BRUNO AZIZA: Hi, everyone.
This is Bruno Aziza, and I'm here with Shan,
and he is the brain behind the anomaly detection design
pattern that we are launching this week.
If you saw our earlier videos, these design patterns
are designed to help you get started
and develop a solution to a business problem you have.
So Shan, thank you so much for taking the time with us today.
Tell us, first of all, what is anomaly detection
and why does it matter?
SHAN KULANDAIVEL: Thanks for having me, Bruno.
I appreciate it.
First, I need to let you know that I've
had three cups of coffee today in the hope
that I can show up here with at least half as much energy
as you do, but I don't think it's going to cut it.
I need to figure what you have for breakfast
and have that for breakfast, lunch, and dinner.
But anyway, with that, let's talk
about anomaly detection, right?
Let me ask you this question.
Let's assume this is a regular working day
and you have to drive in to work, right?
You probably saw hundreds and hundreds of cars on the road.
What are the one or two cars that you're going to remember?
BRUNO AZIZA: Yeah, that's true.
I mean, my commute was actually pretty easy this morning.
But you're right, if I drive to work, I don't know.
I'll probably remember a couple of cars.
Maybe I saw three red cars?
But apart from that--
SHAN KULANDAIVEL: Yeah, maybe--
BRUNO AZIZA: --I don't know.
SHAN KULANDAIVEL: --one with a flashy paint or maybe one
that was outsized, right?
The small, on the smaller side, or a large car, right?
Let me give you another scenario.
I've got an 18-month toddler at home.
She's a bundle of energy, always running around doing things,
asking for things.
And she's around us, right?
So she stays in the background, and then we
go on with our life.
But the minute that she's out of sight
and we don't hear anything from her,
that's when we know she's up to something, right?
She's probably getting ready to test out whether electrocution
is fake news or not.
Like, she's probably grabbing something,
trying to insert it into the power socket.
Now, what is common between these two scenarios?
It is the fact that you've got a constant stream of data coming
at you, but the data that is outlier,
or that is unlike the others, is the one
that is more [INAUDIBLE].
It gives you valuable perspective,
tells you something new, and, in my case,
potentially helps me avoid danger.
Now, that's basically what anomaly detection is, right?
Now, beyond those simple, straightforward examples,
there's a lot of applications for our customers.
Let me give you a few examples.
Let's say you're an e-commerce provider.
Monitoring your transaction data,
monitoring your card data, monitoring your [? auto ?]
information, your monetary information,
real-time [INAUDIBLE],, you're going
to be able to detect fraud much better
and prevent fraudulent activities before it's
too late.
Here is another example.
Let's say you're a manufacturing company, right?
You've got hundreds and hundreds of equipment
in your factory floor.
Now being able to analyze sensor data that
is coming from those equipments in real time
and being able to detect anomalous patterns
means you're able to schedule proactive maintenance for maybe
equipment that needs maintenance.
Or you can pull out equipment from your factory floor
before it's too late.
Always, you are going to be able to--
you're going to be dealing with expensive repairs.
Or even, in some cases, you'll have
to shut down your entire factory floor,
leading to significant costs.
BRUNO AZIZA: We talked about e-commerce here.
We talked about manufacturing.
But, of course, anomaly detection
is appropriate in financial services,
in telecommunications.
So many of the folks listening to us
will be able to apply that for their business, really,
regardless what industry.
So you wrote a great detailed blog
on the techniques being used and how people
can take advantage of it.
Take us through some of them.
SHAN KULANDAIVEL: We've published three
different patents as of today.
So the first one, we worked with the telecommunications customer
to build a network log-based anomaly detection--
our intrusion detection system.
So this customer had a rule-based system in place,
right?
So rule-based systems are when you
know of a specific pattern or a [INAUDIBLE] or an intrusion,
and then you codified that and used this, right?
That works great, but the significant drawback
of that is it can only detect known packets, right?
Now, we all know the dynamic world that we live in, right?
There are new forms of internet code attacks every day.
It's a constantly evolving landscape.
And so we work with the customer to implement AI technique
called [INAUDIBLE] clustering.
It's basically unsupervised [? knowledge ?] technique
to implement in our network detection system
based on their NetFlow logs.
Let me give you another example.
So we work with a customer who is basically a SAAS platform.
Their additional trust and safety platform basically
provides anomaly detection as a service for their customers,
who are mostly in financial services,
and e-commerce, and digital commerce leaders.
So we-- that customer was able to [? liberate ?] some
of the patterns that we publish to basically get up
and running, roll out a production system.
And in fact, they were able to basically go
to market and scale and meet the needs of some
of their very large customers.
And their customers are some of the biggest names
that you would think of when you think about digital commerce.
So what we are seeing is customers
are now able to start, not necessarily from the ground
floor--
they've already got the scaffolding in place.
They've got a system that is architected
based on best practices.
They're able to bring their knowledge of their business,
their domain, and really finetune the system so
that they are able to better serve
the needs of their customers.
At the end, that's what we want customers to be able to do,
right?
It doesn't make sense for every single customer
to cobble together a system, spend a lot of energy building
out a system.
They should really be focused on how
do they bring their knowledge of their business, things that
are unique to the business, and apply that to the patterns
that we provide so that they are able to see value really,
really quickly.
BRUNO AZIZA: That's right.
Well, Shan, thank you so much for taking the time with us
today.
There is a blog that's coming at you
with the reference to all these techniques and these customer
examples.
And like Shan just explained, the goal
here is to take best practices we've learned from customers
and then give you the working code, the samples,
the technical guidance so you can apply this
at your own company and accelerate
your [? pass ?] to value.
Shan, thank you so much for the time, again.
The links are down here.
We want to know what you think about these patterns,
and we also want to know if there are patterns
you want us to develop.
Don't be shy, put them in comments,
and we'll talk to you very soon.
SHAN KULANDAIVEL: Fantastic.
Thanks, Bruno.