Welcome to Business Analysis Live.
I'm your host, Susan Moore, community engagement manager
with the International Institute of Business Analysis.
I explore topics with industry guests about the work of
business analysis professionals and how their work helps
organizations achieve better outcomes.
And Yep, we take your questions too.
Thanks for listening. Let's get started.
We have been hearing a lot about AI these days.
It seems like it is everywhere from fast food restaurants to
online shopping to just about anything that's happening.
One of the things that you may not be thinking about as you are
using AI is the impact of cybersecurity on AI.
And that's those two things are kind of colliding in the world
at the moment. And I know that it's been
something that our business analysis community has been
talking about because our next guest is getting asked a lot of
questions about how those two things work together.
So on today's episode, AI and Cyber security, I am super happy
to welcome to the stage Bindu Chanovarapa.
Hey Bindu. Hello Susan, How are you doing?
I'm doing really well. Thanks for coming to join us
today. I I always enjoy chatting with
you because it it just seems like we kind of vibe naturally.
And so when you and I caught up recently, I said, tell me you've
been around the world speaking. What are people asking you
about? And before you answer that
question, you've been our guest a couple of times on here.
But first, I'd like for you to introduce yourself to the
audience. Tell people a little bit about
you and why they are asking you about AI and cybersecurity.
First of all, thank you, Susan, once again for inviting me to be
the guest here. And it's always great to speak
with the community. So for those of you who don't
know me, I've been the China member.
I'm a business analysis consultant.
I think if I look back, I've been a BA all my life,
consciously and unconsciously. So in the last 7 to 8 years I've
been focusing more on cyber security for business analysts.
As part of that work, I've been a co-author on the CCA
certification as well and currently the founder of
Cybersecurity for Business Analysts, which is a space that
provides cybersecurity education and services and of course with
that, business analysis services and consultation and training as
well. So yeah, along with that, I love
working with the community. So I do take every opportunity
to talk about this to share because as I'm sure all of you
might have, our experiencing that the landscape is shifting
very, very fast than we could imagine.
So which means we have to adapt as business analysts.
And when we talk about adapting, what is that we are adapting to
is technology because technology is becoming pervasive.
So when there is technology, there has to be cyber security.
It's like 2 ends of the state. That's that's how I call it.
So you can't pick up one end and not pick up the other.
So cyber security is no more or security holistically is no more
something an NFR or an add on. So it is very much should be
part of our day-to-day business analysis and that's exactly what
I am advocating. And I'm also on a mission to
probably educate almost every BA to be aware of cyber security
and make that part of their day-to-day work within 2030.
So that's about me. Thank you, Susan.
Yeah, I as you and I have talked.
By the way, I also want to add that you wrote a book on this.
Yes. So there's.
That business analysis, yes, if you can see, I don't know with
the screen, well, not really. There we go.
Cybersecurity and business analysis.
There you go. And so you give a lot of really
good information for, because I, my sense is for the folks that
I've talked to in our community about cybersecurity, they tend
to think that it is about what happens after you've been hacked
or after you've had some sort of incident.
But really what cybersecurity and business analysis is is well
upfront of all of that. And if you know it, because you
are trying to think about ways that you could be vulnerable,
yeah. Is that true?
Yeah. Absolutely.
I think a very common analogy I could give is, you know, when
you're constructing something you lay the foundation and the
foundation is only laid to the depth in the ground depending on
on the length of the building that's going to stand out.
So which means you're talking about security there as well.
So it is in everything that we we do.
It's basically risk, you know, as BAS we constantly look get
risks, we constantly understand that it varies, it changes some
pop up at the late part or some mitigate.
So we it's, it's all about evolving those risks.
And similarly, when we talk about any project, security is
part of every fabric or the DNA of every activity that we do.
It, it is, yeah. And I think that that talking
about how you build a house because there's other ways that
you reduce risk when you are building a house, right.
We, we make sure that the, that we wrap it so that the elements
can't get inside. We make sure that we check the
grading of the land so that water doesn't come in.
All of those things we have to be thinking about, and they are
dependent upon the environment around us.
Now maybe, you know, my sense is that people think of AI as maybe
one thing, but let's define what you're talking about when you're
talking about AI. So how?
How do you define AI in this space?
I think that's actually a really great point starting point,
Susan, because we talked about AI and every time, you know, I
think somewhere we need to understand what AI is and what
AI isn't. In fact, my journey with AI or
specifically RPA started in 2019.
So first part of the project was RPA and the second part we
wanted to bring in AI. So since then, you know, my
understanding has been evolving and especially now with Gen.
AI, which provides A countless number of ways to use AI every
single day. It's a new learning.
And whenever I speak with people on AI and, and I'm sure you, you
might have experienced as well, almost every second conference
topic will be based on AI. So everybody comes from their
perspective. So every time when I hear, I
reminded about the story of the blind man and the elephant,
which actually I was sharing with you earlier as well, where
the one of the blind people, they come and they touch the
body and they say, oh, it's, it's like a wall or someone says
it's like a rope. Someone says it's like a tree
trunk. So the point here is all of them
are correct in their own way, but they are all incomplete.
So the way I think AI is exactly like that.
All of us are seeing it from different experiences.
It could be automation, it could be chat boards, it could be data
analysis or even creativity. But yet the whole picture is
much bigger than all of these pieces.
So I always share this that AI is an incredible tool.
Probably I feel one of the best transformative tool that we have
invented so far. But the challenge or the real
question that always comes to my mind is, are we prepared for it?
So it's, it's like having a precision tool and not knowing
how to use it. So with with anything, you know,
we always have to know how to use it and we also have to know
how not to use it. So at it at it's core, the way I
see AI is it's nothing but algorithm or a math that learns
from data. Data is the engine for it and it
just not learns. It also can make those decisions
and take actions without a human intervention.
Sometimes it does a great job by the times it can be wrong, but
very confidently wrong as well. So, so which means the point
here is it is a very incredible tool, especially very efficient,
fast, good learner. It's like digital intern.
I think I've heard even Angela shared this as an intern and
which I also experienced because whenever I'm prompting, I
actually tell what, what you, you, what the role is.
So it is actually the digital intern who not only understands
the steps that you're asking it to do, but again, at the same
time, it learns. And next time it can even take
action without you involved being involved.
So when it comes to security, it's exactly the same where
there's a lot of work, there's a lot of pressure because today
our threat landscape is so, so, so much complex and complicated
with lot many systems, lot many third party suppliers connecting
across the world, different locations, different touch
points. We have expanded like ever
couldn't even imagine the, the, the kind of lifestyle that we
are having probably even 10 years back.
So all of this is possible only because of technology.
And as I was mentioning, when it then there is technology,
there's always security. So today there's a lot of
pressure. So with AI with its capacity to
think faster, be more efficient, we will, we can, or we are
harnessing its power to help identify threats and many other
things within the cybersecurity space.
And yeah, with this, I think it's if with any tool, it's
double edged sword and even here as well.
And so you said that technology introduces risk.
And I bet if we think about the kinds of systems that we have in
our organizations today. So let's take AI out of it for a
moment. There's probably a list of the
kinds of risk that might be introduced.
But as we think about what AI has the ability to do, because
AII think you said it, AI is a technology.
So we need to think about it in terms of it is another kind of
technology that's heavily dependent on data and it
produces more or less data with some sort of context.
So as I'm hearing you, what I'm thinking is the risk I think
expand with AI because the risk now are what does it do with
data that is not good. It can make poor decisions which
if left unchecked by a human could result in risk or real
trouble in organizations. Is that is that kind of is that
that also the domain of cybersecurity?
The things that the outcomes that happen because of AII.
Think that's again, what do you say?
It's, it's sitting on the fence because that's again a big
discussion who owns the responsibility of AI and that,
that's again, probably many governments are also kind of
trying to bring in governances across.
But in terms of risk itself, definitely that is that is a
risk. And that probably, I think as
business analysts, we should be raising that because identifying
those risks is part of our analysis, whether it is the
quality, data quality or even the biases within the algorithms
or even even to kind of understand what are those
decisions made of. Because today I'm sure you might
have also heard there are many false positives and we'll have
people are or the teams are dealing with it, which I think
from the threat and being, you know, the fatigue today we are
calling it as alert fatigue or something like that.
That means there's, it's just being alerted.
But again, having said that, we are in a learning curve.
We are in an evolving phase. I think last time when I was in
Poland, there was a session again which was talking about
does AI ES really required for the project.
So basically the research that was shared was it was actually
surprising 95 of the projects had actually failed.
But I think that is again the learning curve because we are
trying to like as I said in the beginning, we have this
incredible tool, but we are not prepared for it.
When I say we're not prepared for it is we first of all don't
have complete understanding of what it is and what the
capabilities are and how to use it and how not to use it.
So which means it's any new shiny gadget that's out there,
it becomes a silver bullet to solve everything.
And if one company is doing it's everybody has to ride the wave,
the AI wave which is happening. So that's again another added
reason as well why everybody are just jumping on it.
But that's exactly where from ABA perspective we can raise
those risks. We can even identify whether AI
could really add any value like with any other things that we
analyze, whatever the business objective that we want to
deliver, the way we deliver, we provide options, we suggest
what's the best option. So again, if we are going with
AI, is it really viable? Is it required?
Are we going to really you know, or is it just something
everybody wants to do? We want to do it.
I think that's again, the industry is kind of maturing
because many of them are learning from the failures and
there are good stories as well and there are not so good
learning stories as well. But in to give you definitely
one of the good stories is I think it is Microsoft defender
or not and I'm not too sure. I think probably Defender, they
are kind of AI. Their AI is actually going
through triage chink, probably 7578 trillion security alerts in
a day. It could be from emails,
passwords, laptops, endpoints of IoT, cloud doesn't matter.
So basically it's just try to check every second, every .24 by
7. And this is actually humanely
not possible at all. And the response time, the
alert. And one of the the fascinating
thing which for me was in this particular blog was today, you
know, like when we are providing a solution, like for example, if
in our organization there's an attack or if there's a malware,
we kind of try to, you know, fix it locally within our
organization. But now with Defender kind of
applications or organizations when because it's on the cloud
when they identify within minutes, they just not sorted
there, but it's across all the connected systems globally.
So that is the actually wonder of AI to actually speak.
So because it does things instantly.
And I think with with that this a lot of damage that can be
avoided in terms of financially or you know the reputation as
well. So yeah, it's again how we use
it, but definitely something that risk worth talking about.
Yeah, I, I am struck by how quickly many of us trust AI even
though we don't understand how it does what it does.
And I think therein lies a really significant risk and
maybe why some of these projects, these AI projects are
failing. I think I was reading an article
this morning from in a, in a Business Journal about the
majority of agentic AI projects are failing.
And I suspect it is because we go in with really high hopes of
all of the amazing things that it's going to be able to
automate only to get a bunch of outcomes that we can't, we don't
understand or that aren't really beneficial.
So there's, you know, there's that.
And I wonder if as as AI and cybersecurity are, are mashing
together, how much we need to change our own attitudes about
about the about the technology of AI and how we think about
risk rather than just focusing solely on the technology.
I almost see that we need to kind of check ourselves.
I know when you were here last we talked about reflection.
And so I almost wonder if that's not the thing that we need to be
doing as we are getting started with these AI initiatives.
You touched upon the word reflection, which I wasn't
prepared, but something that's always, you know, I mean, I can
go on. That's what they say even though
I'm not. But I can go on because again,
when we are talking so much within the AI space, Susan, I
feel somewhere because it's kind of knocking everybody's or world
in one way or the other. It could be in good way or it
could be in shocking ways with you know, jobs and all the fear
mongering things. That's how that that is
happening there. But somewhere I always feel that
shift is happening. And if you look back through the
history, every time when things have happened like this, it has
been a point where we as the community or the humanity have
to take stock of where we are going.
It's like, you know, we are on a journey and even on a project we
kind of map out what are the benefits and how do we know we
are on the particular journey. So similarly, I think somewhere
these points really make us reflect on where we are going,
somewhere do we need to pivot accordingly and adapting is
something. Which is a nature law of nature,
which nobody can actually escape it, which is, which is in a good
way because that that's how we grow.
I think after a certain age, if not physically, mental growth is
continuing no matter what the age is.
So again, these are those moments which really brings
reflection to the forefront because as BAS, we are not
getting bogged down into the day-to-day work of writing.
Not many things or trying to do, you know, the normal grind work
so we can take the support of technology or the AI
specifically for all the grind work.
And it really gives us that space or the mental space to
actually think what it is. And that's exactly why I feel
very strongly that as BAS we don't lose our job.
Probably we, we will be evolving, which again, like
looking back two decades, when I started my career, I was working
on a single laptop as, as, as a sorry, not even laptop, the
desktop that everything was happening, then everything
connected. And when everything started
connecting, the way we do business changed.
So which means every time that there has been a constant
adaptation to the new changing landscape.
And I think that's very easy for business analysts because we
somewhere, our mindset is we are all constant learners.
Otherwise we wouldn't be ABA because every role demands
different, you know, tools, skill set and we need to think,
we need to have our intellectual capabilities always up high.
So that means we are lifelong learners.
So again here I think this is giving us this opportunity.
And when we talk about human in the loop, I always see ABA to be
that person or somebody with the BA skills who has to provide
that context. Because when we talk about AI,
again, talking about like interns, they again when when
you say AI, it can, it is fast, it is efficient, but it doesn't
know the culture, it doesn't know the ethics, it doesn't know
the right or wrong or good or bad.
It can only learn something is right or wrong or something is
normal or abnormal based on the data that it it or it is
learning. So these are some of the context
that as BS we should be providing and working with the
team. So you know, like we say, BS are
the bridge between the business and the technology.
But today this technology has kind of expanded.
So that means the bridge is also kind of widened where we not
only deal with or not only collaborate with the business
and technology. Now it's wide.
It could be data, it could be security, it could be AI, it
could be ethics, it could be when we say compliance, there
are multiple levels of compliances here again.
So as B, as I think we now have to be more efficient in terms of
not the quantity but the quality where we can collaborate with
all of these different widened horizon.
So somewhere I think their adaptability comes and to do all
of to understand all of this, going back to the point of
reflection is something that when you sit back, you know,
like watching a movie and you see what how exactly the things
are happening, then you get that understanding.
So definitely, definitely, I find reflection more than ever.
And just to add another point to this here's competing with AI is
foolishness. Competing in what way?
Like us trying to be as good as or as efficient?
Exactly. Exactly.
Because that is, that is not possible.
And that is definite. First of all, let me say
competing itself is wrong in any sense, right?
Right. Because I think by nature we are
all enough complete in our own ways.
Just by competing, we identify the shortcomings, which are
actually not the shortcomings. So basically, competing itself
is wrong, but competing with the wrong competitor is even
stupidity. So there's no point.
But On the contrary, I think we need to focus on what exactly
makes us human. So bringing that ethical compass
or bringing that empathy, bringing those fairness in what
we do is something very much important.
So along with that, as you can see, there are so many different
new roles that are shaping up in these areas as well, which again
will need business analysis skills, our core skills.
So I think there again, that there's a lot of scope for that.
So definitely reflection is something going and and starting
or just trying to see who we are.
And from that space, you know, operating is I find it much more
comfortable, confident and not being worked out of whatever the
things that's that is happening surround.
I am. So here's here's kind of what
you're making me think about. So we've we I think we all
understand the the strengths of AI.
You've said it's, it's fast, it's efficient, it can make very
quick decisions. Those are those are good things
and things we can't quote, UN quote compete with, but it has
lots of weaknesses and you've you've talked about some of
those. I think that instead of us as
business analysis professionals getting really worried about
losing our jobs, I think there's a lot of opportunity for us in
those things that you've identified as weaknesses of AI.
So, you know, making decisions with, with bad or flawed data.
I think there's an opportunity for business analysis
professionals to help to, you know, make sure that data is
structured well, is accurate, is of good quality.
I think there's opportunities for business analysis
professionals in validating decisions that are coming out of
this and not necessarily scrutinizing the details of that
decision, but also playing out the impacts of those decisions
forward because AI can't do that, or at least not do that
well. Are there other weaknesses of AI
that you see as opportunities for business analysis
professionals to step forward? You mentioned two things and I
would just like to dive into a little bit deeper so that we we
really get the sense of it. One thing you mentioned was
data. In fact, I was just having a
conversation the other day where we were talking about data being
the engine and probably, you know, Terry Barish.
So he works in the data space. So he has actually identified
data readiness framework. So we were actually going
through that in detail. So there's a lot of work that
BAS I can do when it comes to preparing data.
It is just not structuring. So there there's a lot of data
hygiene that happens. So every aspect of it, every
step of that particular framework, as we were
discussing, there is a core BA work that that goes into it, the
BA activities that go into it, the BA mindset that goes into
it. So there is again, yeah.
So that is one thing probably as BAS, we can definitely
contribute to the topic. And the other thing you
mentioned was impact. I think, Susan, that is really,
really huge for, you know, the research I was doing when I was
writing the book, one of the examples that I came across when
it when it, I think it's relating to ethics anyway, the,
the, the story, not the story. The, The thing is, the situation
here is when Facebook decided to have a like button, the whole
intention was to kind of spread joy.
So they wanted that somebody, you know, when they like it's,
it's basically spreading joy, but they never ever anticipated
the negative impacts of that. They never thought that that
could be the cause of, you know, teenage suicide as well or any
other the, the, the negative impacts of it.
So again, I always share this as well when today it's just not
the positive impacts that we have to see.
We also have to see the negative impacts and again at different
levels, whether what is the impact positive and negative
when it comes to the project or the value that we are delivering
for a particular stakeholder. And the impacts of the same
thing at the organization level or the society level or even the
ecosystem, because today everything is so very
interconnected, we will actually not know much ahead in advance
what the impacts would be and how this particular feature can
actually, you know, shape up into.
So as much as the analysis goes, that's exactly where I find
this. There's a lot of value that BS
can bring in when they are doing that.
I always say to kind of, you know, we BS were like, you know,
or we, we were conducting the orchestra.
So right now with instead of two or three instruments, now we
have a lot more instruments. So no matter you just take any
particular aspect or any particular team that we are
working, there is that we work. And I mean, I always feel
there's a lot to do now with the expansion.
In fact, we need more BAS because it's expanded.
We as individual BAS have to definitely we need to step up,
we need to learn, study, whatever it is, upscale
ourselves so that we are ready to actually kind of conduct
because definitely in the new landscape, we cannot be experts
in all of those. So focusing on anyone niche, I
think now is the time. And that's exactly what PS can
really, really contribute. And, and I think we can also
advocate for what good use of AI would look like to avoid some of
this cybersecurity slash security.
Maybe it's all cybersecurity these days.
Any kind of security is cybersecurity really.
But I think we can be advocates in our organizations for what
good business analysis for AI can look like and maybe should
look like, because I think, yeah, go ahead.
No, no, I was just saying maybe that is something as IIBA we
should think about it to bring in some yeah, ethical, well
links to it. You know, there's actually on
our website Fabricio Laguna and Michael Lagello have produced a
kind of an opening statement on what business analysis for AI
would look like. So I don't I don't have a link
and I don't know if either of my my marketing Co workers are on,
but if you guys are on, if you can drop in that business
analysis for AI link. If not, I'll make sure that it's
in the in the description of the YouTube.
Somebody also on the comments were asking the link for the
book, so maybe? If you have that.
Yes. Perfect.
I was trying to find it but it it was, I don't know, Google
wasn't behaving for me today. Again, if, if, if they can go
through the link on my website, they get an additional author
discount 30%, which is added benefit rather than going
directly. So that probably would help.
So let me see if I can. I think it's asking me to
connect to the YouTube account to join the chat.
Yeah, let me see. Yeah, when I'm done, I will do
it. Otherwise I can share it with
you. Yeah, yeah.
And then I can include it in the description.
Yes. Well, let's see.
I tell you what, we have sparked a whole lot of conversation over
in our comments. I've I have starred a couple.
I'm just going to, I'm just going to put some of these up.
So you mentioned this was the run.
Rabilani was asking about the the details of the book.
We will get you those. Yeah, I have sent it to you on
the private chat, so maybe whenever.
Yeah, you can add it to the comments.
I I can drop this into the YouTube chat, but I will make
sure that we also drop it in the LinkedIn chat separately.
So there you go Rob Alani, I will respond to you and make
sure that you have that Thank you Let's see so here we have
another human factors in intrigue me as we become too
dependent on AI and lose some of the human elements.
I think that is, I think that is maybe what you see organizations
experiencing right now is they are, boy, we've got a harness
AI, it's going to make a magic happen and then they just trust
it and rely on it too much and don't really get the outcomes
they're looking for. Yeah.
And I think even at an individual level as well, the
more we are relying on it, the more we are losing our
independence. Yeah, I agree.
Or the ability to think for
ourselves. That's what's scary.
Absolutely. I, I myself am experiencing it
that whenever I used to write emails somewhere, you know, I
used to spend the time and go through it, but now I'm finding
it, I'm losing it because I get the help of AI.
But one point which I would like to highlight here when, when we
talk about dependency, Susan is we are, this is again, as part
of the risk workshop that I do, I always highlight normally when
I talk in the workshops or my trainings, we go through a
fantastic risk exercise. It's, it's fun and fantastic.
But one thing that all of us miss is recognizing some of
those fundamental risks, existential risks, because we
think that cannot happen. And one simple example is
unavailability of Internet. Right.
We can never fathom that in our minds that this is a
possibility, but we are experiencing, we are seeing.
I think yesterday day before AWS was down South, Snapchat was not
available and I know my children didn't know how to communicate
with their friends. But I was.
I was. I was having a laugh.
So what would the point I'm trying to make here is, and we
also have our seeing and probably in the last few months
itself, we have heard if we would experience first hand that
there has been some fires here, some unavailability of this
technology. That technology and I know in
the UK itself, repetitively, I think two or three months the
banking systems were down, the payment or the wages wasn't paid
on time, people couldn't honour their direct debits, it impacted
their credit reports. So the whole point I'm trying to
make here is some of those things where we think that this
cannot break. It can break.
That's. Right.
So dependence because today our lives are completely technology
oriented. I think that some people even
wear their watches when they sleep because they want to
monitor even that. And I know one of my children
does that. So the point here is the more we
are dependent, the more we will become helpless when that is not
available. And when I say helpless, it
could be physical, it could be mental, it could be emotional.
And that is where as BAS, you know, like when we talk about,
when we analyse stuff, I think we have to analyse all aspects
today somewhere, you know, our, we have to take those guards and
just look at 365° to actually assess the impacts and analyse
things. Yeah, because because you're
right, it is not just limited to the tool, the organization, the
problem that you're solving, you really have to go beyond that.
And again, I think that's, you know, one of the weaknesses that
we have in our organizations is we really, we've got a lot of
people who are focused on whatever that project outcome
is, you know, reduced loss, increased profit, like whatever
those outcomes are, but at the expense of not sometimes seeing
the problems that are existential that could result in
greater harms to humanity. Yeah, I think probably if we
could learn from COVID or the current wars that's happening
where we didn't even anticipate. But I'm, I'm really glad that
you mentioned this point because this is again one of the common
questions that I get is, you know, we actually don't have
time for analysis and you tell us to do an analyse analysis for
all these levels. When is the time or even if we
do, is it really required because like as you identified,
you know, OK, what's what, what's the benefit that is great
monetary benefit that it's going to serve or what's the profit if
the focus is on that? But then again, I talk about as
ABA, business analysis cannot be seen just as a role.
I always say business analysis is a responsibility.
So whether the organization definitely takes in those points
or not, easy material because I am not the decision maker.
As a business analyst, however, I have a responsibility to
deliver accurate incomplete analysis and that's where my job
stops. So not giving that or giving an
incomplete analysis means I haven't actually done my job
well as a business analyst. So that is where we draw the
line that no matter how the organization sees our business
case for example, we delivering a complete analysis is our job.
Again, the analogies I kind of use is when you go to a doctor,
you believe that whatever you know, the prescribed
prescription or the diagnosis the doctor does is accurate.
So I always feel there is an undocumented trust between the
business analysts and their clients or organization that our
analysis is complete and accurate and that because they
are making decisions based on that.
So maybe out of 10 people, one person or two people might
actually pick up those ethical points or those existential
crisis or the risk that you have raised.
But who knows? That can be a game changer for
the organization. So I think somewhere we cannot
take a reason that if somebody is not taking our analysis, that
doesn't make a stop delivering a complete one.
That's right. Just because they don't accept
what you're providing doesn't mean that it's not valuable.
And so, yeah, I agree with you. And you know, we've, we we've
talked several times about risk management, that part of what
business analysis might look like for AI looks a lot like
risk management. And I just think back to my
working in a PMO for a number of years and I always thought,
well, risk management is the domain of project managers, but
that's not true. That is really not true.
The BABOT guide, which is right here, it's holding up my plan,
but it's very valuable. It talks about risk management
being part of what business analysis professionals do as
well. So that is something that we
should be thinking about, not just how does something work,
how am I putting together requirements for it, How am I
evaluating risk and letting folks know that there are these
potential issues that we might encounter like it.
That's part of the job. Absolutely.
And it is just not business analyst.
I feel it's everybody's responsibility when it comes to
security. I always say it's just not one
person's or it's security team. It's everybody's responsibility.
Similarly, when it comes to risks, it doesn't matter who
raises it or who actually works on it.
It's, it is whoever sees it, they have to raise it.
So it, it is like that. So, and for that, having a
security mindset is fundamental because without that, even if
there is a risk right in front of us, we might not be able to
see because we don't have that lens.
And having that security mindset really really helps to identify
those risks because end of the day, security is nothing but all
the risks taken care. That's right.
So that's why I think we need to, as business analysis
professionals, we need to stop thinking cybersecurity is
knowing the what the threats are, how to use some technology
in order to address those. It is, it is not just that it is
for some professionals in that domain, but for us, it looks
like risk management, it looks like ethics, it looks like data
analysis, it looks like business analysis.
Absolutely. Yeah.
So let's see. So we've got just a couple of
minutes left. I'm just, I'm going to pop in
maybe a couple of other thoughts here.
Here we have, I believe the fear of job loss to AI is natural.
However, AI can't work in isolation.
It's an enabler, does not have human contacts, which is still
needed. And business analysis
professionals provide this. Yeah, thank you.
I think that's, that is definitely a message we want
people to leave with today. Let's see.
And then we've got another one here where the human factor and
impact can never be over emphasized.
I wish more leaders who are thinking about introducing AI or
AI projects into their organization would think this is
a human project. It's enabled by AI instead of
the other way around. Yeah, absolutely.
It just reminds me when I started my career, one of my
seniors told me I was I was a techie, I was coding.
So he said even though you're coding, Bindu, always remember
you're in people's business. No matter what tool, what
technology comes, you're always in people's business.
That means you are doing something that is going to
impact people or a human life. So somewhere I think that really
stuck with me. So it doesn't matter what
technology we use, we are trying to bring a change in a
stakeholder's experience and that is a responsibility.
Yes, Speaking of cyber security, so you've talked about, you do
lots of talks and, and you are writing books in your training.
And so something that I wanted to share with folks because it's
Cybersecurity Awareness month. By the way, October is
Cybersecurity Awareness Month. Every year this month we have an
on demand webinar and it's one of your webinars.
You did it last year. It's elevate your cybersecurity
game. And I've got this new cool QR
code gadget. Let's see, we're going to make
this thing work. Here you go.
So probably some of you are on your device.
I wonder how this works. I have AQR code.
If you can figure out a way to snap a picture of that, it'll
take you over to the page and you can get this on demand
webinar and a little bit of information about Cybersecurity
Awareness Month. And if you aren't able to click
the QR code, don't worry. I will make sure that I put a
link into our chat today. Thanks for sharing this, Susan.
I think I remember when we had it last year, probably it was
one of the webinars, I think around probably 400 or 500
people registered and attended something like that.
So that was actually a big record for me when compared to
those numbers. I I do remember it now, yeah.
And we are already talking about having you come back and do
another webinar on something you're pretty passionate about,
which is IIBAS certificate in cybersecurity analysis.
So it is a certification technically certificate, they're
slightly different, but it's a, it's a program that we've had
for a little, a little while and you actually you do some
preparation training for that as well.
Yeah, that would be good. Thank you for the opportunity.
We're looking forward to having you back.
I will. I will make sure that people
know when we're going to have that.
Let's see. Well, I think that might be it.
Today we want to thank all you guys for for dropping your
thoughts in there. You know what, it's always nice.
I know I have always been a little nervous talking about
cybersecurity because I also believed that it was something
more technical. But let's see here.
There was something that just came in learning.
Thank you. For all the comments I'm I'm
really loving it. I think my take away would be
I'm really learning a lot in this room.
Yeah, Excellent. Yeah, that's the whole idea.
Yes. We want you to really have
tangible takeaways that you can implement it in your work.
Definitely. Yes, yes, exactly.
All right. Well, Bindu it I have, as
always, I have enjoyed catching up with you today and talking
more about cybersecurity and AI. And I know you're gonna be
talking about it more in your trainings and as you are around
the globe talking to people about it.
So thank you for joining us today.
Thank you for the opportunity once again, Susan, as always,
love. I think you said it right in the
beginning. We have that why we connect well
and I think comments are actually so I'm going to spend
some time after the call here to go through the comments that
there's there are there are quite a few and.
There are quite a few really detailed ones I think, that are
right up your alley, yeah. Definitely, I would go through
that. Thank you all for joining once
again and see you all around soon.
I'm on LinkedIn, so if you have any questions, please feel free
to reach out and I would love to hear more stories from you.
Thank you. Thank you.
All right, so that is our episode today.
I hope you've got some takeaways that maybe you will be thinking
about your role in cybersecurity and AI as a business analysis
professionals. You've got a lot of value and a
lot of skills that you can offer.
We'll be back in two weeks and we're going to do a little
something different. We're going to be talking about
your authentic voice and I've got an opera singer, so you're
going to want to come back because there could be singing
and maybe some dancing too, Who knows?
We look forward to seeing you then, so take care.
Bye, bye. If you're hearing this, you made
it to the end of the episode. Thanks for listening.
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