►
From YouTube: OpenShift Commons: Privacy by Design: Transform Data Risk into Business Value with BigID
Description
BigID runs on OpenShift and IBM Cloud Pak for Data
Data and its Privacy is a Strategic Priority
for success in the Digital Age.
● Holistic Privacy Strategies deliver Benefits beyond Regulatory Compliance (GDPR/CCPA)
● Privacy by Design & Default
- Developer Friendly - Privacy Shift Left
- Advent of Privacy Engineers (Technologists)
- Confident Firms apply Data Privacy Best Practices and strong Technology Controls
● Establishing Privacy Governance
- Delivering a Holistic Data Protection Approach is key to Success.
- Transform Data Risk into Business Value
- Data Intelligence & Identity Data Awareness as a “Force Multiplier” to existing Security Frameworks.
● Enterprise Data Privacy & Security Programs need to have Global Scope & Scale Privacy
Special Guests: Alan Taylor, Tim Bayne, Sachin Khungar
Learn more: https://bigid.com/
A
Hi,
I'm
harina
angel
with
the
openshift
product
management
team,
and
we
are
here
today
with
big
id
and
we
have
alan
sachin
and
tim
all
from
big
id
and
they'll
introduce
themselves
in
a
moment.
But
we're
really
excited
about
this
because
they're
using
ai
and
data
intelligence
to
bring
data,
privacy
and
security
and
governance
to
the
openshift
platform,
as
well
as
cloud
pack
for
data
and
really
excited
to
get
to
know
their
platform
better.
So
alan,
please.
B
Yes,
good
day
everybody,
so
this
is
alan
taylor,
I'm
the
global
alliance
vp
here
at
big
id
and
on
the
call
with
me
today
we
have
sachin
who's,
our
global
alliance's
technical
director
and
tim
bain,
also
in
the
technical
side
of
the
world
here
at
berg
id
so
satsune
will
be
doing
some
of
the
slides
and
and
a
demo
of
our
product.
So
your
verdict
open
shift
commons
community
session
today
as
a
brand
new
member
for
those
of
you
that
don't
know
big
id.
B
We
we
generally
deal
with
a
lot
of
the
fortune,
5000
customers
and
we're
proud
to
say
that
fortune,
one
walmart
is
one
of
our
biggest,
so
we
play
with
both
small
and
and
very
large
customers,
so
today
very
pleased
to
present
a
theme
of
privacy
by
design,
as
this
audience
is
probably
a
little
bit
more
technical
and
a
lot
more
developer
focused
we're
going
to
talk
about
privacy
by
design
and
the
advent
of
privacy.
Engineering.
B
Big
id
describes
itself
as
a
modern
privacy,
aware
data
discovery
and
intelligence
platform
and,
as
karina
says,
we
are
based
on
containers
and
kubernetes.
So
that's
the
modern
naming
convention
and
you're
going
to
see
what
we
do
across
three
different
buckets:
both
data
privacy,
data
security
and
data
governance.
B
B
We're
going
to
give
you
a
little
update
on
our
cloud
pack
for
data
status
with
with
the
ibm
team
and
openshift
we're
gonna
talk
a
little
bit
about
our
ecosystem
and
our
alliances.
We
have
our
own
marketplace
and
developer
portal
and
all
of
these
great
things,
so
our
ecosystem
is,
is
very
important
to
us
and
very
strong
and
then
I'll
hand
it
over
to
satchen
to
go
over
the
solution
overview
and
and
finally,
to
give
you
a
demonstration
of
our
product
fairly
quickly.
B
B
You
know
your
privacy
by
design
starts
to
get
you
thinking
about
things
like
privacy
shift
last
and
this
whole
concept
of
privacy
engineers,
but
really
privacy
governance.
You
know
originally,
you
know,
was
very
much
around
regulation,
your
gdpr
ccpa,
obviously,
two
of
the
very
big
ones
for
both
the
u.s
and
in
europe
and
and,
of
course,
there's
similar
regulation
around
the
world
in
different
countries
and
so
designing
by
default
privacy
into
everything.
B
You
do
not
only
has
a
regulatory
and
compliance
mandate,
but
there
is
benefits
to
understanding
your
data
having
data
quality
in
everything
you
do,
whether
it's
ai
analytics
machine
learning,
etc
and
data
intelligence
and
identity
data
awareness
is
very
much
a
force.
Multiplier,
forrester,
talks
about
other
mandates
of
your
a
holistic
platform
being
scalable
and
global
and
you're,
hopefully
going
to
see
that,
through
our
presentation,
this
phrase
of
privacy
engineering,
we
we
at
big
id
wrote
a
blog
on
this
quite
recently
and
you'll
see
here.
B
Your
bit
of
fun
here
make
room
for
the
privacy
engineers
move
over
cpos
and
dpos.
But
again,
privacy
by
design
is
a
set
of
principles
that
embeds
privacy
and
data
protection
into
all
products
and
services
and
privacy.
Engineers
are
the
people
who
are
going
to
implement
this
and,
in
fact,
run
operate
and
what
we're
seeing
in
the
industry
around
data
privacy,
data
security
is
some
industry
standards
starting
to
appear
so
there's
some
iso
standards
which
big
id
are
part
of.
B
B
On
the
right
hand,
side
of
the
screen
you'll
see
a
box
here,
there's
actually
taken
from
carnegie
mellon
university,
where
they
start
to
define
what
a
privacy
engineering
program
looks
like
and
in
in
your
own
time.
You
probably
can
look
through
this
to
say
you
know.
This
is
something
that
you'd
be
very
interested
in
doing
both
in
your
own
organizations
and
personally
and
we'd.
B
B
B
B
We
not
only
want
you
to
think
of
new
applications,
but
the
existing
ones
that
are
already
out
there.
Big
id
has
a
very
strategic
alliance
with
all
of
the
cloud
vendors,
that's
both
from
a
data
source
perspective
because,
of
course
we
are
discovering
data.
So
we
can
tell
you
where
sensitive
data
exists,
but
we
also
integrate
to
many
of
the
cloud
vendors
tools
on
their
platforms.
B
B
We
also
have
a
very
healthy
partnership
with
many
of
the
data
privacy
data
security,
data
governance,
vendors
who
use
our
technology,
especially
as
some
of
our
discovery
technologies
to
augment
and
make
better
what
they
do.
So
some
of
them,
may
you
know
on
paper,
think
you
may
think
of
them
as
competitors
to
big
id,
but
that's
truly
not
the
case.
B
We
are
in
a
very
unique
position
where
we've
developed
this
platform,
this
data
discovery
and
intelligence
platform
across
these
three
buckets
that
you
see
on
screen
data,
privacy,
data
security
and
data
governance,
and
so,
with
that
I'll
hand
you
over
to
to
sachin,
he
might
just
do
a
quick
introduction
and
then
he'll.
Take
you
through
the
product,
followed
by
a
demo.
C
Thank
you
for
the
introduction,
so
I'm
just
gonna
start
off
from
where
ellen
left
off
from
that
perspective
in
terms
of
doing
the
positioning,
so,
firstly,
in
terms
of
the
topic
today,
so
there
are
two
three
topics
that
we
wanted
to
cover.
One
is
in
terms
of
how
do
we
essentially
play
in
the
role
of
privacy
by
design,
and
how
do
we
enable
the
platform
for
a
wider
usage
from
that
perspective?
C
They've
always
been
trying
to
look
at
retrospective
solutions
in
the
current
data
landscape
to
try
and
make
sure
that
you
can
embed
something
somewhere,
but
if
you
have
to
look
at
it
from
a
blue
sky
approach
from
that
perspective,
you
will
always
start
to
look
at
it
from
a
green
field
project
perspective.
So
what
is
it
that
we
really
need
from
a
privacy
perspective
and
hence
central
to
any
of
the
implementation
that
you
will
think
about
from
privacy
by
design
is
to
now
look
at?
Who
are
we
talking
about
privacy?
Is
it
about
individuals?
C
Is
it
about
entities,
and
the
moment
you
realize
that
you
also
realize
that
traditionally,
the
discovery
has
always
been
on
e
on
classification
based,
whereas
in
case
of
privacy,
you
really
need
to
go
to
a
deeper
level
into
the
data
in
itself
to
be
able
to
essentially
create
controls
at
the
data
level.
So
that's
where,
when
we
started
to
create
the
data
discovery
product,
we
thought
that
the
metadata
based
discovery
or
regex
based
discovery
isn't
going
to
be
enough.
C
From
that
perspective,
we
got
to
find
the
business
context
of
the
data
we
got
to
contextualize
any
personal
information
and
that's
where
we
essentially
started
to
look
at
the
different
kind
of
discovery,
methods
that
we
would
use
in
order
to
essentially
achieve
that
goal.
From
that
perspective,
and
hence
any
level
of
data
understanding
that
you're
doing
has
to
be
from
the
perspective
of
centrally
as
an
individual
as
a
person.
C
And
once
we
started
doing
that,
we
then
looked
at
all
the
kind
of
privacy
regulations
that
were
in
place,
which
were
all
consumer
focused
which
were
all
individual
focus.
So
it
came
naturally
for
us
to
be
able
to
now
put
the
solution
in
the
market
in
order
the
compliance
space
and
the
privacy
regulation,
but
equally
by
doing
so
because
of
the
discovery
methods
that
we
are
using
because
of
the
way
we
are
looking
at
the
data,
it
gave
us
deeper
insights
into
the
data
in
itself.
C
Now,
when
we
talk
about
from
our
perspective,
what
are
we
essentially
saying
in
that
respect?
So
right
at
the
core
of
the
platform?
Is
the
data
discovery
foundation
and
we
also
call
it
a
c4
platform
and
the
reason
why
we
say
that
is
because,
in
terms
of
the
data
discovery,
we're
fundamentally
changing
the
way
people
traditionally
has
been
doing
discovery
by
first
of
all,
not
giving
you
one
method
but
multiple
methods
that
can
span
across
all
kind
of
data
system.
C
So
if
you
start
to
look
at
about
coverage
of
the
system
which
I'll
talk
to
in
a
minute
but
really
you're,
looking
at
structured,
unstructured,
semi
structure,
system,
all
kind
of
different
systems
that
you
can
essentially
look
into
into
the
data
assets,
but
from
the
perspective
of
the
data
discovery
methods
that
we
would
use,
we
will
essentially
use
classifiers.
First
of
all,
classifiers
are
traditionally,
people
have
always
relied
on
reject
space
classifier.
C
The
moment
you
start
to
think
about
personal
data.
Those
do
not
work
anymore,
because
you
can't
really
define
rejections
for
finding
somebody's
name.
You
can't
define
regexis
for
finding
somebody's
postcode
or
address.
For
example,
you
need
deeper
data
intelligence
in
that
scenario,
so
for
us,
classifiers
in
itself
was
a
different
kind
of
technology,
so
we
defined
our
own
advanced
classifiers,
which
were
based
on
named
entity
recognition
models
which
we
pre-trained
on
the
context
of
it,
and
hence
we
are
able
to
identify
context
like
names
like
phone
numbers
like
addresses.
C
Those
are
things
that
you
would
essentially
struggle
to
find
otherwise.
Now,
at
the
same
time,
we
wanted
to
also
make
sure
that
the
actual
unstructured
data
we
can
look
at
the
overall
context
of
that
unstructured
data
and
being
able
to
classify
that
into
a
particular
category.
And
hence
we
invented
what
we
call
document
classifiers,
which
are
also
named
entity
based
and
again
trained
based
on
the
model,
so
that
when
we
look
at
a
content
of
the
document
we
can
identify.
If
the
document
that
we're
looking
at
is
a
cv
document,
is
it
a
boarding
pass?
C
We
essentially
looked
at
this
as
a
cluster
analysis
approach,
where
we
group
a
set
of
content
group,
a
set
of
files
together
based
on
the
common
content
that
can
be
identified,
and
that
immediately
gives
you
a
level
of
confidence
that,
whatever
data
that
I
had,
I
can
probably
create
a
smaller
problem
of
it.
If
I
had
thousands
of
file,
I
can
probably
group
them
together
in
smaller
cluster
and
then
look
at
the
cluster
themselves
to
see
what
sort
of
content
do.
C
I
have
to
apply
a
level
of
classification,
on
top
of
it
again,
a
different
kind
of
method
to
look
at
the
data,
but
again
giving
you
a
different
perspective
as
well,
and
then
the
third
discovery
method
that
was
generally
talking
about
from
a
privacy
perspective
is
a
correlation
based
techniques,
wherein
we
look
at
values
of
the
data.
We
look
at
data
values
across
the
enterprise
and
not
only
to
be
identified.
C
Yes,
I've
got
these
phone
numbers,
but
I
now
want
to
go
a
step
further
and
say
whose
phone
number
have
I
discovered,
because
that's
what
is
important
to
apply
any
kind
of
privacy
control
on
top
of
it?
Now
we
use
all
of
these
different
test.
Difficulty
techniques
to
then
create
a
rich
catalog
so
that
you
can
now
have
a
central
view,
central
view
of
the
inventory
central
view
of
all
the
objects
that
you
carry
across
your
enterprise
to
now
understand
on
every
object.
What
is
the
sensitivity
of
data
that
you
carry?
C
You
can
essentially
then
look
at
number
of
use
cases
that
you
can
develop
as
a
result
of
it.
Now.
The
idea
was
that,
having
established
this
kind
of
discovery
foundation,
we
wanted
to
now
go
a
step
further
and
say:
let's
now
create
specific
use
case
based
apps
around
it,
so
we
sort
of
developed
an
app
framework
and
that
there
are
different
kind
of
applications
that
we
develop
depending
on
the
use
cases
both
in
the
privacy
domain
from
the
data
protection
side
of
things
or
the
data
governance
side
of
things.
C
Now,
if
we
were
to
look
at
so
what
else
can
we
do
from
a
privacy
perspective?
Then
we
also
looked
at
also
doing
some
amount
of
consumer
facing
portal
requests.
So
this
is
another
use
case
that,
apart
from
the
code
portal
platform,
we
also
created
this
privacy
portal,
which
can
also
help
individual
customers
and
individual
consumers
also
manage
their
own
request.
It
can
essentially
allow
you
to
do
end-to-end,
workflow
management
of
any
consumer
request
that
may
come
in.
How
do
we
accomplish
that?
How
do
you
manage
that
within
organization?
C
So
that's
again,
another
specific
sas
based
product
that
we
have
recently
launched
to
get
into
that
particular
aspect
now.
Equally.
Another
specific
aspect
of
regulation
is
also
understanding
how
you
manage
your
third-party
data,
how
you
share
that
third-party
data,
so
we're
also
helping
in
terms
of
building
that
kind
of
a
picture
based
on
the
insights
that
we
are
producing,
based
on
the
kind
of
data
that
we
can
essentially
understand
that
we
can
find
out
from
these
different
systems
all
together
and
then
more
recently,
we
started
looking
at.
C
How
can
we
essentially
look
at
scanned
images
scan
pdfs,
for
example,
to
also
be
able
to
identify
personal
information
in
it?
So
we
have
created
our
passes
to
now,
also
be
able
to
look
at
ocr
based
images
essentially,
hence
create
a
text
parsable
image
and
then
be
able
to
report
based
on
that
as
well.
C
Now
this
is
an
additional
one
that
we
are
now
trialing
as
well.
Now,
one
of
the
key
thing
when
we
do
the
unstructured
space
discovery
is
the
volume
of
the
data.
Everybody
has
huge
mountains
of
volumes
of
data.
How
do
we
essentially
reduce
the
amount
of
data
that
we
have
to
scan
from
that
perspective?
So
this
is
where
we
are
now
coming
up
with
a
different
approach
altogether
called
hyperscan,
where
we
are
going
to
be
investigating
the
data
assets,
we're
going
to
be
looking
at
learning
from
the
data
assets
themselves
to
be
able
to
predict.
C
Now,
in
a
way,
this
essentially
works
in
the
way
that
we
can
essentially
do
a
sample
scan
on
those
assets
within
the
system
and
we'll
create
a
sort
of
metadata
model
with
that
sample
scan,
which
will
then
drive
our
generate
predictive
model
on
that
particular
asset,
and
then
it
will
start
to
create
predictions
on
top
of
it.
So,
in
a
way,
sorry,
what's
the
question.
C
Now
this
is
where
we
have
almost
like
three
different
dimensions.
We
are
going
in
terms
of
providing
those
developer
tools.
Firstly,
we've
created
almost
like
a
community
portal
in
itself
for
developers,
wherein
you
would
essentially
be
able
to
find
lots
of
useful
information
about
the
apis
about
the
sdks
or
even
creating
your
own
apps.
There
are
a
bunch
of
apps
that
are
already
community
content
that
are
available
there
as
well,
but
really
from
the
perspective
of
your
launchpad.
This
exchange
community
becomes
your
launchpad
from
a
developer
perspective.
C
Now,
when
we
talk
about
apis
in
general,
the
idea
for
us
in
terms
of
creating
that
api
layer
was
that,
whatever
system
that
we
connect
to,
we
should
be
able
to
essentially
expose
the
information
being
discovered
from
those
through
the
apis.
From
that
perspective,
and
from
that
perspective,
the
whole
platform
in
itself
is
api,
enabled
everything
that
will
basically
go
through
in
the
demo,
which
I'll
show
you
in
a
minute.
C
You
would
also
be
able
to
use
that
information
through
apis,
which
is
how
traditional
we
have
essentially
competed
or
integrated
with
other
kind
of
isp
partners,
and
equally,
we
are
able
to
essentially
provide
the
level
of
data
insight
that
you
may
need
for
creating
your
data
pipelines.
You
may
need
to
essentially
look
at
the
metadata
in
the
data
catalog,
for
example,
or
even
from
the
perspective
of
what
data
is
shared,
how
it
is
monitored,
any
level
of
insight
that
you
want
to
produce.
C
We
can
essentially
do
it
from
the
api
layer
now
from
an
api
perspective.
What
we
are
also
doing
now
recently
is
that
we
are
also
exposing
our
scan
apis
so
that
you
can
use
the
scan
apis
as
a
developer,
embedded
in
your
applications
to
be
able
to
discover
and
classify
your
data
as
it
essentially
is
being
used.
C
This
is
especially
helpful
if
you
have
etl
workloads
or
you
would
essentially
want
to
essentially
look
at
the
payloads
asynchronously,
and
that
is
where
this
particular
api
can
be
especially
useful
in
order
for
you
to
essentially
scan
the
pipelines
in
order
for
you
to
embed
that
in
etl
tools
or
application
in
itself,
where
traditionally,
it
would
be
difficult
to
discover
data
from
that
perspective,
now,
just
just
going
above
from
the
api's
perspective
and
landing
into
the
data
coverage
aspect.
So
today,
in
terms
of
the
kind
of
data
assets
that
we
do
discovery
against.
C
As
I
mentioned,
they
are
all
kind
of
structured,
unstructured,
semi-structured
systems,
even
enterprise
applications
as
well,
but
the
idea
is
that
we
want
to
essentially
make
sure
it
is
a
scalable
approach.
So
if
today
you
have
a
set
of
legacy,
applications
for
which
we
don't
have
a
connector,
we
want
to
make
sure
that
you
have
the
developer
tools
to
create
your
own
connector
as
a
result
of
it
and
that's
where
we
essentially
expose
our
own
sdk.
C
From
that
perspective,
we
also
have
a
generic
api
based
or
rest
api
based
sas
connector,
which
only
requires
configuration,
so
you
can
pretty
much
create
your
own
configuration
for
a
sas
service
put
in
the
api
endpoint
that
can
be
used
to
scan
information
on
that,
and
here
you
go
essentially
in
terms
of
creating
your
own
connector
from
that
perspective.
So
not
only
are
we
essentially
trying
to
cover
as
many
different
systems
by
having
our
own
connector
factory
produce
additional
connectors.
C
We
want
to
make
sure
that,
as
a
developer,
you're
also
able
to
create
your
own
connectors
as
a
sas
solution.
It
is
easier
for
you
to
connect
to
those
systems
as
well.
As
a
result
of
it
now
just
coming
back
to
the
app
framework
in
itself,
so
this
is
where
we
traditionally
started
to
look
at
specific
use
cases
where
we
may
have
a
role
to
play,
and
hence
these
are
some
examples
of
the
apps
that
we
have
developed
on
the
platform
such
as
in
the
privacy
space.
C
You
essentially
can
manage
the
consent,
essentially
look
at
the
consent,
management
from
a
privacy
angle,
tied
back
to
the
consumer
rights,
so
that
you
can
essentially
implement
that
within
the
applications.
You
have
access,
request
management,
app
to
essentially
understand
and
look
at
the
data
from
individual
perspective.
C
We
already
created
specific
connectors,
which
are
essentially
subscribing
to
events
on
those
data
pipelines,
then
monitoring
the
payloads
whenever
those
come
in
on
those
particular
events,
and
once
there
is
some
payload,
they
can
essentially
push
that
data
payload
back
into
the
system
to
do
a
discovery
and
classification
and
then
based
on
the
results
of
that
discovery,
you
can
essentially
manage
the
workflow
from
those
data
pipelines
as
a
result
of
it
as
well.
So
that's
how
we
essentially
change
the
model
in
terms
of
the
way
that
you
look
at
in
terms
of
the
data
pipeline.
C
The
moment
you
push
the
data
from
the
producer
into
the
pipeline.
We
can
essentially
consume
the
payload,
get
it
through.
The
scanning
process
correlate
it,
and
hence
you
have
a
level
of
metadata
available
for
you
to
essentially
make
your
decisions
based
on
what
is
the
sensory
level
of
the
data,
and
how
do
you
manage
essentially
pushing
that
information
back
into
the
system
as
a
result
of
it?
C
C
So
we
start
off
at
the
top
level.
In
this
case,
we
have
15
different
systems
that
we
have
configured,
and
these
are
all
a
mixed
of
different
kind
of
systems
that
we
have
so
you'll
see.
We
have
sharepoint
mysql
postgres
snowflakes
kafka
pipeline.
These
are
some
of
the
systems
that
we
essentially
used
in
order
to
discover
information
against
it.
Now
beyond.
C
Looking
at
these
configured
data
systems,
now
as
a
part
of
the
discovery,
we
will
now
start
to
establish
which
one
of
them
have
any
kind
of
relevant
information
now
in
privacy,
specific
information
we're
looking
for
pi
we're
looking
for
personally
related
information.
That
is
what
this
is
quantifying
in
this
particular
case,
that
14
of
these
systems
have
some
amount
of
earth
information
that
I've
discovered
from
these
data
assets
so
right
at
the
top
level,
we're
now
down
to
from
15
systems
to
14
system.
C
From
that
perspective,
now,
within
these
14
systems,
then
we
go
down
to
the
underlying
objects
and
again,
when
we
say
objects,
depending
on
the
class
of
system
that
we
have,
these
may
be
different
if
you're
talking
of
structured
system
we'll
be
talking
about
tables
or
views.
If
we're
talking
of
unstructured
systems,
we're
talking
of
files,
if
you're
typing
of
emails
or
messaging
servers,
then
we're
talking
of
actual
emails
and
messages
being
objects
as
well,
so,
depending
on
the
underlying
system,
that's
how
we
classify
these
objects.
C
So
in
this
particular
case
we
had
1.1
billion
records
again
when
we
talk
about
records,
we're
talking
of
individual
values
in
this
particular
model,
we're
talking
about
individual
values
of
addresses,
names,
phone
numbers,
etc.
That's
the
lowest
level
of
granularity
that
the
system
will
discover
information
against
and
again
in
between
these
layers.
We
also
have
the
fourth
dimension,
which
is
where
the
individuals
really
come
in
from
privacy
perspective
that
we
now
essentially
start
to
establish
what
information
have
we
discovered
and
who
does
it
belong
to?
C
So
from
these
perspectives,
the
system
will
now
go
down
a
step
further
after
identifying
these
records
is
going
to
now
correlate
all
the
information
back
to
the
individuals
or
entities
to
which
it
belongs.
To
and
again,
the
classes
of
entities
in
itself
is
configurable
in
the
product
as
well.
You
can
define
whether
the
entities
should
be
consumers
should
be,
customers
should
be,
employees
should
be
patient
should
be.
Insurance
policy
doesn't
have
to
be
personal,
for
example,
it
can
be
non-personal
information
as
well.
C
You
can
decide
to
correlate
information
to
products
assets
any
kind
of
entities
from
that
perspective,
but
again
the
idea
is
that
you're
doing
discovery
at
such
a
granular
level
that
you're
identifying
the
record
you're
also
correlating
back
to
the
entities
where
it
belongs
to
to
give
you
a
much
more
granular
view
of
the
system.
From
that
perspective,
now,
when
we
do
this
level
of
classification
from
a
privacy
perspective
or
from
a
regulation
perspective,
what's
also
important
for
you
is
to
also
understand
the
residency
of
the
data
or
individuals
for
which
the
information
has
been
discovered.
C
So
from
that
perspective,
as
part
of
an
additional
classification,
the
system
also
identifies
whether
the
data
is
being
discovered
is
of
canadian
citizens.
Is
it
of
brazilian
citizens?
Is
it
of
chinese
citizen,
for
example?
So
you
get
that
level
of
granularity
in
terms
of
determining.
What
exactly
are
you
looking
for
from
that
perspective?
C
So
this
level
of
determination
can
also
be
done
at
a
country.
Level
can
also
be
done
at
a
state
level,
so
in
case
of
u.s,
we
can
essentially
do
it
at
a
state
level
to
be
able
to
essentially
identify
whether
the
data
that
we
have
is
of
californian
citizens,
texan
citizens
and
so
on.
So
you
can
decide
what
level
of
granularity
you
can
essentially
go
to
from
that
perspective.
C
Now,
beyond,
looking
at
this
statistical
view,
we
can
essentially
now
go
back
into
a
more
granular
view
of
this
inventory,
which
essentially
gives
you
a
much
more
deeper
insights
into
the
data.
So
now
we
are
looking
at
these
records
themselves
and
trying
to
see
the
distribution
of
those
across
the
different
residencies,
the
different
attributes
that
have
been
discovered,
the
different
data
sources
as
it
belongs
to
and
then
the
same
kind
of
a
geospatial
map,
but
it
is
basically
filter
based
on
the
criteria
that
we
might
want
to
adopt.
C
So
if
I
were,
for
example,
interested
in
knowing
and
going
back
into
the
chart
system,
I
can
see
within
the
hr
system,
I've
got
different.
Eight
attributes
of
information
there
are
about
117.4k
records
access.
I
can
then
even
go
down
to
a
much
more
granular
fashion.
If
I
wanted
to
and
say
that
I
wanted
to
identify
information
about
brazilian
citizens
in
hr
system,
for
example.
C
I
can
then
essentially
go
down
to
that
level
of
detail
as
well,
and
at
that
level
I
can
now
start
to
identify
the
underlying
objects,
the
actual
records
of
information
and
equally
the
actual
entities.
So
these
are
all
individuals
that
are
brazilian
citizens
whose
data
exist
in
hr
system,
so
you
are
able
to
essentially
create
any
level
of
deeper
insights
from
the
system
in
itself,
depending
on
what
level
of
classification
you
wanted
to
adopt,
or
what
level
of
granularity
you
wanted
to
adopt
now.
C
C
So
that
establishes
the
two
key
components
in
terms
of
going
into
doing
the
discovery
creating
this
inventory,
but
really
from
a
data
governance
point
of
view.
This
is
where
you
now
start
off
to
essentially
get
insights
at
a
catalog
level.
Now
this
catalog
is
an
inverted
view
of
the
inventory,
but
it
really
goes
down
to
the
individual
systems
that
you're
looking
at
from
that
perspective,
and
then
each
every
object
that
you
have
in
that
system.
C
You
can
essentially
get
to
that
level
of
information
at
a
more
granular
level,
so,
for
example,
in
this
financial
object
that
was
discovered
at
a
column
level,
I
can
identify
what
are
the
technical
metadata
elements
that
it
carries,
but
equally,
I
can
now
also
go
down
to
that
level
and
identify
what
relevant
information
has.
I
discovered
in
these
data
elements
themselves.
That's
where
this
attribute
mapping
really
comes
in.
C
So
if
I
were
just
to
hover
around
at
an
attribute
section,
this
would
now
help
me
highlight
what
sort
of
information
are
we
discovering
in
these
columns,
for
example,
so
we've
got
things
like
card
numbers,
15
transaction
amount,
transaction
numbers,
user
identifier,
email
addresses
ip
addresses
now
these
are
all
being
discovered
by
the
different
discovery
techniques,
which
is
why
you
essentially
see
some
of
the
different
prefixes
coming
in
things
which
are
prefixed
by
classifier.
These
are
a
result
of
classifier
based
discovery,
things
which
are
prefixed
by
enrichment
base.
C
This
is
based
on
the
proximity
analysis
that
we
would
do
across
the
data
sets
to
be
able
to
identify
additional
fields,
things
which
have
a
proper
logical
name
like
emails,
residence,
user
identifier.
These
are
all
based
on
our
training,
the
system
based
on
the
data
values
or
a
correlation
based
discovery.
There
are
multiple
different
discovery
techniques
that
have
been
used
on.
C
C
Now
this
was
an
example
of
a
structured
asset.
We
can
also
look
at
an
unstructured
asset,
for
example,
so
I'm
just
gonna
pick
up
an
example
of
sharepoint
in
this
particular
case
and
try
and
establish
how
what
level
of
information
can
be
derived
at
a
file
level.
So,
at
a
file
level,
you'll
basically
see
some
additional
things
coming
up
here,
such
as
a
document
classifier
so
earlier.
I
also
talked
about
content
level
classifier,
but
also
document
level
classifier.
C
So
as
an
overall
content
of
this
document,
it
can
essentially
establish
that
this
was
a
cv
document
based
on
the
similarity
groupings
it
also
identified
which
cluster
this
particular
document
was
essentially
put
in.
So
that's
additional
level
of
information
that
you
will
get
at
an
unstructured
file
level
now.
Equally,
if
I
were
to
just
hover
around
the
attribute
section,
this
will
now
again
go
back
to
the
content
level,
where
it
will
say,
I've
discovered
full
names
and
emails.
C
I've
also
discovered
email
addresses,
and
here
you
will
see
the
prefix
ner,
which
essentially
shows
that
some
of
these
classifiers
were
essentially
discovered
based
on
the
ner
based
recognition,
rather
than
the
reject
space
recognition.
The
full
names
and
country
and
city
fields
were
identified
based
on
ner
based
model,
email
was
identified,
based
on
a
regex
based
model
emails
and
full
names
were
identified,
based
on
correlation
based
search
and
as
an
overall
document
classification.
C
C
So
we
look
at
the
classified
based
tricks,
discovery.
This
is
where
you
can
now
get
to
the
next
level
of
detail
where
you're
now
starting
to
look
at
the
specific
classifier
and
where
you
find
those
specific
findings
and
again
in
this
particular
case,
these
are
database
classifiers,
which
are
both
project
spaced.
But
equally
there
are
all
ner
based
classifiers
as
well.
C
Then
there
are
metadata-based
classifiers,
which
again
something
that
you
can
identify
only
on
the
objects
themselves,
where
you're
looking
at
metadata
within
that,
and
then
there
are
document
level
classifiers,
which
I
said
there
are
about
18
to
20
of
them,
which
are
already
pre-trained
within
the
system.
In
order
for
you
to
establish
that
these
four
documents
look
like
financial
statement
documents
or
these
four
documents
look
like
invoices,
for
example,
and
again
that's
based
on
what
we
have
already
trained
the
model
for
it
to
identify
such
classifications
and
the
model
in
itself
can
be
further
trained.
C
You
can
create
your
own
additional
classifiers
in
order
for
you
to
essentially
then
create
your
own
kind
of
custom
classifiers,
which
you
can
either
make
it
part
of
the
product
or
leave
it
at
specific
things.
From
that
perspective
as
well,
and
then
we
talk
about
cluster
analysis,
which
essentially
helps
us
to
look
at
the
similarity
of
the
data
from
that
perspective.
C
Now,
when
we
look
at
the
similarity
of
the
data
in
a
raw
form,
what
the
system
does
is
it
essentially
groups
the
actual
files
together
based
on
the
common
content?
So
in
this
particular
case
you
see,
seven
of
these
files
were
grouped
together
because
they
all
had
these
common
keywords
now
by
default.
The
name
of
these
cluster
would
just
be
the
appended
list
of
these
keywords,
but
as
a
business
user.
The
idea
is
that
you
would
essentially
come
up
to
this
particular
case.
C
So
there
are
a
number
of
different
use
cases
that
you
want
to
essentially
use
in
this
particular
model,
but
the
most
relevant
one
in
this
particular
case
is
to
auto
classify
these
grouping
of
files
by
essentially
first
of
all,
grouping
them
into
cluster,
and
then
you
essentially
apply
a
level
of
classification
at
each
cluster
to
be
able
to
essentially
then
get
to
the
granular
level
and
hence
being
able
to
adopt
a
more
automated
way
of
flashing
or
a
more
automated
way
of
classifying
these
documents.
Together.
C
C
Things
like
jira
integration,
which
allows
synchronization
of
tasks
between
jira
and
us
things
like
service
now,
integration
which
again
there
are
multiple
actions
that
you
can
take
from
a
service
now
perspective
and
again,
these
are
all
api
level
integrations
that
we
have
done,
for
example,
importing
the
data
sources
from
the
service
now
cmdb
syncing,
the
tickets
or
inputs
into
the
service
now
or
even
pushing
the
actual
data
sources
or
asset
information
back
into
servicenow
as
well.
So
these
different
kind
of
applications
are
different
connotations.
C
Some
of
these
are
more
security
related,
such
as
integration
with
password
vault,
like
hashicorp,
cyber
off,
etc.
Some
of
them
are
more
in
the
discovery
domain.
We
can
can
do
network
discovery
as
well
as
a
result
of
it,
and
then
there
are
others,
for
example,
which
can
essentially
help
us
do
things
like
breach
analysis,
so
the
breach
response
app
essentially
helps
us
do
a
breach
analysis.
C
This
microsoft
aip
essentially
allows
us
to
do
labeling
based
on
microsoft
labels.
We
have
the
access
intelligence
dashboard,
which
helps
essentially
to
look
at
over
permissioning
of
data
sets
so
beyond.
Looking
at
the
data
themselves,
it
also
essentially
highlights
where
some
of
those
data
sets
may
be
over
permissioned
and
hence
available
to
the
whole
organization,
to
give
us
a
view
of
the
risk
frameworks
as
a
result
of
it.
C
So
there
are
different
kind
of
applications
that
we
have
behind
the
scenes
in
order
for
us
to
essentially
work
and
establish
based
on
these,
so
that,
hopefully
gives
you
a
good
flavor
of
the
product
or
the
way
it
is
positioned.
So
far.
If
there
are
any
questions,
we
can
probably
open
the
floor
for
any
specific
q.
A
we
have
at
this
point.
A
B
A
So
I
mean
this
platform
looks
to
be
really
helpful
for
just
getting
organizations
to
go
from
policy
to
code
to
qa
and
providing
that
real
business
value
and
just
going
beyond
the
mere
theory
on
how
to
build
privacy
into
products
and
processes
in
your
application.
It's
it's
just
incredible
what
you
all
have
done
and
built.
B
Well,
thank
you.
You
know
it
was
a
great
vision
four
years
ago,
and
here
we
are
delivering
on
that
vision
and
you're
like
yourself,
when
we
present
it
to
partners
to
customers
even
to
the
analyst
community
and
whatever
it.
It
gets
the
same
reaction,
and
you
know
it
took
a
complete
rethink
of
of
the
world
that
we
live
in
and
you
know
and
making
it
look
simple,
as
is
hard
work,
as
we
all
know,
when
we
don't
stuff,
but
from
our
discovery
and
depth,
which
is
the
which
is
the
foundation
for
all
things.
B
Your
your
people
on
this
call
can
take
huge
advantage
of
that
discovery
in
depth.
I
mean
we
recall
last
week
with
a
partner
who
really
was
you
know,
lots
of
discovery
products,
but
it
was
really
struggling
with
unstructured
data
and
we
we
took
them
through
the
story
myself
and
satchen
and
we
showed
them
what
was
possible
and
again
they
thought.
Oh
god,
we
didn't.
We
didn't
even
know
a
product
like
this
existed,
so
you
know
when
you
can't
get
to
all
of
your
data,
then
you
know
that's
your
weakest
link
effectively.
A
I
think
you've
left
them
speechless.
This
is
usually
when
you
know
we
know
it's
been
a
really
good
presentation
and
we
have
to
go
back
and
digest
it,
and
I
would
love
to
have
you
back
again
and
we
can
have
some
more
discussions
around
privacy
and
everything
that
you've
talked
about
because
there's
a
lot
here.
Where
do
you
see
it
going
from
here.
B
B
As
I
said
earlier,
I
think
the
the
whole
reason
for
ibm
acquiring
red
hat-
and
you
know
this
whole
delivery
on
a
multi-cloud
environment
is-
is
extraordinary.
I
think
it's
a
great
strategy
and
you
know
we,
you
know
we've
been
lucky
that
right
from
the
beginning,
you
know
we
defined
you
know.
An
architecture
was
not
only
scalable
and
global,
but
it
was.
It
was
based
on
containers
and
kubernetes.
B
So
you
know
we
we
already
have
customers,
as
I
said
earlier,
using
our
technology
in
an
open
shift
environment
and
your
we'd
love
to
give
it
to
the
community
and
and
really
deliver
against.
You
know
what
our
topic
of
the
day
was:
is
privacy
by
design
and
privacy
engineering
and
show
people
how
easy
it
is
to
take
what
we've
built
our
ecosystem.
B
There's
a
huge
amount
of
heavy
lifting
been
done,
whether
it's
sap
or
cloud
vendors
or
some
of
the
other
isvs
that
you
will
see
in
your
customers.
So,
a
lot
of
that
heavy
lifting
and
integration
works
already
been
done
and
it's
just
drag
and
drop
and
bring
it
into
your
flows.
So
you're
a
call
to
action
for
us
is
your
at
the
end
of
the
slides.
You
have
my
contact
information.
You
have
sanctions,
you
know
we'd
love
people
to
reach
out
to
us.
B
We'd
love
some
comments,
we'd
love
to
see
a
whole
bunch
of
people
getting
on
our
training.
I
mean
we
through
the
covent
period,
we've
been
doing
a
huge
amount
of
training,
and
it's
all
free
and
people
have
been
well
have
been
stuck
at
home,
I've
taken
time
to
do
our
training
and
start
to
move
through
the
certification
process.
So
we
we'd
love
to
even
create
a
dedicated
session
for
the
open
shift
community
and
run
a
training
course,
perhaps
just
for
the
openshift
teams.