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Description
OpenShift Commons Gathering at Kubecon/EU
May 4th, 2021
Title: Using Text Analytics and AI to reduce GDPR risk - Filippo Sassi (Version1)
Speaker: Filippo Sassi (Version1)
Abstract:
Want the power of text analytics but can't or won't use cloud-based services? No problem! In this talk, Filippo Sassi (Version1) will tell you how we leveraged the power of open source technology to mitigate GDPR risk, using best of breed open-source text analytics and AI services. Deployed on our customer's Openshift cluster, this solution now provides a broad text analytics capability which will be applied to many use cases beyond GDPR.
Discusses implementation at Ireland's Department of Agriculture, Food and the Marine https://www.gov.ie/en/organisation/department-of-agriculture-food-and-the-marine/
https://commons.openshift.org/gatherings/OpenShift_Commons_Gathering_at_Kubecon_EU.html
A
Okay,
so
thanks
anyone
for
making
the
time
for
joining
this
presentation
today,
during
this
session,
I'm
going
to
introduce
you
one
of
the
application
we
we
have
implemented
for
one
of
our
customers
and
the
application
makes
use
of
text
analytics
and
artificial
intelligence
to
reduce
the
risk
of
gdpr
breaches.
But
before
diving
into
that,
let
me
introduce
myself.
My
name
is
filippo
sassi.
I
am
a
senior
software
engineer.
I've
been
working
in
the
industry
in
this
industry
for
quite
a
few
years.
A
A
We
believe
that
this
is
what
makes
version
one
different
and,
more
importantly,
our
customers
agree
on
these
slides
some
stats
about
version
one.
The
interesting
thing,
I
suppose,
is
the
quick
growing
rate
on
some
of
the
figures,
and
I'm
not
gonna
lie
to
you
to
create
this
deck.
I
reused
some
of
the
slides
from
a
private
presentation.
We
ran
in
october
2020.
A
A
Dapham
is
the
irish
government
department
of
agriculture,
food
and
the
marine
defense
vision
is
to
be
an
innovative
and
sustainable
agri-food
sector
operating
to
the
highest
standards.
Daffon
is
one
of
the
oldest
version,
one
customers
and
version.
One
provides
many
teams
dealing
with
the
different
advanced
schemes,
applications
and
more
one
of
these
teams
is
the
bps
team.
Bps
stands
for
a
basic
payment
scheme.
A
In
the
last
couple
of
years,
da
fam
has
invested
heavily
in
the
openshift
container
platform.
This
choice
was
primarily
justified
by
one
of
the
key
strategic
aims
for
the
department
to
provide
a
capability
for
fast,
flexible
application
deployment
and,
at
the
same
time,
to
be
responsive
to
changing
and
emerging
needs
over
time.
A
All
of
these,
while
focusing
on
small
products
that
can
be
designed
quickly,
iterated
and
released,
often
in
particular,
the
openshift
container
platform,
was
a
suitable
choice
for
the
product.
I'm
sure
going
to
introduce
you
because
there
was
real
concern
about
using
public
cloud
services
to
scan
and
analyze
documents
which
might
contain
personally
sensitive
information.
A
The
this
solution
reaffirmed
the
department
believes
that
the
investment
in
the
openshift
platform
would
provide
long-term
strategic
gains
in
line
with
the
public
service.
Ict
strategy.
Dapham
is
focused
on
digital
transformation,
including
both
front-end
and
back
office
transformation
to
deliver
services
for
citizens,
businesses
and
the
government
from
may
2018.
A
The
general
data
protection
gdpr
regulation
came
into
effect,
requiring
businesses
to
protect
the
personal
data
and
privacy
of
the
european
citizens
for
any
transaction
that
occurs
between
the
european
member
states
and
in
line
with
these
requirements.
With
this
regulation,
one
of
the
fan
priority
for
transformation
was
to
protect
the
personal
data
for
not
only
the
different
customers,
but
also
for
the
customers
of
the
public
service
as
a
whole,
and
in
particular
we
consider
the
following
use
case
and
to
receive
to
receive
grant
payments.
The
farmers
must
upload
various
documentation
through
the
department
website.
A
A
A
The
labs
is
a
video
of
the
service
that
version
provide
to
its
customers
to
explore
disruptive
technologies.
A
couple
of
points
to
note
here,
so
it's
a
version,
one
customers.
That
means
that
whatever
we
do,
we
do
it
for
the
clients
which
are
already
within
the
version
1
customers
base
and
for
them
we
are
a
value-added
service.
So
we
are
free
of
charge
that
doesn't
mean
that
we
are
free
of
cost.
Indeed,
we
are
expecting
to
use
their
data.
A
We
are
expecting
to
use
their
resources.
This
will
have
particularly
an
impact
on
cost.
If
we
decided
to
go
cloud,
we
are
expecting
to
interview
their
employees
to
better
list
the
requirements,
we're
expecting
them
to
test
the
pov
and
finally,
we're
expecting
at
least
one
person
from
the
customer
side
to
play
the
role
of
the
product
owner
and
to
actively
collaborate
with
us
almost
on
a
day-to-day
basis
to
implement
approval
value.
A
The
proof
of
value
is
the
same
thing
of
a
proof
of
concept.
Basically,
a
fully
working
prototype.
We
just
applied
the
semantic
suites
to
highlight
that
what
we
do
actually
bring
values
into
the
customers,
businesses
so
far,
we
have
implemented
at
least
one
pov
in
all
the
technological
areas
shown
on
the
slides,
the
only
exception
being
the
iot.
Some
of
those
povs
were
quite
cool.
I
remember
one
of
the
first
one
I
work
at
when
I
joined
the
lab
was
provo
value
for
a
virtual
reality.
Application
using
oculus
skate
headset
for
the
same
customer.
A
We
immediately
implemented
another
pov
this
time
using
augmented
reality
on
an
android
tablet,
just
to
show
them
the
different
experiences.
Both
the
povs
were
very
well
accepted
received
by
the
customers,
but
we
understood
that
to
push
this
forward,
to
move
this
into
production
and
to
provide
the
client
with
the
wow
factor
they
were
looking
for.
We
simply
didn't
have
the
right
capabilities
within
the
company.
That's
because
these
technologies
are
quite
neat
and
to
they
require
very
advanced
graphical
skills,
especially
3d
graphical
skills,
which
are
almost
those
required
in
the
gaming
industry.
A
The
innovation
engagement
process
with
dapham
was
exactly
the
same
standard
approach
that
any
version
one
customer
faces
when
engaging
with
the
labs.
The
process
is
the
following:
it
always
start
from
ideation,
so
we
are
constantly
talking
with
our
customers
to
understand
if
they
are
facing
business
problems
which
are
not
solvable
by
standard
day-to-day
technology.
When
we
identify
one
of
those
problems
we
started
searching.
So
we
look
for
academical
or
industrial
resources.
A
We
run
brainstorming
and
design
thinking
sessions
until
we
found
the
technology
that
could
help
solving
the
problem
at
hand
and
when
we
identify
such
such
a
technology,
we
start
experimenting
with
it.
When
we're
happy
enough,
when
we
think
we
have
found
a
potential
solution,
we
formalize
it
into
an
innovation
canvas
the
canvas
acts
like
a
contract
between
us
and
the
in
the
in
the
customer,
and
the
document
contains
information
such
as
the
problem.
A
A
A
We
consider
we
have
proven
the
value
of
the
technology
we
got
in
touch
with
the
rest
of
the
verse,
1
delivery
teams
to
the
final
roadmap
for
moving
the
pov
live.
So
this
is
exactly
the
same
process
as
that
and
follow
when
engaging
with
us
on
this
particular
use
case
and
the
outcome.
The
outcome
of
the
whole
process
is
smart
text,
so
using
best
of
breed
open
source
technology,
smart
text
provide
text,
analytic
capabilities
to
extract
meaningful
insights
from
unstructured
data,
so
documents
images,
pdfs,
etc.
A
These
insights
are
the
features
that
are
later
used
for
artificial
intelligence
modeling
to
ultimately
classify
if
the
document
contained
or
not
personally
sensitive
information.
Obviously
this
is
just
one
of
the
many
possible
applications.
Martex
could
be
used
by
many
other
scenarios
and
we
will
shortly
see
some
examples,
but
for
now
let
me
just
dive
a
little
bit
more
into
the
components
of
the
solution.
The
first
one
is
the
ocr
cr
stands
for
optical
character,
recognition,
and
this
component
extract
the
textual
content
from
the
instructor
documents.
A
Each
of
these
components
is
exposed
as
a
separate
api,
ensuring
those
coupling
and
easily
combination
the
apis
use,
cartinate,
open
source
libraries
with
a
with
appropriate
customization
for
these
and
other
use
cases,
as
example
of
customization.
We
are
currently
retraining
the
open
source
machine
learning
model,
with
specific
set
of
documents
for
making
the
models
domain
specific.
A
A
As
a
consequence,
we
wanted
that
smart
text
utilize
the
power
of
the
platform
to
demonstrate
its
value
and
that
came
out
to
be
a
great
source
as
the
openshift
platform
helped
us
solving
some
of
the
issues
that
we
could
have
faced.
Otherwise,
for
instance,
the
smart
text
solution
was
designed
to
take
advantage
of
the
python
machine
learning
libraries,
but
this
architecture
was
not
supported
in
the
data
from
infrastructure.
A
The
openshift
platform
allowed
for
secure
deployment
and
build
of
reddit
published
containers.
What
would
have
been
would
have
been
impossible.
Otherwise,
given
the
available
budget
and
time
and
likewise
beating
our
tests
in
production,
environments
for
the
project
would
have
normally
been
another
large
costs,
but
this
was
easily
overcome
with
openshift
animates
streams.
A
The
solution
is
currently
live
actively.
Mitigating
gdpr
risk
for
farmers
and
agents
flagging
potential
errors
during
the
documents
upload.
This
has
enabled
the
department
to
switch
from
a
reactive
to
a
proactive
approach
of
identifying
a
data
breaches
and
isolating
them
and
preventing
them
from
occurring.
A
The
project
demonstrated
that
the
department
led
the
way
in
using
cantonese
open
source
technology
such
as
openshift
and
natural
language
processing
libraries.
For
for
what
concern
in
the
labs,
we
were
able
to
demonstrate
our
credibility
in
the
areas
of
text,
analytics,
machine
learning
and
artificial
intelligence.
A
The
smart
tech
solution
is
now
a
key
piece
of
our
smart
action.
Suite
that
we
are
developing.
We
will
shortly
talk
about
the
smart
action
suite
here.
I
just
would
like
to
say
that,
since
we
have
implemented
the
solution,
we
are
having
many
conversations
with
our
customers
and
smart
tech
storage
generated
really
interested.
A
We
immediately
understood
that
creating
an
ability
to
extract
available
insights
and
metadata
from
a
structured
document
being
them
forms
handwritten
letters.
Images
of
document
whatever
would
be
hugely
available
behind
the
initial
use
case,
for
instance
for
one
of
our
customers
in
the
uk,
we
have
been
recently
implementing
a
document
summarization
tool
and
the
the
goal
of
the
tool
is
to
provide
key
pieces
of
information
from
the
end
to
the
end
users
from
a
set
of
documents
without
the
user
having
to
read
any
of
those
documents
at
the
core
of
this
solution.
A
There
is
more
text
we
have
also
recently
demonstrated
it
to
many
other
clients,
both
in
ireland
and
in
the
in
the
uk.
All
in
all,
we
think
that
this
project
is
an
excellent
demonstration
of
how
open
source
technology
could
be
utilized
and
augmented
to
develop
solution
which
which
are
comparable
to
the
major
cloud
vendors.
A
Indeed,
we
commissioned
our
report
to
compare
smart
tech
solution
with
similar
technologies
from
azure
and
aws,
and
this
report
showed
that
the
performance
from
those
smart
texts
are
very
much
comparable
to
those
of
microsoft,
computer
vision
and
cognitive
services
on
one
side
and
aws
extract
and
comprehend
on
the
other
within
daphne.
The
smart
tech
solution
was
the
first
application
deployed
on
the
openshift
container
platform
and
I
started
ironed
out
all
the
user
technical
challenges
with
deploying
onto
a
new
platform.
A
I
was
not
directly
involved
in
the
original
development,
so
I
won't
spend
too
much
time
here
on
the
technical
challenges
and
the
subsequential
learnings.
However,
talking
with
one
of
the
main
developers,
I
found
particularly
interesting
that
one
of
the
weakest
point
of
the
original
implementation
was
the
central
role
of
the
orchestrator
components
in
in
the
regional
architecture
because
of
the
orchestrator.
A
That
architecture
was
highly
coupled
working
through
a
set
of
well-defined
steps
to
be
executed,
together
being
so,
the
orchestrator
needed
to
know
everything
about
anything
else,
making
it
the
single
point
of
failure,
that
is
their
case,
goes
down
everything
everything
it
goes
down
to.
So
we
look,
we
look
at
more
modern
architectural
approaches.
At
the
end,
we
went
for
a
reactive
base
architecture
which
make
the
single
components
responsive
to
relevant
changes
in
the
data.
A
I
previously
mentioned
the
smart
action
suite
so
before
concluding
this
presentation.
Just
please
allow
me
to
a
quick,
introduce
it
to
you
and
before
we
look
at
the
standard
innovation
journey,
our
customers
are
facing
when
engaging
with
the
innovation
labs.
The
journey
goes
from
ideation
to
the
successful
implementation
of
the
of
the
apov.
A
However,
over
time
we
noticed
that
many
of
our
customers
were
facing
similar
problems.
So,
instead
of
reinventing
the
wheel
all
the
time,
we
have
decided
to
start
productizing
over
existing
povs
and
build
what
we
call
the
smart
action
suite.
This
is
a
suite
of
components
which
could
be
used
either
either
in
isolation
or
like
lego.
Bricks
could
be
combined
together
in
different
numbers
in
order
to
build
many
solutions
which
could
apply
to
different
use
cases
and
scenarios.
A
Some
of
the
components
like
smart
text
and
smart
data
capture
have
already
been
developed
developed
the
other
will
be
implemented
in
the
next
future.
The
overall
idea
here
is
to
provide
our
clients
with
a
hyper
automation
set
of
apps
which
empower
their
employees,
allowing
them
to
take
a
better
and
more
efficient
decision
in
a
shorter
time.
In
a
nutshell,
the
key
components
are
shown
on
the
slide.
We
already
talked
about
smart
text.
I
will
just
introduce
another
couple
of
them.
A
The
right
reference
from
the
documents
will
be
returned:
smart
automation,
the
best
of
breed
automation,
tools
to
develop
hyper
automation,
so
with
a
combination
of
rpa
and
ai
and
finally,
smart
process
advisor,
which
is
designed
and
designed
to
guide
staff
through
organizational
processes
advising
them
each
step
of
the
way,
and
that
was
all
I
wanted
to
share
with
you
today.
I
hope
you
find
it
interesting.
Thank
you
very
much
for
your
attention.
If
you
have
any
question
you
can
enter
it
in
the
chat
below.