:04:04,680 --> 00:04:07,680 Yeah, I as you and I have talked. 64 00:04:07,720 --> 00:04:12,360 By the way, I also want to add that you wrote a book on this. 65 00:04:12,360 --> 00:04:13,600 Yes. So there's. 66 00:04:14,240 --> 00:04:19,120 That business analysis, yes, if you can see, I don't know with 67 00:04:19,120 --> 00:04:23,480 the screen, well, not really. There we go. 68 00:04:23,760 --> 00:04:25,640 Cybersecurity and business analysis. 69 00:04:25,640 --> 00:04:28,760 There you go. And so you give a lot of really 70 00:04:28,760 --> 00:04:33,440 good information for, because I, my sense is for the folks that 71 00:04:33,440 --> 00:04:37,560 I've talked to in our community about cybersecurity, they tend 72 00:04:37,560 --> 00:04:41,400 to think that it is about what happens after you've been hacked 73 00:04:41,440 --> 00:04:44,800 or after you've had some sort of incident. 74 00:04:45,120 --> 00:04:49,400 But really what cybersecurity and business analysis is is well 75 00:04:49,400 --> 00:04:53,920 upfront of all of that. And if you know it, because you 76 00:04:53,920 --> 00:04:57,120 are trying to think about ways that you could be vulnerable, 77 00:04:58,240 --> 00:04:59,040 yeah. Is that true? 78 00:04:59,320 --> 00:05:00,920 Yeah. Absolutely. 79 00:05:00,920 --> 00:05:06,160 I think a very common analogy I could give is, you know, when 80 00:05:06,160 --> 00:05:09,560 you're constructing something you lay the foundation and the 81 00:05:09,560 --> 00:05:15,000 foundation is only laid to the depth in the ground depending on 82 00:05:15,000 --> 00:05:17,840 on the length of the building that's going to stand out. 83 00:05:18,120 --> 00:05:20,920 So which means you're talking about security there as well. 84 00:05:21,200 --> 00:05:24,400 So it is in everything that we we do. 85 00:05:24,400 --> 00:05:28,400 It's basically risk, you know, as BAS we constantly look get 86 00:05:28,400 --> 00:05:32,920 risks, we constantly understand that it varies, it changes some 87 00:05:33,360 --> 00:05:37,880 pop up at the late part or some mitigate. 88 00:05:37,880 --> 00:05:41,200 So we it's, it's all about evolving those risks. 89 00:05:41,200 --> 00:05:45,680 And similarly, when we talk about any project, security is 90 00:05:45,680 --> 00:05:50,720 part of every fabric or the DNA of every activity that we do. 91 00:05:51,760 --> 00:05:54,760 It, it is, yeah. And I think that that talking 92 00:05:54,760 --> 00:05:57,600 about how you build a house because there's other ways that 93 00:05:57,600 --> 00:06:01,200 you reduce risk when you are building a house, right. 94 00:06:01,200 --> 00:06:06,200 We, we make sure that the, that we wrap it so that the elements 95 00:06:06,200 --> 00:06:09,400 can't get inside. We make sure that we check the 96 00:06:09,400 --> 00:06:12,200 grading of the land so that water doesn't come in. 97 00:06:12,480 --> 00:06:15,640 All of those things we have to be thinking about, and they are 98 00:06:15,640 --> 00:06:19,040 dependent upon the environment around us. 99 00:06:20,000 --> 00:06:24,880 Now maybe, you know, my sense is that people think of AI as maybe 100 00:06:24,880 --> 00:06:29,760 one thing, but let's define what you're talking about when you're 101 00:06:29,760 --> 00:06:32,760 talking about AI. So how? 102 00:06:32,840 --> 00:06:35,200 How do you define AI in this space? 103 00:06:36,360 --> 00:06:39,560 I think that's actually a really great point starting point, 104 00:06:39,560 --> 00:06:47,520 Susan, because we talked about AI and every time, you know, I 105 00:06:47,520 --> 00:06:49,920 think somewhere we need to understand what AI is and what 106 00:06:49,920 --> 00:06:54,200 AI isn't. In fact, my journey with AI or 107 00:06:54,200 --> 00:06:57,600 specifically RPA started in 2019. 108 00:06:57,600 --> 00:07:00,680 So first part of the project was RPA and the second part we 109 00:07:00,680 --> 00:07:04,800 wanted to bring in AI. So since then, you know, my 110 00:07:04,800 --> 00:07:09,800 understanding has been evolving and especially now with Gen. 111 00:07:09,800 --> 00:07:14,240 AI, which provides A countless number of ways to use AI every 112 00:07:14,240 --> 00:07:15,800 single day. It's a new learning. 113 00:07:16,240 --> 00:07:20,600 And whenever I speak with people on AI and, and I'm sure you, you 114 00:07:20,680 --> 00:07:24,960 might have experienced as well, almost every second conference 115 00:07:24,960 --> 00:07:29,320 topic will be based on AI. So everybody comes from their 116 00:07:29,320 --> 00:07:31,800 perspective. So every time when I hear, I 117 00:07:32,280 --> 00:07:35,960 reminded about the story of the blind man and the elephant, 118 00:07:36,640 --> 00:07:40,080 which actually I was sharing with you earlier as well, where 119 00:07:40,320 --> 00:07:42,800 the one of the blind people, they come and they touch the 120 00:07:42,800 --> 00:07:47,160 body and they say, oh, it's, it's like a wall or someone says 121 00:07:47,160 --> 00:07:49,800 it's like a rope. Someone says it's like a tree 122 00:07:49,800 --> 00:07:53,600 trunk. So the point here is all of them 123 00:07:53,600 --> 00:07:57,520 are correct in their own way, but they are all incomplete. 124 00:07:58,080 --> 00:08:02,040 So the way I think AI is exactly like that. 125 00:08:02,080 --> 00:08:06,360 All of us are seeing it from different experiences. 126 00:08:06,360 --> 00:08:09,840 It could be automation, it could be chat boards, it could be data 127 00:08:09,840 --> 00:08:14,640 analysis or even creativity. But yet the whole picture is 128 00:08:14,640 --> 00:08:17,120 much bigger than all of these pieces. 129 00:08:17,480 --> 00:08:23,200 So I always share this that AI is an incredible tool. 130 00:08:23,200 --> 00:08:28,240 Probably I feel one of the best transformative tool that we have 131 00:08:28,560 --> 00:08:33,840 invented so far. But the challenge or the real 132 00:08:33,840 --> 00:08:37,960 question that always comes to my mind is, are we prepared for it? 133 00:08:38,760 --> 00:08:42,679 So it's, it's like having a precision tool and not knowing 134 00:08:42,679 --> 00:08:45,840 how to use it. So with with anything, you know, 135 00:08:45,840 --> 00:08:49,160 we always have to know how to use it and we also have to know 136 00:08:49,160 --> 00:08:54,280 how not to use it. So at it at it's core, the way I 137 00:08:54,280 --> 00:09:00,280 see AI is it's nothing but algorithm or a math that learns 138 00:09:00,280 --> 00:09:03,440 from data. Data is the engine for it and it 139 00:09:03,440 --> 00:09:07,040 just not learns. It also can make those decisions 140 00:09:07,040 --> 00:09:10,360 and take actions without a human intervention. 141 00:09:10,960 --> 00:09:16,080 Sometimes it does a great job by the times it can be wrong, but 142 00:09:16,080 --> 00:09:22,000 very confidently wrong as well. So, so which means the point 143 00:09:22,000 --> 00:09:29,760 here is it is a very incredible tool, especially very efficient, 144 00:09:29,760 --> 00:09:33,600 fast, good learner. It's like digital intern. 145 00:09:34,080 --> 00:09:38,240 I think I've heard even Angela shared this as an intern and 146 00:09:38,240 --> 00:09:41,360 which I also experienced because whenever I'm prompting, I 147 00:09:41,360 --> 00:09:43,720 actually tell what, what you, you, what the role is. 148 00:09:43,960 --> 00:09:48,920 So it is actually the digital intern who not only understands 149 00:09:48,920 --> 00:09:52,560 the steps that you're asking it to do, but again, at the same 150 00:09:52,560 --> 00:09:55,160 time, it learns. And next time it can even take 151 00:09:55,160 --> 00:09:58,720 action without you involved being involved. 152 00:09:59,080 --> 00:10:02,600 So when it comes to security, it's exactly the same where 153 00:10:03,600 --> 00:10:08,760 there's a lot of work, there's a lot of pressure because today 154 00:10:09,320 --> 00:10:17,640 our threat landscape is so, so, so much complex and complicated 155 00:10:17,920 --> 00:10:24,640 with lot many systems, lot many third party suppliers connecting 156 00:10:24,640 --> 00:10:28,240 across the world, different locations, different touch 157 00:10:28,240 --> 00:10:31,600 points. We have expanded like ever 158 00:10:32,000 --> 00:10:35,640 couldn't even imagine the, the, the kind of lifestyle that we 159 00:10:35,640 --> 00:10:37,640 are having probably even 10 years back. 160 00:10:37,920 --> 00:10:41,440 So all of this is possible only because of technology. 161 00:10:41,440 --> 00:10:44,080 And as I was mentioning, when it then there is technology, 162 00:10:44,080 --> 00:10:48,040 there's always security. So today there's a lot of 163 00:10:48,040 --> 00:10:52,520 pressure. So with AI with its capacity to 164 00:10:52,880 --> 00:10:57,240 think faster, be more efficient, we will, we can, or we are 165 00:10:57,240 --> 00:11:04,000 harnessing its power to help identify threats and many other 166 00:11:04,000 --> 00:11:06,240 things within the cybersecurity space. 167 00:11:07,360 --> 00:11:11,280 And yeah, with this, I think it's if with any tool, it's 168 00:11:11,280 --> 00:11:13,240 double edged sword and even here as well. 169 00:11:14,760 --> 00:11:18,560 And so you said that technology introduces risk. 170 00:11:18,560 --> 00:11:24,240 And I bet if we think about the kinds of systems that we have in 171 00:11:24,240 --> 00:11:28,000 our organizations today. So let's take AI out of it for a 172 00:11:28,000 --> 00:11:30,240 moment. There's probably a list of the 173 00:11:30,240 --> 00:11:33,040 kinds of risk that might be introduced. 174 00:11:34,320 --> 00:11:38,360 But as we think about what AI has the ability to do, because 175 00:11:38,360 --> 00:11:41,640 AII think you said it, AI is a technology. 176 00:11:41,640 --> 00:11:44,760 So we need to think about it in terms of it is another kind of 177 00:11:44,760 --> 00:11:48,960 technology that's heavily dependent on data and it 178 00:11:48,960 --> 00:11:54,560 produces more or less data with some sort of context. 179 00:11:54,720 --> 00:11:59,120 So as I'm hearing you, what I'm thinking is the risk I think 180 00:11:59,120 --> 00:12:04,240 expand with AI because the risk now are what does it do with 181 00:12:04,240 --> 00:12:09,400 data that is not good. It can make poor decisions which 182 00:12:09,440 --> 00:12:15,320 if left unchecked by a human could result in risk or real 183 00:12:15,320 --> 00:12:21,880 trouble in organizations. Is that is that kind of is that 184 00:12:21,880 --> 00:12:24,440 that also the domain of cybersecurity? 185 00:12:24,440 --> 00:12:29,600 The things that the outcomes that happen because of AII. 186 00:12:31,480 --> 00:12:34,920 Think that's again, what do you say? 187 00:12:34,920 --> 00:12:38,160 It's, it's sitting on the fence because that's again a big 188 00:12:38,160 --> 00:12:42,640 discussion who owns the responsibility of AI and that, 189 00:12:42,640 --> 00:12:46,240 that's again, probably many governments are also kind of 190 00:12:46,240 --> 00:12:49,640 trying to bring in governances across. 191 00:12:49,880 --> 00:12:53,600 But in terms of risk itself, definitely that is that is a 192 00:12:53,600 --> 00:12:55,800 risk. And that probably, I think as 193 00:12:55,800 --> 00:13:00,080 business analysts, we should be raising that because identifying 194 00:13:00,080 --> 00:13:04,200 those risks is part of our analysis, whether it is the 195 00:13:04,200 --> 00:13:09,680 quality, data quality or even the biases within the algorithms 196 00:13:09,680 --> 00:13:14,200 or even even to kind of understand what are those 197 00:13:14,200 --> 00:13:17,800 decisions made of. Because today I'm sure you might 198 00:13:17,800 --> 00:13:24,000 have also heard there are many false positives and we'll have 199 00:13:24,120 --> 00:13:27,840 people are or the teams are dealing with it, which I think 200 00:13:27,840 --> 00:13:32,400 from the threat and being, you know, the fatigue today we are 201 00:13:32,400 --> 00:13:35,360 calling it as alert fatigue or something like that. 202 00:13:36,160 --> 00:13:38,400 That means there's, it's just being alerted. 203 00:13:38,400 --> 00:13:42,800 But again, having said that, we are in a learning curve. 204 00:13:42,800 --> 00:13:47,600 We are in an evolving phase. I think last time when I was in 205 00:13:47,600 --> 00:13:53,040 Poland, there was a session again which was talking about 206 00:13:53,760 --> 00:13:56,720 does AI ES really required for the project. 207 00:13:56,720 --> 00:14:00,120 So basically the research that was shared was it was actually 208 00:14:00,120 --> 00:14:04,840 surprising 95 of the projects had actually failed. 209 00:14:06,160 --> 00:14:10,040 But I think that is again the learning curve because we are 210 00:14:10,040 --> 00:14:13,520 trying to like as I said in the beginning, we have this 211 00:14:13,520 --> 00:14:16,040 incredible tool, but we are not prepared for it. 212 00:14:16,040 --> 00:14:19,720 When I say we're not prepared for it is we first of all don't 213 00:14:19,720 --> 00:14:22,360 have complete understanding of what it is and what the 214 00:14:22,360 --> 00:14:25,040 capabilities are and how to use it and how not to use it. 215 00:14:25,440 --> 00:14:28,560 So which means it's any new shiny gadget that's out there, 216 00:14:28,560 --> 00:14:30,640 it becomes a silver bullet to solve everything. 217 00:14:30,960 --> 00:14:35,560 And if one company is doing it's everybody has to ride the wave, 218 00:14:35,560 --> 00:14:38,880 the AI wave which is happening. So that's again another added 219 00:14:39,160 --> 00:14:42,360 reason as well why everybody are just jumping on it. 220 00:14:42,680 --> 00:14:47,160 But that's exactly where from ABA perspective we can raise 221 00:14:47,160 --> 00:14:51,560 those risks. We can even identify whether AI 222 00:14:51,560 --> 00:14:54,600 could really add any value like with any other things that we 223 00:14:54,600 --> 00:14:58,320 analyze, whatever the business objective that we want to 224 00:14:58,320 --> 00:15:03,200 deliver, the way we deliver, we provide options, we suggest 225 00:15:03,200 --> 00:15:05,520 what's the best option. So again, if we are going with 226 00:15:05,520 --> 00:15:08,560 AI, is it really viable? Is it required? 227 00:15:08,560 --> 00:15:12,840 Are we going to really you know, or is it just something 228 00:15:12,840 --> 00:15:14,400 everybody wants to do? We want to do it. 229 00:15:15,000 --> 00:15:17,480 I think that's again, the industry is kind of maturing 230 00:15:17,480 --> 00:15:21,840 because many of them are learning from the failures and 231 00:15:21,840 --> 00:15:25,000 there are good stories as well and there are not so good 232 00:15:25,000 --> 00:15:29,960 learning stories as well. But in to give you definitely 233 00:15:29,960 --> 00:15:33,440 one of the good stories is I think it is Microsoft defender 234 00:15:33,440 --> 00:15:36,880 or not and I'm not too sure. I think probably Defender, they 235 00:15:36,880 --> 00:15:42,640 are kind of AI. Their AI is actually going 236 00:15:42,640 --> 00:15:49,400 through triage chink, probably 7578 trillion security alerts in 237 00:15:49,400 --> 00:15:52,000 a day. It could be from emails, 238 00:15:52,000 --> 00:15:56,400 passwords, laptops, endpoints of IoT, cloud doesn't matter. 239 00:15:56,720 --> 00:16:02,760 So basically it's just try to check every second, every .24 by 240 00:16:02,760 --> 00:16:07,400 7. And this is actually humanely 241 00:16:07,400 --> 00:16:12,240 not possible at all. And the response time, the 242 00:16:12,280 --> 00:16:14,360 alert. And one of the the fascinating 243 00:16:14,360 --> 00:16:19,840 thing which for me was in this particular blog was today, you 244 00:16:19,840 --> 00:16:24,120 know, like when we are providing a solution, like for example, if 245 00:16:24,560 --> 00:16:27,480 in our organization there's an attack or if there's a malware, 246 00:16:27,480 --> 00:16:31,000 we kind of try to, you know, fix it locally within our 247 00:16:31,000 --> 00:16:34,680 organization. But now with Defender kind of 248 00:16:34,960 --> 00:16:38,840 applications or organizations when because it's on the cloud 249 00:16:38,840 --> 00:16:43,400 when they identify within minutes, they just not sorted 250 00:16:43,400 --> 00:16:46,440 there, but it's across all the connected systems globally. 251 00:16:46,800 --> 00:16:50,960 So that is the actually wonder of AI to actually speak. 252 00:16:50,960 --> 00:16:53,880 So because it does things instantly. 253 00:16:54,040 --> 00:17:00,000 And I think with with that this a lot of damage that can be 254 00:17:00,080 --> 00:17:04,800 avoided in terms of financially or you know the reputation as 255 00:17:04,800 --> 00:17:08,319 well. So yeah, it's again how we use 256 00:17:08,319 --> 00:17:13,240 it, but definitely something that risk worth talking about. 257 00:17:13,839 --> 00:17:21,560 Yeah, I, I am struck by how quickly many of us trust AI even 258 00:17:21,560 --> 00:17:26,160 though we don't understand how it does what it does. 259 00:17:26,760 --> 00:17:30,040 And I think therein lies a really significant risk and 260 00:17:30,040 --> 00:17:33,520 maybe why some of these projects, these AI projects are 261 00:17:33,520 --> 00:17:35,840 failing. I think I was reading an article 262 00:17:35,840 --> 00:17:40,480 this morning from in a, in a Business Journal about the 263 00:17:40,480 --> 00:17:44,080 majority of agentic AI projects are failing. 264 00:17:44,280 --> 00:17:48,960 And I suspect it is because we go in with really high hopes of 265 00:17:48,960 --> 00:17:51,320 all of the amazing things that it's going to be able to 266 00:17:51,320 --> 00:17:54,880 automate only to get a bunch of outcomes that we can't, we don't 267 00:17:54,880 --> 00:17:57,600 understand or that aren't really beneficial. 268 00:17:58,360 --> 00:18:00,800 So there's, you know, there's that. 269 00:18:01,360 --> 00:18:08,520 And I wonder if as as AI and cybersecurity are, are mashing 270 00:18:08,520 --> 00:18:13,680 together, how much we need to change our own attitudes about 271 00:18:14,720 --> 00:18:18,800 about the about the technology of AI and how we think about 272 00:18:18,800 --> 00:18:22,880 risk rather than just focusing solely on the technology. 273 00:18:22,880 --> 00:18:25,840 I almost see that we need to kind of check ourselves. 274 00:18:28,080 --> 00:18:31,120 I know when you were here last we talked about reflection. 275 00:18:31,280 --> 00:18:35,360 And so I almost wonder if that's not the thing that we need to be 276 00:18:35,360 --> 00:18:40,280 doing as we are getting started with these AI initiatives. 277 00:18:43,360 --> 00:18:45,640 You touched upon the word reflection, which I wasn't 278 00:18:46,080 --> 00:18:49,880 prepared, but something that's always, you know, I mean, I can 279 00:18:49,880 --> 00:18:51,480 go on. That's what they say even though 280 00:18:51,480 --> 00:18:56,080 I'm not. But I can go on because again, 281 00:18:56,080 --> 00:19:00,560 when we are talking so much within the AI space, Susan, I 282 00:19:00,560 --> 00:19:07,200 feel somewhere because it's kind of knocking everybody's or world 283 00:19:07,200 --> 00:19:10,400 in one way or the other. It could be in good way or it 284 00:19:10,400 --> 00:19:15,600 could be in shocking ways with you know, jobs and all the fear 285 00:19:15,600 --> 00:19:17,440 mongering things. That's how that that is 286 00:19:17,440 --> 00:19:20,680 happening there. But somewhere I always feel that 287 00:19:20,680 --> 00:19:25,040 shift is happening. And if you look back through the 288 00:19:25,040 --> 00:19:30,560 history, every time when things have happened like this, it has 289 00:19:30,560 --> 00:19:37,920 been a point where we as the community or the humanity have 290 00:19:37,920 --> 00:19:41,120 to take stock of where we are going. 291 00:19:41,120 --> 00:19:46,960 It's like, you know, we are on a journey and even on a project we 292 00:19:46,960 --> 00:19:51,080 kind of map out what are the benefits and how do we know we 293 00:19:51,080 --> 00:19:53,920 are on the particular journey. So similarly, I think somewhere 294 00:19:54,320 --> 00:19:58,640 these points really make us reflect on where we are going, 295 00:19:59,160 --> 00:20:03,600 somewhere do we need to pivot accordingly and adapting is 296 00:20:03,600 --> 00:20:07,000 something. Which is a nature law of nature, 297 00:20:07,000 --> 00:20:11,120 which nobody can actually escape it, which is, which is in a good 298 00:20:11,120 --> 00:20:13,000 way because that that's how we grow. 299 00:20:13,720 --> 00:20:16,960 I think after a certain age, if not physically, mental growth is 300 00:20:16,960 --> 00:20:19,720 continuing no matter what the age is. 301 00:20:20,160 --> 00:20:25,560 So again, these are those moments which really brings 302 00:20:25,600 --> 00:20:32,160 reflection to the forefront because as BAS, we are not 303 00:20:32,720 --> 00:20:36,760 getting bogged down into the day-to-day work of writing. 304 00:20:36,920 --> 00:20:41,640 Not many things or trying to do, you know, the normal grind work 305 00:20:41,960 --> 00:20:45,520 so we can take the support of technology or the AI 306 00:20:45,520 --> 00:20:47,520 specifically for all the grind work. 307 00:20:47,920 --> 00:20:51,680 And it really gives us that space or the mental space to 308 00:20:51,680 --> 00:20:56,200 actually think what it is. And that's exactly why I feel 309 00:20:56,280 --> 00:21:01,480 very strongly that as BAS we don't lose our job. 310 00:21:01,480 --> 00:21:04,560 Probably we, we will be evolving, which again, like 311 00:21:04,560 --> 00:21:08,640 looking back two decades, when I started my career, I was working 312 00:21:08,640 --> 00:21:13,560 on a single laptop as, as, as a sorry, not even laptop, the 313 00:21:13,560 --> 00:21:16,400 desktop that everything was happening, then everything 314 00:21:16,400 --> 00:21:18,440 connected. And when everything started 315 00:21:18,440 --> 00:21:20,760 connecting, the way we do business changed. 316 00:21:21,120 --> 00:21:23,920 So which means every time that there has been a constant 317 00:21:23,920 --> 00:21:27,480 adaptation to the new changing landscape. 318 00:21:27,480 --> 00:21:31,600 And I think that's very easy for business analysts because we 319 00:21:31,680 --> 00:21:34,440 somewhere, our mindset is we are all constant learners. 320 00:21:34,440 --> 00:21:38,680 Otherwise we wouldn't be ABA because every role demands 321 00:21:38,680 --> 00:21:43,280 different, you know, tools, skill set and we need to think, 322 00:21:43,280 --> 00:21:45,920 we need to have our intellectual capabilities always up high. 323 00:21:46,360 --> 00:21:48,280 So that means we are lifelong learners. 324 00:21:48,280 --> 00:21:51,480 So again here I think this is giving us this opportunity. 325 00:21:52,080 --> 00:21:55,680 And when we talk about human in the loop, I always see ABA to be 326 00:21:55,680 --> 00:22:00,320 that person or somebody with the BA skills who has to provide 327 00:22:00,320 --> 00:22:02,960 that context. Because when we talk about AI, 328 00:22:02,960 --> 00:22:07,000 again, talking about like interns, they again when when 329 00:22:07,000 --> 00:22:13,440 you say AI, it can, it is fast, it is efficient, but it doesn't 330 00:22:13,440 --> 00:22:16,480 know the culture, it doesn't know the ethics, it doesn't know 331 00:22:16,480 --> 00:22:18,320 the right or wrong or good or bad. 332 00:22:19,360 --> 00:22:22,760 It can only learn something is right or wrong or something is 333 00:22:22,760 --> 00:22:26,560 normal or abnormal based on the data that it it or it is 334 00:22:26,560 --> 00:22:29,440 learning. So these are some of the context 335 00:22:29,440 --> 00:22:34,680 that as BS we should be providing and working with the 336 00:22:34,680 --> 00:22:38,120 team. So you know, like we say, BS are 337 00:22:38,120 --> 00:22:41,880 the bridge between the business and the technology. 338 00:22:41,880 --> 00:22:44,640 But today this technology has kind of expanded. 339 00:22:44,640 --> 00:22:49,680 So that means the bridge is also kind of widened where we not 340 00:22:49,680 --> 00:22:52,880 only deal with or not only collaborate with the business 341 00:22:52,920 --> 00:22:54,880 and technology. Now it's wide. 342 00:22:55,120 --> 00:22:59,040 It could be data, it could be security, it could be AI, it 343 00:22:59,040 --> 00:23:03,320 could be ethics, it could be when we say compliance, there 344 00:23:03,320 --> 00:23:06,400 are multiple levels of compliances here again. 345 00:23:06,680 --> 00:23:13,520 So as B, as I think we now have to be more efficient in terms of 346 00:23:14,400 --> 00:23:19,880 not the quantity but the quality where we can collaborate with 347 00:23:19,960 --> 00:23:23,080 all of these different widened horizon. 348 00:23:23,720 --> 00:23:28,600 So somewhere I think their adaptability comes and to do all 349 00:23:28,600 --> 00:23:31,320 of to understand all of this, going back to the point of 350 00:23:31,320 --> 00:23:35,200 reflection is something that when you sit back, you know, 351 00:23:35,200 --> 00:23:37,840 like watching a movie and you see what how exactly the things 352 00:23:37,840 --> 00:23:40,000 are happening, then you get that understanding. 353 00:23:40,360 --> 00:23:45,640 So definitely, definitely, I find reflection more than ever. 354 00:23:45,640 --> 00:23:53,160 And just to add another point to this here's competing with AI is 355 00:23:53,160 --> 00:23:57,960 foolishness. Competing in what way? 356 00:23:58,040 --> 00:24:01,360 Like us trying to be as good as or as efficient? 357 00:24:01,360 --> 00:24:02,480 Exactly. Exactly. 358 00:24:02,480 --> 00:24:04,800 Because that is, that is not possible. 359 00:24:04,800 --> 00:24:07,520 And that is definite. First of all, let me say 360 00:24:07,640 --> 00:24:11,920 competing itself is wrong in any sense, right? 361 00:24:11,920 --> 00:24:15,120 Right. Because I think by nature we are 362 00:24:15,120 --> 00:24:19,120 all enough complete in our own ways. 363 00:24:19,480 --> 00:24:23,560 Just by competing, we identify the shortcomings, which are 364 00:24:23,560 --> 00:24:26,760 actually not the shortcomings. So basically, competing itself 365 00:24:26,760 --> 00:24:29,840 is wrong, but competing with the wrong competitor is even 366 00:24:29,840 --> 00:24:33,080 stupidity. So there's no point. 367 00:24:33,080 --> 00:24:36,760 But On the contrary, I think we need to focus on what exactly 368 00:24:36,760 --> 00:24:41,160 makes us human. So bringing that ethical compass 369 00:24:41,160 --> 00:24:47,120 or bringing that empathy, bringing those fairness in what 370 00:24:47,120 --> 00:24:50,320 we do is something very much important. 371 00:24:50,560 --> 00:24:53,680 So along with that, as you can see, there are so many different 372 00:24:53,680 --> 00:24:58,720 new roles that are shaping up in these areas as well, which again 373 00:24:58,760 --> 00:25:03,160 will need business analysis skills, our core skills. 374 00:25:03,720 --> 00:25:07,960 So I think there again, that there's a lot of scope for that. 375 00:25:08,120 --> 00:25:13,320 So definitely reflection is something going and and starting 376 00:25:14,040 --> 00:25:16,160 or just trying to see who we are. 377 00:25:16,160 --> 00:25:21,480 And from that space, you know, operating is I find it much more 378 00:25:22,080 --> 00:25:27,640 comfortable, confident and not being worked out of whatever the 379 00:25:27,640 --> 00:25:29,600 things that's that is happening surround. 380 00:25:31,720 --> 00:25:35,320 I am. So here's here's kind of what 381 00:25:35,320 --> 00:25:40,280 you're making me think about. So we've we I think we all 382 00:25:40,280 --> 00:25:42,880 understand the the strengths of AI. 383 00:25:42,880 --> 00:25:46,520 You've said it's, it's fast, it's efficient, it can make very 384 00:25:46,520 --> 00:25:48,920 quick decisions. Those are those are good things 385 00:25:48,920 --> 00:25:52,840 and things we can't quote, UN quote compete with, but it has 386 00:25:52,840 --> 00:25:56,200 lots of weaknesses and you've you've talked about some of 387 00:25:56,200 --> 00:26:00,880 those. I think that instead of us as 388 00:26:00,880 --> 00:26:04,440 business analysis professionals getting really worried about 389 00:26:04,440 --> 00:26:09,800 losing our jobs, I think there's a lot of opportunity for us in 390 00:26:09,800 --> 00:26:13,760 those things that you've identified as weaknesses of AI. 391 00:26:15,200 --> 00:26:23,040 So, you know, making decisions with, with bad or flawed data. 392 00:26:23,080 --> 00:26:25,400 I think there's an opportunity for business analysis 393 00:26:25,400 --> 00:26:29,640 professionals to help to, you know, make sure that data is 394 00:26:29,640 --> 00:26:32,800 structured well, is accurate, is of good quality. 395 00:26:32,920 --> 00:26:35,360 I think there's opportunities for business analysis 396 00:26:35,360 --> 00:26:40,600 professionals in validating decisions that are coming out of 397 00:26:40,600 --> 00:26:44,640 this and not necessarily scrutinizing the details of that 398 00:26:44,640 --> 00:26:48,840 decision, but also playing out the impacts of those decisions 399 00:26:49,240 --> 00:26:53,600 forward because AI can't do that, or at least not do that 400 00:26:53,600 --> 00:26:56,720 well. Are there other weaknesses of AI 401 00:26:56,720 --> 00:26:59,280 that you see as opportunities for business analysis 402 00:26:59,280 --> 00:27:04,800 professionals to step forward? You mentioned two things and I 403 00:27:04,800 --> 00:27:09,840 would just like to dive into a little bit deeper so that we we 404 00:27:09,840 --> 00:27:13,080 really get the sense of it. One thing you mentioned was 405 00:27:13,080 --> 00:27:15,800 data. In fact, I was just having a 406 00:27:15,800 --> 00:27:22,000 conversation the other day where we were talking about data being 407 00:27:22,000 --> 00:27:26,160 the engine and probably, you know, Terry Barish. 408 00:27:26,480 --> 00:27:32,000 So he works in the data space. So he has actually identified 409 00:27:34,200 --> 00:27:37,160 data readiness framework. So we were actually going 410 00:27:37,160 --> 00:27:40,880 through that in detail. So there's a lot of work that 411 00:27:40,880 --> 00:27:44,360 BAS I can do when it comes to preparing data. 412 00:27:44,680 --> 00:27:48,360 It is just not structuring. So there there's a lot of data 413 00:27:48,360 --> 00:27:52,080 hygiene that happens. So every aspect of it, every 414 00:27:52,080 --> 00:27:54,080 step of that particular framework, as we were 415 00:27:54,080 --> 00:27:58,280 discussing, there is a core BA work that that goes into it, the 416 00:27:58,280 --> 00:28:01,440 BA activities that go into it, the BA mindset that goes into 417 00:28:01,440 --> 00:28:04,480 it. So there is again, yeah. 418 00:28:04,480 --> 00:28:08,520 So that is one thing probably as BAS, we can definitely 419 00:28:08,800 --> 00:28:13,160 contribute to the topic. And the other thing you 420 00:28:13,160 --> 00:28:16,960 mentioned was impact. I think, Susan, that is really, 421 00:28:16,960 --> 00:28:22,720 really huge for, you know, the research I was doing when I was 422 00:28:22,720 --> 00:28:28,600 writing the book, one of the examples that I came across when 423 00:28:28,600 --> 00:28:31,960 it when it, I think it's relating to ethics anyway, the, 424 00:28:31,960 --> 00:28:36,480 the, the story, not the story. The, The thing is, the situation 425 00:28:36,480 --> 00:28:42,520 here is when Facebook decided to have a like button, the whole 426 00:28:42,520 --> 00:28:45,280 intention was to kind of spread joy. 427 00:28:46,000 --> 00:28:49,000 So they wanted that somebody, you know, when they like it's, 428 00:28:49,000 --> 00:28:54,120 it's basically spreading joy, but they never ever anticipated 429 00:28:54,120 --> 00:28:57,880 the negative impacts of that. They never thought that that 430 00:28:57,880 --> 00:29:03,600 could be the cause of, you know, teenage suicide as well or any 431 00:29:03,600 --> 00:29:07,840 other the, the, the negative impacts of it. 432 00:29:08,480 --> 00:29:14,440 So again, I always share this as well when today it's just not 433 00:29:14,440 --> 00:29:16,440 the positive impacts that we have to see. 434 00:29:16,800 --> 00:29:20,720 We also have to see the negative impacts and again at different 435 00:29:20,720 --> 00:29:24,480 levels, whether what is the impact positive and negative 436 00:29:24,480 --> 00:29:27,360 when it comes to the project or the value that we are delivering 437 00:29:27,360 --> 00:29:31,600 for a particular stakeholder. And the impacts of the same 438 00:29:31,600 --> 00:29:36,160 thing at the organization level or the society level or even the 439 00:29:36,280 --> 00:29:39,280 ecosystem, because today everything is so very 440 00:29:39,280 --> 00:29:45,760 interconnected, we will actually not know much ahead in advance 441 00:29:46,640 --> 00:29:50,080 what the impacts would be and how this particular feature can 442 00:29:50,080 --> 00:29:52,240 actually, you know, shape up into. 443 00:29:52,760 --> 00:29:57,280 So as much as the analysis goes, that's exactly where I find 444 00:29:57,280 --> 00:29:59,440 this. There's a lot of value that BS 445 00:29:59,440 --> 00:30:03,800 can bring in when they are doing that. 446 00:30:05,280 --> 00:30:09,280 I always say to kind of, you know, we BS were like, you know, 447 00:30:09,320 --> 00:30:11,920 or we, we were conducting the orchestra. 448 00:30:12,320 --> 00:30:17,360 So right now with instead of two or three instruments, now we 449 00:30:17,360 --> 00:30:22,160 have a lot more instruments. So no matter you just take any 450 00:30:22,160 --> 00:30:24,720 particular aspect or any particular team that we are 451 00:30:24,720 --> 00:30:29,120 working, there is that we work. And I mean, I always feel 452 00:30:29,120 --> 00:30:31,560 there's a lot to do now with the expansion. 453 00:30:32,040 --> 00:30:35,040 In fact, we need more BAS because it's expanded. 454 00:30:35,920 --> 00:30:41,480 We as individual BAS have to definitely we need to step up, 455 00:30:42,160 --> 00:30:48,000 we need to learn, study, whatever it is, upscale 456 00:30:48,000 --> 00:30:51,760 ourselves so that we are ready to actually kind of conduct 457 00:30:51,760 --> 00:30:55,760 because definitely in the new landscape, we cannot be experts 458 00:30:55,760 --> 00:31:00,120 in all of those. So focusing on anyone niche, I 459 00:31:00,120 --> 00:31:03,760 think now is the time. And that's exactly what PS can 460 00:31:03,760 --> 00:31:07,600 really, really contribute. And, and I think we can also 461 00:31:07,640 --> 00:31:15,280 advocate for what good use of AI would look like to avoid some of 462 00:31:15,280 --> 00:31:18,680 this cybersecurity slash security. 463 00:31:18,680 --> 00:31:21,160 Maybe it's all cybersecurity these days. 464 00:31:21,320 --> 00:31:24,160 Any kind of security is cybersecurity really. 465 00:31:24,960 --> 00:31:28,960 But I think we can be advocates in our organizations for what 466 00:31:28,960 --> 00:31:34,160 good business analysis for AI can look like and maybe should 467 00:31:34,160 --> 00:31:38,800 look like, because I think, yeah, go ahead. 468 00:31:39,800 --> 00:31:42,520 No, no, I was just saying maybe that is something as IIBA we 469 00:31:42,520 --> 00:31:46,680 should think about it to bring in some yeah, ethical, well 470 00:31:46,720 --> 00:31:50,240 links to it. You know, there's actually on 471 00:31:50,240 --> 00:31:55,800 our website Fabricio Laguna and Michael Lagello have produced a 472 00:31:56,440 --> 00:32:00,360 kind of an opening statement on what business analysis for AI 473 00:32:00,360 --> 00:32:03,760 would look like. So I don't I don't have a link 474 00:32:03,760 --> 00:32:09,240 and I don't know if either of my my marketing Co workers are on, 475 00:32:09,240 --> 00:32:12,200 but if you guys are on, if you can drop in that business 476 00:32:12,200 --> 00:32:15,480 analysis for AI link. If not, I'll make sure that it's 477 00:32:15,480 --> 00:32:17,840 in the in the description of the YouTube. 478 00:32:18,880 --> 00:32:22,800 Somebody also on the comments were asking the link for the 479 00:32:22,800 --> 00:32:26,040 book, so maybe? If you have that. 480 00:32:26,920 --> 00:32:28,480 Yes. Perfect. 481 00:32:28,520 --> 00:32:33,360 I was trying to find it but it it was, I don't know, Google 482 00:32:33,360 --> 00:32:38,880 wasn't behaving for me today. Again, if, if, if they can go 483 00:32:38,880 --> 00:32:42,120 through the link on my website, they get an additional author 484 00:32:42,120 --> 00:32:46,160 discount 30%, which is added benefit rather than going 485 00:32:46,200 --> 00:32:48,600 directly. So that probably would help. 486 00:32:49,160 --> 00:32:52,000 So let me see if I can. I think it's asking me to 487 00:32:52,000 --> 00:32:54,360 connect to the YouTube account to join the chat. 488 00:32:55,080 --> 00:32:57,400 Yeah, let me see. Yeah, when I'm done, I will do 489 00:32:57,400 --> 00:32:58,760 it. Otherwise I can share it with 490 00:32:58,760 --> 00:33:00,080 you. Yeah, yeah. 491 00:33:00,080 --> 00:33:02,000 And then I can include it in the description. 492 00:33:02,760 --> 00:33:04,040 Yes. Well, let's see. 493 00:33:04,040 --> 00:33:09,560 I tell you what, we have sparked a whole lot of conversation over 494 00:33:09,560 --> 00:33:12,560 in our comments. I've I have starred a couple. 495 00:33:12,960 --> 00:33:15,120 I'm just going to, I'm just going to put some of these up. 496 00:33:15,120 --> 00:33:16,640 So you mentioned this was the run. 497 00:33:16,640 --> 00:33:20,480 Rabilani was asking about the the details of the book. 498 00:33:20,640 --> 00:33:24,520 We will get you those. Yeah, I have sent it to you on 499 00:33:24,520 --> 00:33:26,280 the private chat, so maybe whenever. 500 00:33:26,280 --> 00:33:28,080 Yeah, you can add it to the comments. 501 00:33:28,600 --> 00:33:33,400 I I can drop this into the YouTube chat, but I will make 502 00:33:33,400 --> 00:33:38,560 sure that we also drop it in the LinkedIn chat separately. 503 00:33:38,560 --> 00:33:41,320 So there you go Rob Alani, I will respond to you and make 504 00:33:41,320 --> 00:33:45,480 sure that you have that Thank you Let's see so here we have 505 00:33:45,480 --> 00:33:49,000 another human factors in intrigue me as we become too 506 00:33:49,000 --> 00:33:52,120 dependent on AI and lose some of the human elements. 507 00:33:52,480 --> 00:33:57,720 I think that is, I think that is maybe what you see organizations 508 00:33:57,720 --> 00:34:01,920 experiencing right now is they are, boy, we've got a harness 509 00:34:02,000 --> 00:34:07,120 AI, it's going to make a magic happen and then they just trust 510 00:34:07,120 --> 00:34:11,480 it and rely on it too much and don't really get the outcomes 511 00:34:11,480 --> 00:34:14,719 they're looking for. Yeah. 512 00:34:14,880 --> 00:34:18,080 And I think even at an individual level as well, the 513 00:34:18,080 --> 00:34:23,199 more we are relying on it, the more we are losing our 514 00:34:23,199 --> 00:34:26,840 independence. Yeah, I agree. 515 00:34:27,280 --> 00:34:29,400 Yeah, I agree. Or the ability to think for 516 00:34:29,400 --> 00:34:31,320 ourselves. That's what's scary. 517 00:34:31,600 --> 00:34:35,719 Absolutely. I, I myself am experiencing it 518 00:34:36,120 --> 00:34:41,120 that whenever I used to write emails somewhere, you know, I 519 00:34:41,120 --> 00:34:45,560 used to spend the time and go through it, but now I'm finding 520 00:34:45,560 --> 00:34:49,239 it, I'm losing it because I get the help of AI. 521 00:34:50,199 --> 00:34:54,000 But one point which I would like to highlight here when, when we 522 00:34:54,000 --> 00:34:58,800 talk about dependency, Susan is we are, this is again, as part 523 00:34:58,800 --> 00:35:04,200 of the risk workshop that I do, I always highlight normally when 524 00:35:04,200 --> 00:35:09,960 I talk in the workshops or my trainings, we go through a 525 00:35:09,960 --> 00:35:13,440 fantastic risk exercise. It's, it's fun and fantastic. 526 00:35:13,840 --> 00:35:20,400 But one thing that all of us miss is recognizing some of 527 00:35:20,400 --> 00:35:26,760 those fundamental risks, existential risks, because we 528 00:35:26,760 --> 00:35:30,000 think that cannot happen. And one simple example is 529 00:35:30,960 --> 00:35:35,040 unavailability of Internet. Right. 530 00:35:35,480 --> 00:35:40,040 We can never fathom that in our minds that this is a 531 00:35:40,040 --> 00:35:45,800 possibility, but we are experiencing, we are seeing. 532 00:35:45,800 --> 00:35:50,360 I think yesterday day before AWS was down South, Snapchat was not 533 00:35:50,360 --> 00:35:53,920 available and I know my children didn't know how to communicate 534 00:35:54,240 --> 00:35:56,480 with their friends. But I was. 535 00:35:57,280 --> 00:36:00,000 I was. I was having a laugh. 536 00:36:01,680 --> 00:36:06,680 So what would the point I'm trying to make here is, and we 537 00:36:06,680 --> 00:36:10,960 also have our seeing and probably in the last few months 538 00:36:10,960 --> 00:36:15,440 itself, we have heard if we would experience first hand that 539 00:36:15,480 --> 00:36:19,440 there has been some fires here, some unavailability of this 540 00:36:19,440 --> 00:36:21,360 technology. That technology and I know in 541 00:36:21,360 --> 00:36:26,320 the UK itself, repetitively, I think two or three months the 542 00:36:26,320 --> 00:36:31,480 banking systems were down, the payment or the wages wasn't paid 543 00:36:31,480 --> 00:36:36,400 on time, people couldn't honour their direct debits, it impacted 544 00:36:36,400 --> 00:36:39,360 their credit reports. So the whole point I'm trying to 545 00:36:39,360 --> 00:36:42,920 make here is some of those things where we think that this 546 00:36:42,920 --> 00:36:45,080 cannot break. It can break. 547 00:36:46,520 --> 00:36:47,080 That's. Right. 548 00:36:47,080 --> 00:36:51,400 So dependence because today our lives are completely technology 549 00:36:51,400 --> 00:36:53,800 oriented. I think that some people even 550 00:36:53,800 --> 00:36:56,680 wear their watches when they sleep because they want to 551 00:36:56,680 --> 00:36:58,960 monitor even that. And I know one of my children 552 00:36:58,960 --> 00:37:03,360 does that. So the point here is the more we 553 00:37:03,360 --> 00:37:07,760 are dependent, the more we will become helpless when that is not 554 00:37:07,760 --> 00:37:09,960 available. And when I say helpless, it 555 00:37:09,960 --> 00:37:12,400 could be physical, it could be mental, it could be emotional. 556 00:37:13,560 --> 00:37:17,040 And that is where as BAS, you know, like when we talk about, 557 00:37:17,600 --> 00:37:22,080 when we analyse stuff, I think we have to analyse all aspects 558 00:37:22,080 --> 00:37:26,880 today somewhere, you know, our, we have to take those guards and 559 00:37:26,880 --> 00:37:33,000 just look at 365° to actually assess the impacts and analyse 560 00:37:33,000 --> 00:37:35,520 things. Yeah, because because you're 561 00:37:35,560 --> 00:37:40,440 right, it is not just limited to the tool, the organization, the 562 00:37:40,440 --> 00:37:44,160 problem that you're solving, you really have to go beyond that. 563 00:37:44,160 --> 00:37:47,880 And again, I think that's, you know, one of the weaknesses that 564 00:37:47,880 --> 00:37:50,960 we have in our organizations is we really, we've got a lot of 565 00:37:50,960 --> 00:37:54,760 people who are focused on whatever that project outcome 566 00:37:54,760 --> 00:37:58,280 is, you know, reduced loss, increased profit, like whatever 567 00:37:58,280 --> 00:38:03,400 those outcomes are, but at the expense of not sometimes seeing 568 00:38:03,440 --> 00:38:08,080 the problems that are existential that could result in 569 00:38:08,320 --> 00:38:13,640 greater harms to humanity. Yeah, I think probably if we 570 00:38:13,640 --> 00:38:17,000 could learn from COVID or the current wars that's happening 571 00:38:17,000 --> 00:38:20,680 where we didn't even anticipate. But I'm, I'm really glad that 572 00:38:20,680 --> 00:38:23,120 you mentioned this point because this is again one of the common 573 00:38:23,120 --> 00:38:27,040 questions that I get is, you know, we actually don't have 574 00:38:27,040 --> 00:38:30,800 time for analysis and you tell us to do an analyse analysis for 575 00:38:30,800 --> 00:38:34,080 all these levels. When is the time or even if we 576 00:38:34,080 --> 00:38:37,200 do, is it really required because like as you identified, 577 00:38:37,200 --> 00:38:40,200 you know, OK, what's what, what's the benefit that is great 578 00:38:40,200 --> 00:38:43,280 monetary benefit that it's going to serve or what's the profit if 579 00:38:43,280 --> 00:38:48,440 the focus is on that? But then again, I talk about as 580 00:38:48,440 --> 00:38:54,000 ABA, business analysis cannot be seen just as a role. 581 00:38:54,320 --> 00:38:57,320 I always say business analysis is a responsibility. 582 00:38:58,440 --> 00:39:03,400 So whether the organization definitely takes in those points 583 00:39:03,400 --> 00:39:06,520 or not, easy material because I am not the decision maker. 584 00:39:06,520 --> 00:39:11,560 As a business analyst, however, I have a responsibility to 585 00:39:11,560 --> 00:39:15,000 deliver accurate incomplete analysis and that's where my job 586 00:39:15,000 --> 00:39:19,000 stops. So not giving that or giving an 587 00:39:19,040 --> 00:39:22,160 incomplete analysis means I haven't actually done my job 588 00:39:22,160 --> 00:39:26,960 well as a business analyst. So that is where we draw the 589 00:39:26,960 --> 00:39:33,280 line that no matter how the organization sees our business 590 00:39:33,280 --> 00:39:37,720 case for example, we delivering a complete analysis is our job. 591 00:39:38,640 --> 00:39:42,640 Again, the analogies I kind of use is when you go to a doctor, 592 00:39:43,400 --> 00:39:45,960 you believe that whatever you know, the prescribed 593 00:39:45,960 --> 00:39:49,080 prescription or the diagnosis the doctor does is accurate. 594 00:39:49,600 --> 00:39:53,240 So I always feel there is an undocumented trust between the 595 00:39:53,240 --> 00:39:56,560 business analysts and their clients or organization that our 596 00:39:56,560 --> 00:40:00,040 analysis is complete and accurate and that because they 597 00:40:00,040 --> 00:40:01,840 are making decisions based on that. 598 00:40:02,200 --> 00:40:07,360 So maybe out of 10 people, one person or two people might 599 00:40:07,360 --> 00:40:11,960 actually pick up those ethical points or those existential 600 00:40:11,960 --> 00:40:14,040 crisis or the risk that you have raised. 601 00:40:14,040 --> 00:40:16,760 But who knows? That can be a game changer for 602 00:40:16,760 --> 00:40:20,120 the organization. So I think somewhere we cannot 603 00:40:20,120 --> 00:40:25,040 take a reason that if somebody is not taking our analysis, that 604 00:40:25,040 --> 00:40:28,560 doesn't make a stop delivering a complete one. 605 00:40:29,320 --> 00:40:31,200 That's right. Just because they don't accept 606 00:40:31,360 --> 00:40:34,760 what you're providing doesn't mean that it's not valuable. 607 00:40:35,840 --> 00:40:39,760 And so, yeah, I agree with you. And you know, we've, we we've 608 00:40:39,760 --> 00:40:44,160 talked several times about risk management, that part of what 609 00:40:44,480 --> 00:40:49,760 business analysis might look like for AI looks a lot like 610 00:40:49,760 --> 00:40:52,680 risk management. And I just think back to my 611 00:40:52,720 --> 00:40:57,320 working in a PMO for a number of years and I always thought, 612 00:40:57,320 --> 00:41:00,840 well, risk management is the domain of project managers, but 613 00:41:00,840 --> 00:41:03,960 that's not true. That is really not true. 614 00:41:05,360 --> 00:41:08,160 The BABOT guide, which is right here, it's holding up my plan, 615 00:41:09,160 --> 00:41:12,520 but it's very valuable. It talks about risk management 616 00:41:12,560 --> 00:41:15,520 being part of what business analysis professionals do as 617 00:41:15,520 --> 00:41:18,000 well. So that is something that we 618 00:41:18,000 --> 00:41:21,680 should be thinking about, not just how does something work, 619 00:41:21,680 --> 00:41:25,000 how am I putting together requirements for it, How am I 620 00:41:25,200 --> 00:41:30,200 evaluating risk and letting folks know that there are these 621 00:41:30,440 --> 00:41:35,080 potential issues that we might encounter like it. 622 00:41:35,120 --> 00:41:38,080 That's part of the job. Absolutely. 623 00:41:38,080 --> 00:41:39,880 And it is just not business analyst. 624 00:41:39,880 --> 00:41:43,320 I feel it's everybody's responsibility when it comes to 625 00:41:43,320 --> 00:41:45,440 security. I always say it's just not one 626 00:41:45,440 --> 00:41:48,920 person's or it's security team. It's everybody's responsibility. 627 00:41:49,240 --> 00:41:52,120 Similarly, when it comes to risks, it doesn't matter who 628 00:41:52,120 --> 00:41:54,240 raises it or who actually works on it. 629 00:41:55,040 --> 00:41:58,360 It's, it is whoever sees it, they have to raise it. 630 00:41:58,360 --> 00:42:03,560 So it, it is like that. So, and for that, having a 631 00:42:03,560 --> 00:42:07,920 security mindset is fundamental because without that, even if 632 00:42:07,920 --> 00:42:10,840 there is a risk right in front of us, we might not be able to 633 00:42:10,840 --> 00:42:12,960 see because we don't have that lens. 634 00:42:13,480 --> 00:42:17,600 And having that security mindset really really helps to identify 635 00:42:17,600 --> 00:42:22,120 those risks because end of the day, security is nothing but all 636 00:42:22,120 --> 00:42:25,080 the risks taken care. That's right. 637 00:42:25,280 --> 00:42:28,840 So that's why I think we need to, as business analysis 638 00:42:28,840 --> 00:42:32,600 professionals, we need to stop thinking cybersecurity is 639 00:42:32,720 --> 00:42:38,360 knowing the what the threats are, how to use some technology 640 00:42:38,360 --> 00:42:43,480 in order to address those. It is, it is not just that it is 641 00:42:43,480 --> 00:42:48,000 for some professionals in that domain, but for us, it looks 642 00:42:48,000 --> 00:42:52,440 like risk management, it looks like ethics, it looks like data 643 00:42:52,440 --> 00:42:55,600 analysis, it looks like business analysis. 644 00:42:56,560 --> 00:42:57,720 Absolutely. Yeah. 645 00:42:58,640 --> 00:43:01,120 So let's see. So we've got just a couple of 646 00:43:01,120 --> 00:43:04,200 minutes left. I'm just, I'm going to pop in 647 00:43:04,200 --> 00:43:06,400 maybe a couple of other thoughts here. 648 00:43:07,840 --> 00:43:11,680 Here we have, I believe the fear of job loss to AI is natural. 649 00:43:12,200 --> 00:43:14,480 However, AI can't work in isolation. 650 00:43:14,520 --> 00:43:17,440 It's an enabler, does not have human contacts, which is still 651 00:43:17,440 --> 00:43:19,280 needed. And business analysis 652 00:43:19,280 --> 00:43:22,480 professionals provide this. Yeah, thank you. 653 00:43:22,720 --> 00:43:26,080 I think that's, that is definitely a message we want 654 00:43:26,080 --> 00:43:30,880 people to leave with today. Let's see. 655 00:43:31,480 --> 00:43:34,680 And then we've got another one here where the human factor and 656 00:43:34,680 --> 00:43:37,640 impact can never be over emphasized. 657 00:43:38,600 --> 00:43:43,520 I wish more leaders who are thinking about introducing AI or 658 00:43:43,600 --> 00:43:47,280 AI projects into their organization would think this is 659 00:43:47,280 --> 00:43:51,320 a human project. It's enabled by AI instead of 660 00:43:51,360 --> 00:43:56,080 the other way around. Yeah, absolutely. 661 00:43:56,760 --> 00:44:00,680 It just reminds me when I started my career, one of my 662 00:44:00,680 --> 00:44:04,800 seniors told me I was I was a techie, I was coding. 663 00:44:04,800 --> 00:44:10,480 So he said even though you're coding, Bindu, always remember 664 00:44:10,480 --> 00:44:13,520 you're in people's business. No matter what tool, what 665 00:44:13,520 --> 00:44:16,800 technology comes, you're always in people's business. 666 00:44:17,800 --> 00:44:21,360 That means you are doing something that is going to 667 00:44:21,360 --> 00:44:26,960 impact people or a human life. So somewhere I think that really 668 00:44:26,960 --> 00:44:29,520 stuck with me. So it doesn't matter what 669 00:44:29,520 --> 00:44:33,520 technology we use, we are trying to bring a change in a 670 00:44:33,520 --> 00:44:39,440 stakeholder's experience and that is a responsibility. 671 00:44:40,160 --> 00:44:45,240 Yes, Speaking of cyber security, so you've talked about, you do 672 00:44:45,240 --> 00:44:51,680 lots of talks and, and you are writing books in your training. 673 00:44:51,720 --> 00:44:55,600 And so something that I wanted to share with folks because it's 674 00:44:55,600 --> 00:44:58,560 Cybersecurity Awareness month. By the way, October is 675 00:44:58,560 --> 00:45:02,800 Cybersecurity Awareness Month. Every year this month we have an 676 00:45:02,800 --> 00:45:07,240 on demand webinar and it's one of your webinars. 677 00:45:07,880 --> 00:45:10,960 You did it last year. It's elevate your cybersecurity 678 00:45:10,960 --> 00:45:14,160 game. And I've got this new cool QR 679 00:45:14,160 --> 00:45:16,360 code gadget. Let's see, we're going to make 680 00:45:16,360 --> 00:45:17,600 this thing work. Here you go. 681 00:45:17,840 --> 00:45:20,800 So probably some of you are on your device. 682 00:45:20,880 --> 00:45:24,840 I wonder how this works. I have AQR code. 683 00:45:26,400 --> 00:45:30,760 If you can figure out a way to snap a picture of that, it'll 684 00:45:30,760 --> 00:45:35,240 take you over to the page and you can get this on demand 685 00:45:35,240 --> 00:45:38,600 webinar and a little bit of information about Cybersecurity 686 00:45:38,600 --> 00:45:42,760 Awareness Month. And if you aren't able to click 687 00:45:42,760 --> 00:45:45,400 the QR code, don't worry. I will make sure that I put a 688 00:45:45,400 --> 00:45:50,440 link into our chat today. Thanks for sharing this, Susan. 689 00:45:50,440 --> 00:45:55,320 I think I remember when we had it last year, probably it was 690 00:45:55,320 --> 00:45:59,680 one of the webinars, I think around probably 400 or 500 691 00:45:59,680 --> 00:46:01,920 people registered and attended something like that. 692 00:46:02,240 --> 00:46:06,200 So that was actually a big record for me when compared to 693 00:46:06,240 --> 00:46:09,600 those numbers. I I do remember it now, yeah. 694 00:46:11,680 --> 00:46:15,200 And we are already talking about having you come back and do 695 00:46:15,200 --> 00:46:19,040 another webinar on something you're pretty passionate about, 696 00:46:19,040 --> 00:46:25,400 which is IIBAS certificate in cybersecurity analysis. 697 00:46:25,400 --> 00:46:29,880 So it is a certification technically certificate, they're 698 00:46:29,880 --> 00:46:33,600 slightly different, but it's a, it's a program that we've had 699 00:46:33,600 --> 00:46:36,160 for a little, a little while and you actually you do some 700 00:46:36,160 --> 00:46:39,640 preparation training for that as well. 701 00:46:40,400 --> 00:46:43,840 Yeah, that would be good. Thank you for the opportunity. 702 00:46:44,200 --> 00:46:45,920 We're looking forward to having you back. 703 00:46:45,920 --> 00:46:47,520 I will. I will make sure that people 704 00:46:47,520 --> 00:46:49,280 know when we're going to have that. 705 00:46:49,960 --> 00:46:53,440 Let's see. Well, I think that might be it. 706 00:46:53,440 --> 00:46:57,880 Today we want to thank all you guys for for dropping your 707 00:46:57,880 --> 00:47:01,280 thoughts in there. You know what, it's always nice. 708 00:47:01,880 --> 00:47:04,320 I know I have always been a little nervous talking about 709 00:47:04,320 --> 00:47:08,280 cybersecurity because I also believed that it was something 710 00:47:08,280 --> 00:47:11,600 more technical. But let's see here. 711 00:47:11,600 --> 00:47:16,080 There was something that just came in learning. 712 00:47:16,080 --> 00:47:17,880 Thank you. For all the comments I'm I'm 713 00:47:17,920 --> 00:47:21,360 really loving it. I think my take away would be 714 00:47:21,360 --> 00:47:23,400 I'm really learning a lot in this room. 715 00:47:24,680 --> 00:47:26,920 Yeah, Excellent. Yeah, that's the whole idea. 716 00:47:26,960 --> 00:47:29,000 Yes. We want you to really have 717 00:47:29,320 --> 00:47:32,880 tangible takeaways that you can implement it in your work. 718 00:47:33,240 --> 00:47:35,520 Definitely. Yes, yes, exactly. 719 00:47:35,920 --> 00:47:38,560 All right. Well, Bindu it I have, as 720 00:47:38,560 --> 00:47:41,960 always, I have enjoyed catching up with you today and talking 721 00:47:41,960 --> 00:47:46,040 more about cybersecurity and AI. And I know you're gonna be 722 00:47:46,480 --> 00:47:51,440 talking about it more in your trainings and as you are around 723 00:47:51,440 --> 00:47:53,880 the globe talking to people about it. 724 00:47:53,880 --> 00:47:55,640 So thank you for joining us today. 725 00:47:56,840 --> 00:47:59,480 Thank you for the opportunity once again, Susan, as always, 726 00:47:59,480 --> 00:48:01,000 love. I think you said it right in the 727 00:48:01,000 --> 00:48:03,240 beginning. We have that why we connect well 728 00:48:03,680 --> 00:48:06,640 and I think comments are actually so I'm going to spend 729 00:48:06,640 --> 00:48:10,200 some time after the call here to go through the comments that 730 00:48:10,200 --> 00:48:12,480 there's there are there are quite a few and. 731 00:48:12,560 --> 00:48:15,960 There are quite a few really detailed ones I think, that are 732 00:48:16,080 --> 00:48:19,400 right up your alley, yeah. Definitely, I would go through 733 00:48:19,400 --> 00:48:20,760 that. Thank you all for joining once 734 00:48:20,760 --> 00:48:22,880 again and see you all around soon. 735 00:48:23,360 --> 00:48:26,240 I'm on LinkedIn, so if you have any questions, please feel free 736 00:48:26,240 --> 00:48:30,040 to reach out and I would love to hear more stories from you. 737 00:48:30,720 --> 00:48:32,080 Thank you. Thank you. 738 00:48:33,720 --> 00:48:36,800 All right, so that is our episode today. 739 00:48:36,800 --> 00:48:41,280 I hope you've got some takeaways that maybe you will be thinking 740 00:48:41,280 --> 00:48:46,440 about your role in cybersecurity and AI as a business analysis 741 00:48:46,440 --> 00:48:49,240 professionals. You've got a lot of value and a 742 00:48:49,240 --> 00:48:51,480 lot of skills that you can offer. 743 00:48:52,080 --> 00:48:55,480 We'll be back in two weeks and we're going to do a little 744 00:48:55,480 --> 00:48:58,000 something different. We're going to be talking about 745 00:48:58,000 --> 00:49:02,040 your authentic voice and I've got an opera singer, so you're 746 00:49:02,040 --> 00:49:04,360 going to want to come back because there could be singing 747 00:49:04,480 --> 00:49:06,640 and maybe some dancing too, Who knows? 748 00:49:07,040 --> 00:49:10,160 We look forward to seeing you then, so take care. 749 00:49:10,200 --> 00:49:13,000 Bye, bye. If you're hearing this, you made 750 00:49:13,000 --> 00:49:15,320 it to the end of the episode. Thanks for listening. 751 00:49:15,360 --> 00:49:17,760 Do you have any questions, comments or thoughts about 752 00:49:17,760 --> 00:49:19,640 today's topic? We'd love to hear them. 753 00:49:19,640 --> 00:49:22,080 Drop us a review or leave a note in the comments. 754 00:49:22,080 --> 00:49:25,280 Then like, subscribe or share this podcast if you like what 755 00:49:25,280 --> 00:49:27,160 you heard. And hey, you can help us shape 756 00:49:27,160 --> 00:49:29,480 future episodes. What do you want to know more 757 00:49:29,480 --> 00:49:31,640 about? Send us an e-mail at Live at 758 00:49:31,840 --> 00:49:35,480 iba.org with your ideas. See you again on our next 759 00:49:35,480 --> 00:49:36,040 episode.