DENVER
―
The
growing
use
of
artificial
intelligence
to
enhance
monitoring
of
adversary
activities
poses
huge
interoperability
challenges
for
NATO
that
require
near-term
agreements
on
policies
and
data
standards,
NATO’s
top
intelligence
policy
officer
warned
on
Monday.
Among
the
biggest
concerns
for
Maj.
Gen.
Paul
Lynch,
a
British
Royal
Marine
serving
as
NATO
deputy
assistant
secretary
general
for
intelligence,
is
the
potential
for
allied
commanders
to
be
faced
with
conflicting
national
intelligence
reports.
“We
have
decades
of
experience
or
common
standards
for
air
defense,
maritime
awareness,
data
formats.
The
question
is
whether
we
apply
that
same
rigor
to
AI
before
the
technology
outpaces
the
frameworks,
or
after,”
Lynch
said
at
the
US
Geospatial
Intelligence
Foundation’s
annual
GEOINT
Symposium
here.
“And
the
answer
will
be
decided
in
the
next
three
years.”
Lynch
said
that
it
has
become
clear
that
“AI-enabled
exploitation
for
imagery
analysis,
change
detection
and
multisource
fusion
is
genuinely
changing
what
is
possible,
reducing
the
time
from
collection
to
actionable
product
and
enabling
analysts
to
focus
on
tasks
that
require
human
judgment,
rather
than
pattern
recognition
at
scale.”
However,
it
raises
a
whole
host
of
“governance”
challenges
given
that
each
of
NATO’s
32
members
are
responsible
for
both
developing
their
own
policies,
rules
and
regulations
about
AI
usage,
as
well
as
the
sharing
of
how
that
is
done
and
the
products
that
are
created.
Lynch
outlined
a
hypothetical
scenario
where
two
different
NATO
members
each
have
a
“national
AI
model”
trained
on
a
national
“imagery
data
set
with
that
country’s
labeling
conventions
and
analytical
priorities.”
Each
country
the
provides
an
intelligence
report
to
a
NATO
commander,
and
they
contradict
each
other.
“Which
one
does
the
commander
used
on
what
basis,
with
what
confidence?
And
I
think
that’s
the
AI
interoperability
challenge
for
allied
GEOINT
[geospatial
intelligence],
and
no
single
nation
is
able
to
solve
that
alone.
It
requires
agreed
standards
for
how
models
are
trained
and
documented,
how
AI
enabled
products
are
attributed,
and
what
confidence
thresholds
are
operationally
usable
in
what
context?”
he
said.
NATO
already
is
struggling
with
how
to
incorporate
the
vast
amounts
of
GEOINT
data
now
available
from
commercial
satellite
constellations
into
military
and
intelligence
community
systems
in
a
way
that
promotes
member
state
interoperability,
he
said.
GEOINT
is
primarily
about
providing
location
and
change
detection
data
about
human
activities
and
natural
phenomena
such
as
wildfires,
using
satellite
imagery,
maps,
and
other
types
of
data.
“The
problem
is
that
our
frameworks
for
incorporating
commercial
intelligence
into
allied
decision
cycles
were
built
for
a
different
world.
What
the
operational
environment
demands
now
is
a
framework
in
which
commercial
GEOINT
data
collected,
processed
and
analyzed
by
industry
can
be
fused
with
national
imagery,
open-source
and
partner
provided
intelligence,
and
then
delivered
to
a
commander
at
the
speed
of
operational
need,
across
32
national
classification
systems
and
a
set
of
legal
and
contractual
frameworks
that
were
written
for
most
of
those
capabilities
existed,”
Lynch
explained,
“I’m
sure
that
all
sounds
perfectly
straightforward,
and
I’m
using
the
English
phrase
of
‘perfectly
straightforward,’”
he
added
sardonically.
“It
means
it’s
not.”
At
the
moment,
Lynch
said,
“commercial
data
enters
NATO
through
intelligence
systems,
mostly
through
exceptions
and
workarounds,
not
designed
pathways.”
NATO
last
June
signed
its
first
commercial
space
strategy,
he
said,
and
now
is
getting
down
to
the”unglamorous
work”
needed
to
develop
“data
use
policies,
security
classification
guides,
contract
frameworks,
[and]
releasability
rules.”
The
advent
of
AI
will
complicate
those
efforts,
Lynch
warned,
especially
because
while
some
member
states
such
as
the
US
are
already
integrating
AI
processing
to
produce
GEOINT,
others
are
only
just
contemplating
foundational
questions
about
its
use.
“This
governance
challenge
becomes
significantly
more
complex
when
the
data
being
shared
is
being
processed
by
AI
systems,
because
then
we’re
no
longer
simply
asking
who
can
share
what,”
he
said.
“We’re
asking
whose
model
produced
it,
on
what
training
data,
with
what
documented
assumptions,
with
what
confidence
threshold
is
operationally
usable
in
what
context?”
“The
path
to
AI-enabled,
allied
intelligence
advantage
runs
primarily
through
governance,
not
necessarily
through
additional
capability,”
Lynch
said.
“NATO
needs
data
standards
that
are
designed
for
the
world
we’re
in
now
where
commercial
data,
national
data,
partner
data,
increasingly
processed
by
AI,
all
contribute
to
the
same
operational
picture,
common
meta-data
schemes,
common
AI
model
documentation,
[and]
common
interfaces
that
don’t
require
bespoke
integration
every
time
a
new
partner
or
new
source
joins
the
enterprise.”