AI: Your Next Tool or New Colleague? Next-Gen Security Skills with Priyanka Chatterjee
TLDR;
- When it comes to AI, it helps with immense productivity gain. Automate your well defined playbooks (Data collection, Investigation, False Positives detection and more) to kick start adoption.
- We are in a skills economy now vs knowledge economy as earlier. If you are entering into the cybersecurity world, focus on building skills and portfolio. This will put you ahead of others in your job search.
- Build human agency and utilize it for job search and also in your skill building process. This is what will differentiate you from others.
Transcript
Host: Hi everyone. this is Puruottam. and I'm super excited to welcome Priyanka Chatterjee to today's episode of Scale to Zero Podcast. Priyanka is a cybersecurity leader with over twenty-two years of global experience advising public and private organizations across twenty plus countries. And today she serves as CEO of London School of Cybersecurity. Priyanka, thank you so much for taking the time and joining with me today.
Priyanka: No, thank you so much, Purusottam, for having me and thank you for doing the great work. There's some really good content on your podcast. So thank you for having me on this.
Host: Thank you. You're you're too kind. so let's let's get started, right? But before we start, I I just want to understand, like what do what does a day in your life look like? You handle so many things, right? as part of your life. What does a day in your life look like?
Priyanka: My everyday looks very different. If you open my calendar, it looks very, very different because today I do so many different roles. I lead business, so I need to be a leader, I need to be a cybersecurity professional; I need to be an educator. So depending on all of that, and plus on top of that, obviously, because I'm an entrepreneur, I have to spend a significant amount of time in marketing, which means creating content. And so depending on the day.
I wear and swap so many different hats. But if I can give you a glimpse of a an average day, I would say. I would wake up, my first priorities are my children. I've got two of them. So the morning goes in sending them to school. And once I've done that, I tend to finish up my content creation. Now I've got to a cadence where I dedicatedly book a couple of days in the week for content creation, but
So that's my morning, and I tend to finish up all my brainstorming, anything to do with my education side of the London School of Cybersecurity work in the morning. My afternoon is more focused on any leadership decisions, brainstorming with my team to understand where we need to evolve the strategy. And I do work with some clients still. it has reduced over the years because
With God's grace, I've been able to build a team and the systems that is needed to scale the business. But when the time allows, I work with some customers out of the US in the evening. So that's what typically my day looks like. On the weekend, yeah, Saturday mornings are mine. So anybody, please don't ask me for anything on Saturday mornings. And and that includes my kids and my partner.
But I do run the two mentorship sessions over the weekend. So that that's overall what my week looks like. And each day is very different and depends on the priority week to week.
Host: Mm-hmm. The key thing that I see is you wear multiple hats and depending on what focus you are like what are you focusing on at a particular point in time, you are wearing that hat anyway, right? Either as a founder or as a leader, and then you're contributing to the community as well. so hopefully you will be able to cover some of those things during the podcast today.
Priyanka: Yes, definitely. Yes. Well, I'll try my best.
Host: So for today our focus is AI, cybersecurity, and the skills gap that we see in cybersecurity. So let's let's jump in. So you have been in the industry for more than twenty years now, right? And now we are in the age of AI. So how are you personally finding the progress we have seen with chain AI and what do you think some challenges there are in security industry that is facing today?
Priyanka: Mm-hmm. Yep. See, for the non-cyber industry, right, it's all about AI adoption. Yeah. And that is where I'm also adopting AI, like everyone else. But when we talk about cybersecurity, I call it we have to almost think through three pillars of APP, right? An app pillar. Now, what app stands for is adopting AI, protecting AI, and protecting from AI.
And that is where the benefits and the challenges of AI and cybersecurity are slightly different from what everyone else is saying. And that is where a lot of the content that we might be seeing on social media and online is varied. Right? Because if we have to work across the three pillars, we have to understand where are people adopting it and how do we really protect the systems that we as organizations are adopting or creating.
Plus, how do we protect ourselves, all the legacy systems, from the AI-enabled threat vectors, if you like. Right? And where I am personally invested in AI is definitely I have to work across three pillars, but if I talk about AI adoption I am focusing on tasks or parts of the system where it is augmenting or it is taking away pain from my business, right? And that includes, from a consulting perspective, that may look like for the lack of better words, automating or augmenting the report creation or using AI for faster analysis of data, faster creation of management reports, right? Now because to me AI is frankly, one of the best analysts that I that never sleeps and that never s sleeps, that never falls sick, will never ask me for an overtime.
Not that I'm saying I have a problem with that, but that's just as a business leader, it's it's a great opportunity, right? And that is where I'm also encouraging my team that look at where we can adopt AI to make our lives better. Because as part of our lives, we all have to do things we like and we don't like.
Can we augment what we don't like and still create same or better value? So that's kind of where I am personally enjoying the ride of AI. But obviously the ch with the challenges, then the the two P pillars, it it is quite a challenge because it is evolving so fast that the cat and mouse race is becoming quite interesting. Like how do we protect like thing something that is moving that fast.
Host: Yeah. I I really like how you structure it like a like with app, right? APP adopt protect with AI and from AI. And I'm pretty sure like ninety percent of people or maybe higher than that, just focus on the first part, right? How do I adopt AI? But you being in cybersecurity, you have to look at the other two pieces all also, right?
Priyanka: Yeah. Yes. You you can't. I mean you can't overlook that. It's it's it has been ingrained in me now. After twenty two years there's no other way I can see anything I see. I'm like, okay, what's what's gonna hap what wrong can happen from this? That th that's just an automatic reaction.
Host: Yeah. I'm pretty sure when let's say ChatGPT, the first version came out, most folks were super excited, right, to try something new. It for s folks like you in cybersecurity, the second thought would be, how do how do I keep my data secure? right. So yeah, it's a very different perspective. I can totally understand. Now, on the adoption curve, some look at AI as a tool, mere tool that which does certain tasks, right?
Some go beyond that and look at it as a colleague that they and trust a lot, right? What how are you distincting that? And how should even security professionals think about this?
Priyanka: See, this has been a bit of a mindset change for me as well. And I think that's where the industry is today. Because AI is we have seen a lot of technology evolution over the last century, right? But AI is probably the first technology that is how do you say, augment our thinking, right? Not just automating tasks in in cybersecurity, we have used machine learning for a long, long time. Right?
I mean, we heard about the whole behavioral analytics maybe got two decades ago now. Yeah? Or maybe a decade and a half ago. Now that was more or so that was machine learning, right? We still in AI we still have a lot of that. But with Gen AI, we actually are seeing and commercially seeing the augmented thinking. And that is, I think that has revolutionized how and why people are kind of really literally running after adopting AI. Now from a cybersecurity perspective, what it means is, and if I can take some examples, right?
Today I I do a lot of different hats, but if I have to think very simple of my very young people, and that is where the term AI colleague kind of really resonates with me is if I think back to like 2008 when I was quite young and I r I still remember that one night I was working with IBM and I was trying to set up the vulnerability management center of excellence, right?
For one of their industries. Now that one night, why I still remember is I had one Excel sheet with 9,000 rows and 11 columns. So that's about 100,000 data set data fields. Right. And my management manager came to me at nine o'clock when I was leaving for home and said, Priyanka, I need you to analyze all of this and have a summary ready for me for a meeting at nine o'clock tomorrow morning. And I was like, my God, do you really? Well, that was the way things were working.
We we have better labor laws now, but that is how what it was. So I went back home, had my dinner, and I sat through all night looking through all of those hundred thousand data fields. And back then, accessing data or knowledge was not that easy. So I had to literally go on Google, search for most of the things that I did not know about that. And that was a sheet with a lot of vulnerability information, right? And I was not even looking at what does it mean. Wha how to prioritize. I was just trying to analyze the data and see what does it mean. What can my manager go and talk to with the IT leaders? Right. Now, when I look at AI, that is exactly it it takes me back to that moment that I would not want anybody to go through that nightmare. So that is where I see the power of AI, where I can give it 100,000 fields and it will ch churn through that, it will analyze it.
And it will help me get to prioritization probably in under 30 minutes. So if if I compare, I spend the whole night, and here I'm doing that in 30 minutes. So that is where I think I almost see that not as a tool, because the better I build my relationship with the tool, the better I know how to use the tool, the better I have tuned the tool from a security perspective.
I can function better, I can deliver better. Right. And that tool is doing a big part of my role and is a part of a bigger success part of my role. And that is where I think I see it as a colleague, not so much as a tool, because we have seen machine learning, automation for a long time in cybersecurity. Those are not new concepts for us.
Host: A mere tool.
Priyanka: But because now we can think, we can talk and it can challenge us back, that is where I think it bec it gives it a bit of a human flavor, if you like.
Host: Yeah, I I I loved the example that you shared because pre even today I feel folks who have not adopted AI, they still go through a similar experience. I know that there are more tools and all of that, but and continuing on that example, right? Even today if you use a supply chain scanner, it will give you thousands of findings, right?
Priyanka: They do, they do.
Host: And if you have to report it to your leadership, you have to look at that. If you are not using AI, you have to go through the same manual process. Versus AI does as you said, right? Like does it in thirty minutes. So that's a big win for like productivity and in cybersecurity as well.
Priyanka: Yeah. No, it is. It is a big productivity win. Plus, if you think it from a leader's perspective, right, today if I have to run through that nine thousand rows and give a prioritization, I probably will need like a r really great S subject matter expert to go through that and tell me what is going to be easily fixable, what is more important, what assets. So there's a whole lot of context that goes behind prioritization, right?
But with AI that becomes much faster. I can get the same outcome. But may not need to get use the time of the subject matter expert. I can get them to do something more valuable.
Host: Right. Yeah. That that's that's a very valid point. Like it it helps you become more productive for like the power users. Now one question often comes with AI and even in cybersecurity, that we you give a good example, we see how it is providing value. There is also a lot of promise right that it can do. Like one example I'll take is Mythos or Mythos, however it is pro pronounced. Some organizations are saying that it is very valuable. Some organizations are saying it's just burning tokens, but it is not mostly false positive. So how how do you determine before you invest in it?
Priyanka: So if I have to go by what my experience tells me, any machine learning will need some level of tuning. Yeah? Because you need to give the machine your context, your organization context. And the better your context, the better the results, right? Now, how do you get to that better stage or better state? You need quality data. Right.
So data quality is where we will see the use cases or the tools of you can you can put all machine learning you like in any model you want. Right. But if your data is not great, data quality is not great, your tool is not going to give you a lot of valuable information. And today, to be honest, when I look at all the different tools.
Right and I and I go through and I test a lot of tools on a weekly basis. I see that the amount of false positive in certain processes and certain workflows has reduced quite a lot, right? So I if I have to make a decision, I would probably be okay to trust a little bit more on workflows that include re reporting, that include go find the data that includes tre things like triaging in a Security Incident Response Center. But I will not fully depend on autonomous decisions from a response perspective. Right? Investigation is okay.
I think the false positives are quite I think heading in the right direction. Still not 100%. And I don't think it will ever reach 100%.
But the response is where I'm still nervous. I'm not gonna put everything autonomous. And that is where I am also advising my clients and advising my own team. Like, okay, you can depend on it for analysis, but you need to still check how much what's the rate of false positives. Because you go to any conference, every single tool has something AI enabled. Now that could be just a chatbot Or it could be like a really powerful AISOC analyst, right? So we've got a big spectrum. Now, but the question is, what part are you okay to adopt? What part do you still need to wait to adopt? And for me, that autonomous response, like, do you want to go and like disconnect a machine from the network? Do you want to go ahead and disable an account, right?
So those kind of decisions I'm still not okay to give it, and I would want a human in the loop. So that's kind of where I see that whole area of false positives to be.
Host: Hmm. So if I if I want to summarize what you said, like if you have a very defined playbook which can be easily automated, then AI would be great at it, right? So you can just automate through it. But if you have a very high impact decision, maybe you take the data that is provided by AI, but at the end of the day you have a human in the loop who is taking that decision rather than let AI take the decision and run with it, right?
So yeah, that that makes a lot of sense.
Priyanka: Yeah, you have summarized it very well actually. I rambled through it but you summarized it very well.
Host: Thank you. so the you touched on like security operations earlier. you have seen a lot of technology waves come and go throughout the your your career now that we are in AI age. what is fundamentally different compared to previous technologies when it comes to security operations? I mean when somebody is trying to adopt AI into their SOC workflows.
What would you recommend? How do they start?
Priyanka: Okay. So the first I would say introduction or insights of machine learning or or some version of AI if you like. In the world of security operations came with the likes of next gen firewalls, the likes of SOAR, so security orchestration and automation, right? automated response. So these platforms have been around for a while now at least for a decade, if not if not more. Yeah. But where I see the big challenge still is and why those platforms did not get very well adopted.
Yes, they are people are buying the licenses, but when you look at the actual adoption, it's a it's an entirely different story. That whole automated response is not there. The automated fire application of firewall rules is not there. No one really adopted that as much. Now where AI is going to make a big difference is if we take the example of SOAR, right? In the pre-AI er era,
The you could define a workflow and you could automate it, right? But you the analysis or the trigger point of that whole SOR workflow had a lot of inconsistencies and a lot of the effort was still needed to be given to actually triaging the data data, right? It was more a data problem and knowledge problem. How is the level one analyst looking at it?
Right now the organizations where they had much cleaner decision or more mature decision workflows, they were able to adopt the SOAR in a much better way, I would say. But most organizations struggled because their decision workflows in the first place was not in place. So if you cannot run something manually, it cannot run automatically.
Where AI is helping is it can it churns through it makes that whole analysis piece much much easier, right? Which means the trigger points start becoming more clear. Because now AI is giving you a summary, and even though there's a road to or a journey to actually adopting that AI sock, it it is making it easier, right?
So that's where I see the difference from a cybersecurity perspective because I I did mention that AI is the first technology where it is augmenting our thinking. So it is not just taking that it's not just acting as in an if and else model. We give it some question, but if it when it does not feel that it's right or if if there's an alternate logic, it will come back and challenge us. And that is where I think the real benefit of AI in a SOC world is. yeah, that that's that's where I see that that's the biggest difference of AI and the security operations world is going to be.
Host: So now as a if I'm a security analyst using using traditional tools or or and now with AI ingrained in it, what sort of skills should I build? Let's say three or three to five years down the line.
Priyanka: Of course, see if you are whether you are a working professional or you're going to be entering workforce in the next year, two years, you need to learn AI. There is no second guessing that question. Okay. The sooner we start learning, the better we will the the longer our runway will be. Now, what they should be learning, I I want to almost start take the question in with a little bit different spin, if you like. I I would want to for the audience to visualize what the life of a SOC analyst might look like in three to five years. Right. So today, to run a 24-7 SOC, we need at minimum six people to work in shifts, right? So we have someone in the morning, someone in the afternoon, and someone in the night shift or graveyard shift. Some people call it that.
Now when I see three to five years ahead, the vision or the visual that I get in my head is something that I've seen in Iron Man 2. Have you watched that movie? Okay. If you have, then you probably will connect to that, right? So every time someone tells me about an AI-enabled SOC or an AI analyst in the SOC, this is exactly what I see in my head, right? In Iron Man 2.
There is a clip which says, Welcome back home, right? Where the lead Tony Stark, he's he literally does this. He claps and he says, Wake up, daddy's home. So that is what I see in my head. That is what the future of SOC is going to be. Where the SOC or our AI colleagues would be running the triage part, I would be going back home.
At the end of the day, right before sunset, no more graveyard people in graveyard shifts. And when I come back the next day for my normal day shift, it would have analyzed and kept everything ready. And so it will have like almost the shift handover sheet ready for me and would be able to talk to me and give me a report or in a document format, depending on the organization.
And it would have a list of decisions I need to make, list of judgment calls that I need to make as an analyst. And that is where I think the learning and the evolution of the SOC analyst role will be. Today, most of the time is spent in going through the data, but in an AI SOC, the AI analyst would have done that and it would be waiting for the analyst to make the decisions.
So we will start seeing more and more what is today called level three analysts, right? The team members who make decisions, the team members who approve, like, yep, take that machine off the network, those kind of decision calls. Which means the workforce that is going to enter the SOC, that is all that SOC is going to do. It will be making decisions based on the analysis that our AI colleagues or Jarvis would have done for us already while we are sleeping.
Host: I imagine every sock analyst having that kind of power. I I mean I love the visual that you painted. I would love to see that because that makes the sock analyst life much better. Like and they are more productive, they are not spending slash wasting a lot of time analyzing or doing mundane tasks, right? AI has done most of the prep for them. The key thing that they are focusing on is the decision, which is sort of moving the needle forward, right? rather than going through, as you highlighted earlier, hundred thousand elements in a spreadsheet to decide what should my lead what should my manager present in front in front of my leadership, right? I I love it.
So now the question is that if AI can do all of these things. then why why should I even learn anything? AI will do most of these things, right? what role will I do five years down the line if AI is doing let's say ninety five percent of the work? yeah, I mean I'm curious, like what what would I do?
Priyanka: Okay. Love that question, yes. Okay. So yes, so why why should I learn something? Let me tackle that question first and then we'll take the question, what what would I do? Right? So I'll I'll give you a very take a very simple, silly non cyber example. So my mom used to say this to us when we were little, yeah. And she used to say, if you have to oversee a maid or a cleaner, you need to know how to clean the house yourself.
Yeah. And that's exactly I think what's happening. We will have our AI colleagues and we will tell them this is what I need you to do for me, but if I don't know what I need to be done then what? Then it's just gonna be just another bill on the on the list, right? And that is where I think the fundamental change of the workforce is going to be.
Today I in fact I would say not today. When we were hired initially, right? When I was hired 22 years ago, we were hired for some knowledge and more attitude, like yeah, the person can learn and coachable. Now the whole game has changed. Because our barrier to knowledge was quite high, right? We had we did not have free access to knowledge. We had to buy books, buying like expensive certifications was not easy. I remember I had to like say for got six months before I could even attempt for a CCNAs certificate 20 20 years ago. Now, today the barrier to knowledge is very, very low. We have got free tools, we've got free certifications, we've got free YouTube.
There's there's a ton of knowledge available, right? But the barrier to getting hired or barrier to employment is really high. And that is the big shift that people who want to grow and people who want to enter the workforce need to change. That we have moved on from being in a knowledge economy to already a skills economy. And there are two different words: knowledge, being knowledgeable and being skillful. And that is where I think a lot of the confusion comes as well in my mind. Is we may have knowledge, but unless we know where and how to apply that is not a skill, that is just a knowledge.
Host: Yeah, yeah. So I love the example. I I loved the example that you gave about the c like looking at what your maid is doing and how do you evaluate and what do you do with it. now the thing is that you spoke about knowledge economy and skill economy, right?
I think I have a fair idea, but if you can maybe double click on that for our audience. How do you see that different?
Priyanka: Yeah. Sure. I will let me do this. Okay, so when we double click on let's let's focus on the word knowledge and skill. Knowledge is knowing about something. It can be any topic, how to cook, how to clean, how to clean s we turn on the light. It could be anything, knowledge, right? And that is how the school and education system is structured today. It is meant to give you knowledge. Yeah.
Now, if we look at the different waves of employment or different waves of how people got value out of the economy, if you like. Yeah. Now, the pre-industrial wave was about skills. So I could be a blacksmith, a goldsmith, I could be someone a weaver, a mechanic, whatever, right?
And in fact, in India we had surnames based on your occupation and and in a lot of other countries too. Then with industrialization and the in the whole explosion of internet, it became an information and knowledge economy. The more knowledge you have, and that the roles of law the likes of lawyers, doctors, those kind of things exploded with with that, right? The more knowledge you have, the and then the information as well.
The companies who had more information available with them, they were more they had more value. Now, the wave is going back to, and I I think I can use the word history is going to repeat itself quite safe here, is we are going back to where the value of skills is coming back. Because information has become very cheap and abundant. And people are asking, like, okay, what do we do with this information?
So people are asking, like, if I have to hire you, I'm going to ask you, like, okay, you know this information, where are you going to apply it? So that is where the skills economy is all about getting the value out of the skills. So you have the knowledge plus you know where and how to apply it. And you have agency. There's a there's a new term that's making the rounds, I would say. But it's very simple concept.
Agency means you have a drive, you're self-motivated, and you know where you have a skill and you know you go after really applying that, right? Which means we might see more entrepreneurs, less bigger, big less of bigger corporations, if you like. There will still be some bigger corporations and they will evolve. And I have my own hypothesis on those, but I think for general public.
It is going back to skills economy, and people who have some agency are going to be more successful. So for all the young people here in the audience, I would say build agency. If you know what does that what that means, just search it up. What does human agency mean? And it's not opening an agency like a marketing agency. You having agency means you are driven, you will have the willpower.
And you will strive forward, you decide you're gonna do this, you actually do that. So that is what having agency means. And you know what your value is and you go and you exchange things for your for what your value is, not just for money. You you probably will not be working for money, but maybe we will go back to the barter system, who knows?
Host: So I I thank you again for doing that deep dive into knowledge economy, skills economy, and how we are sort of history is repeating itself. Now I have an important question here. So knowledge is available, right? You can just sign up to Chat GPT and you can ask any question or Claude and you get answers to those you sort of get knowledge by that. for somebody who is starting their career in let's say in particularly in cybersecurity.
You need to have some skills, right? Because we are in a skill economy. But in order to build the skill, it will take time. So, how do you break that chicken and egg problem? Like if I do not have skills, I'll not get hired. If I do not get hired, I do not I cannot build skills. So, how how do you tackle that? As a as someone, let's say I'm try I just finished my education, which is very knowledge-driven.
Host: Now I want to get into the workforce, cybersecurity workforce. Wha what do I do?
Priyanka: Okay, so if you are new to like if you're fresh and you want to start with cybersecurity, and and you you said it very well, it's like a chicken and egg problem, right? Because the schools and colleges are giving me the knowledge, but we don't have a curriculum where the application piece is embedded yet, right?
So what a lot of universities are doing is they are partnering up with industries, right, and specialized training providers like so we have our programs at London School of Cybersecurity, we have designed it in a way so that we teach more application than knowledge, because I know the people can find knowledge everywhere, right? So we do talk about knowledge, but we also foc but we focus most of the time on actually learning the application. Right.
And that is what our students are telling is making the biggest difference. Now, what it means for new people, young people who are who might still be in school and colleges, right? So they need to, for example, they they have a module on I don't know, vulnerability management again. I'll take that example. Yeah. So they need to come back and actually brainstorm with AI and say, okay, how do I really learn to do a scan? How do I really do a vulnerability management in a company? So that would be a very simple way of starting to learn with AI, AI enabled learning, right? And what I'm telling all of my students is having a C V is important.
Because that is that opens the gate to or door to an interview. But what really gets you hired is having a portfolio where you can show that you know how to apply the knowledge that you have gained over the years. And it is not something very complicated.
You could use cloud or Chat GPT to create a portfolio. You can create it on Notion. You can create it as a Google site, as your website. There are lot of different ways to create it.
But it is essentially nothing but a way of showing the employer and evidence that you know how to apply the knowledge. For example, if you're applying for cybersecurity, a SOC analyst role, right? You need to show that yes, I can I know how to look at logs. I have created a sim in my house. I have tested with the open source and map scanner, right? Under the hood, most of the vulnerability scanners use Nmap.
So if I have tested that in my house, that's very easy. And a lot of people ask me, how do I build a home lab? Well, you can ask AI today. You don't need to wait to get on a call with me. Right? So that is where the students need to become really smart. They are smart and they are starting to use AI. But a lazy use of AI is not where the value is. You need to ask smarter questions. Because if you have if you have to enter the workforce where you are making the decisions.
Making a decision making requires you to ask smart questions. So learn how to ask those smart questions as you are learning. That is what, but just to summarize, if I'm a young person, I would have two things. One, a solid CV, which is ATS compliant, and second is a portfolio where the employers can see, and that is how the employers are hiring today.
Yes, some industries are asking for certifications, but that trend is going down.
Host: Now how do you now compare certifications? to the portfolios that you highlighted, right? That which shows you have actually done versus somebody certifying that you have done. Like how how do you look at those two things?
Priyanka: See, the certif the whole concept of certifications was valuable when we were in a knowledge economy, right? Certifications are proof or were considered as proof that we have knowledge, right? But it does not mean that we know where to apply. Plus, the the pioneers of the certifications, like they don't teach application.
They only teach you knowledge, right? There are some of them that teach you where maybe to apply, but most of them are like this is how you do pentest, this is how you do encryption, this is how you do that, but it does not give you any context, like this is where in a company you would need to encrypt. This is where and this is how you need to plan the whole pen test. It does not touch anything that the industry does, all the right.
And that is where the portfolio becomes really important. Is if you have the certification and the portfolio, then it's really hard to beat you because then you have proof that you have knowledge and you have proof that you have up you know how to apply. Then it's a very easy conversation, right? Because if you only have a CV, the questions you are handing over all the power to the interviewer, right? But when you have a portfolio and you can guide the employer through the portfolio, there would be the whole interview could just be talking about one use case and that's about it. And if you can confidently answer to that, you're golden. You don't have to worry about not passing the interview.
Host: Mm-hmm. Makes sense. So maybe I have one last question. You earlier touched on that maybe if you have s enough skills, you do not maybe have to work for money, you just enjoy what you're doing, right? and this is where like mm folks like Sam Altman or Elon Musk, who is now a trillionaire, spoke about money would be ir irrelevant and jobs will be optional and things like that. How do how do you see that playing out in future?
Priyanka: okay. So those those are some big hypotheses. Okay. And if we have to look back at history, I I don't know where if we have an equivalent of that. I mean, we have heard similar statements when the industrialization happened, when the internet exploded. But I am yet to come across anything any resource where it this has been proven, right?
So if we have to focus on just focus on the statements that Mr. Musk and Sam Altman Altman have said in the past, in the recent past. So Mr. Musk thinks that we can exponentially grow the amount of things that we can produce using robots, right? So robotic manufacturing is going to give us surplus supply right but what he's not thinking about is if there are no humans working who is gonna buy those things right so that one question that I don't think I have seen an answer from any economist and I'm not an economist but I'm just thinking as a normal human being and a a a citizen of the world and Sam Altman said talked about some concepts like universal basic income and things like that. Now, that is a very socialist mindset. And if we have to believe where what history has taught us, that does not really exist.
People with more skills, more value, they will create more value, they will have more wealth. Right? So how how does that and that also makes me wonder if we will go back to some form of kingdom, right? When we where we would have kings and queens or equivalence where these big companies who have these who are creating these big AI models, the likes of OpenAI or Anthropic or Google DeepMind, right? So will they become the new kings and queens of the society? And the companies who can really execute at scale, right?
And help with AI adoption, will they become the new sort of the nobleman class, right? The the higher middle class. And people who can use AI will become probably the new middle class, maybe. So that's that's where I I mean there is a lot of when I hear these statements, these are the thoughts that go through my mind.
And and if I have to kind of I've been I'm I'm listening to a lot of podcasts as well. So th one of the recent ones was on the Diary of a CEO by Stephen Bartlett. And they were talking alongs around the same things, right? Like what is a solution? How do we really bring in that equilibrium? Because if people don't have jobs, who buys the stuff? If we have a surplus. So that is the so the equilibrium is going to be disturbed, at least in the short term. That's my
Gut feeling. And but we will human beings are very clever and we will evolve quite fast to see where do we bring that balance, right? Because just having the tool is one thing, and we have said that for pretty much everything that we have invented and discovered over the last centuries, but until we get to a point where we know where the AI adoption kind of balances out, I think. Not everything is going to be AI-fied if I can use that term. So where do we find that balance? Because the one thing that I always worry about is as powerful and well-intentioned AI is it is it it is a tool at the end of the day, or it is a piece of code, let's call it. I I it's a colleague but it's still a piece of code.
How do we bring that the centuries of morality and ethical ways of working that we human beings have and a sense of responsibility, right? Because we are seeing use cases where nation states are using these AI models for like targeting missiles, targeting rockets, things like that, right? So the same tool can be used for good and not so good and bad purposes.
So where that balance would be? I don't know. Does everyone know? We no, I don't think so either.
Host: I love how we started with AI and ending with humanity going which ways. but I I I liked how you deep you think when it comes to the impact of AI, the wealth generation and all of that that c comes with it.
Priyanka: No. W we have to. No, we have to, right? Because cybersecurity is all about trust and resilience. If we are not able to protect our own species, if you like, what are we doing?
Host: Yeah. Like our j like folks in cybersecurity have that responsibility of protecting people and with technology tools and all of that, but yeah, that's the core driver, right?
Priyanka: Yeah, that that's what gives us Yeah, that that's what I think gives us that sense of fulfillment. And I would say whether someone is doing a technical role or not, we are in a weird way trigger somehow f fulfilled by the fact that we are contributing to protection, contributing to safety, con contributing to security.
Host: Mm-hmm. It may not be visible on the surface. Like as a as a consumer, we may not see the SOC analysts, those are working at let's say an airline that you fly with, but they are contributing to your safety and security and as well, right? So yeah.
Priyanka: Yes, it is not. Yes. No, they are. We we feel the difference when an airport is attacked. Who is working? Is it's our shop team is working. Right? Investigation and response. The forensics people. See at the end of the day it's all about protecting the society.
Host: Mm-hmm. Yeah. yeah, that that's a great way to sort of close the episode. But before I do, I have one last question for you.
Do you have any learning recommendation for our audience? It can be like a blog or a book or a podcast, anything that you would recommend.
Priyanka: So we talked about a lot of things today. everyone, whether you're working or in school, in college, please start learning AI. That's my one humble request to everyone, right? And you can ask AI where to start learning. That's how I started learning, right? So that's one thing.
But if I have to move away from AI and suggest one thing that everybody in cybersecurity should learn is what is called being called soft skills today, but I would say learn how to communicate. And that should be in verbal, that should be in written. And yes, AI will do a lot of work for you in terms of creating the documents, in terms of like creating the words, right?
But if you cannot communicate well because the communication and thinking go hand in hand. If you cannot think very clearly, you cannot communicate well. And because we are heading into an era of AI colleagues, communication becomes very, very important, right? Because we all know we all have heard about the word prompt engineering. Now the clearer your thoughts are, the better you are able to communicate it with your AI colleague, the better your results will be. And the same goes for governance because as the adoption of AI increases, the need and the impact of people who are governing how AI is used and where AI is used is going to be massively more important than what it is today. Right? Now, to do both of that role whether you want to build your relationship with your AI colleague by better communicating and having a better thought process, or you want to do a better job at governing, you want to define better policies, you need to be able to communicate, you need to be able to think very clearly.
So that's one thing that I would suggest learning.
If I have to recommend a book, there are two books that I found quite helpful for the young people, there's a book called Atomic Habits. I would highly recommend to read that. And on communication, I like the book Surrounded by Idiots. It's it has a very funny title, and I I honestly bought it for that.
But when I read read the whole book, it it gave me a lot of different context because we all like to think we are surrounded by idiots. People don't understand cybersecurity, so when we know how to really operate in that space, you're golden.
Host: Sounds good. thank you so much for those recommendations. So when we publish the episode we'll add them to the show notes. And with that, thank you so much, Brianka, for joining. And this was a lovely conversation. Thank you so much for spending time with me.
Priyanka: Thank you very much. No, thank you so much Purushotam for having me and thank you for waking up early to have this conversation. Well thank you so much, yeah. Yes. Thank you. I will need to
Host: Absolutely. thank you.
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Abhiram Shindikar
Abhiram Shindikar