1
00:00:01,049 --> 00:00:05,440
Welcome to The Bottom Line Up Front, the
podcast that cuts through the noise to
2
00:00:05,440 --> 00:00:09,220
deliver distilled insights from today's
most important technical webinars,
3
00:00:09,220 --> 00:00:11,050
presentations, and demonstrations.
4
00:00:11,460 --> 00:00:15,020
Designed for federal and military
IT leaders, each episode breaks down
5
00:00:15,050 --> 00:00:19,629
complex technologies into mission-ready
takeaways so you get the key points fast.
6
00:00:19,829 --> 00:00:22,940
Whether it's cybersecurity, cloud
architecture, or emerging defense
7
00:00:22,940 --> 00:00:26,709
technologies, we highlight what matters
most and how trusted integrators
8
00:00:26,709 --> 00:00:30,659
like ATPGov can help implement
and operationalize these solutions
9
00:00:30,659 --> 00:00:32,400
across your agency or command.
10
00:00:32,720 --> 00:00:36,080
No fluff, no filler, just
the bottom line up front.
11
00:00:38,194 --> 00:00:41,805
Today's episode is all about AI
systems that actually work in
12
00:00:41,805 --> 00:00:45,284
federal environments, not just
lab demos or commercial hype.
13
00:00:45,475 --> 00:00:48,985
We're breaking down a deep technical
session with vast data looking at
14
00:00:48,985 --> 00:00:52,905
how modern AI workloads are reshaping
the data stack from storage to
15
00:00:52,905 --> 00:00:57,835
databases to real-time AI pipelines,
and what that means for DoD, IC, and
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00:00:57,835 --> 00:01:01,354
civilian agencies that care about
security, scale, and automation.
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00:01:01,595 --> 00:01:05,044
First, we have to talk about the problem
that the government is actually facing.
18
00:01:05,255 --> 00:01:10,805
If you had asked us in 2023, I would
say majority of our opportunities were
19
00:01:10,805 --> 00:01:14,994
based around this concept of universal
storage and being able to collapse
20
00:01:14,994 --> 00:01:16,674
tiers of data within a data center.
21
00:01:16,924 --> 00:01:21,005
Today, that has pivoted
tremendously towards the AI side.
22
00:01:21,264 --> 00:01:24,354
Federal agencies are no longer
struggling with, quote, "how much
23
00:01:24,354 --> 00:01:28,764
storage," but how fast, how current,
and how actionable their data really is.
24
00:01:29,119 --> 00:01:31,990
And that's because AI has
shifted from batch model training
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00:01:31,990 --> 00:01:35,149
to real-time inference and
retrieval, also known as RAG.
26
00:01:35,340 --> 00:01:39,280
And the government use cases now demand
near real-time ingestion, continuous
27
00:01:39,280 --> 00:01:43,099
updates on knowledge bases, and
thousands of concurrent AI agents.
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00:01:43,309 --> 00:01:47,929
But as we move into utilizing those kinds
of, hesitate to say applications, but the
29
00:01:47,929 --> 00:01:52,280
AI implementations, you know, it really
changes that it's not batch oriented.
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00:01:52,280 --> 00:01:53,360
It's gotta be real time.
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As things get updated, you know,
these RAG pipelines and so forth that
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we'll talk a little about, have to
be very, very current, meaning within
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seconds of, you know, new documents
being added to a realm, being able
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to utilize those in these workflows.
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00:02:05,769 --> 00:02:09,440
Very different requirements as
we move into this inference era.
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Maybe tens of thousands of agents will
be accessing this stuff, and it really
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changes the whole dynamic of what's
required for computing, and that's really
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what we are bringing to the forefront.
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Existing legacy architectures weren't
designed for streaming inference
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00:02:22,749 --> 00:02:26,690
pipelines, multi-tenant AI workloads,
or zero trust data access at scale.
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00:02:26,839 --> 00:02:28,959
And this is where a lot
of agencies get stuck.
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They don't fail on GPUs or models,
they fail on data movement,
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latency, and security boundaries.
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It's a very different conversation than
what capacity needs do you have or what
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fees and feeds and those kind of things.
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So we're really getting away from that
quite a bit, especially talking about
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the AI workflows as everyone's trying
to figure out how to do that, um, and
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we can help to simplify a lot of those.
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So I want to touch on the DAY's
architecture in case you're,
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uh, not familiar with this.
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As I said, it's, it's
a unique architecture.
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It's very different.
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Some of the limitations that even a
scale-out architecture built on HA
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pairs has were kind of glaring and,
and that's where the VAST system
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filled all of those gaps, if you will.
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This architecture is very different
in the, in the obvious part of being
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shared everything, and what that
really means is that if you think of
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a controller-based implementation,
controllers manage disks, right?
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Or, or Flash in, in our case.
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Um, and so we don't actually
have that same relationship.
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So what does disaggregated and
share everything actually mean?
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VAST DAY's architecture
fundamentally changes how
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performance and scale are delivered.
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In plain technical terms, that means
C nodes handle protocols and compute,
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those being NFS, S3, SMB, Kafka,
NVMe, and TCP, while D nodes store
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state and data with no controller
ownership and no HA pair bottlenecks.
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This also includes NVMe over Fabrics
connecting everything at low latency.
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And storage class memory absorbs
write pressures, and QLC flash is used
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efficiently and gently for long-term data.
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Having an understanding of storage and
integration experience really matters in
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this conversation because mapping mission
workloads to performance profiles while
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avoiding over-provisioning is an extremely
important part of this implementation.
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You do have to consider independent
scale of performance versus capacity,
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simpler life scale management, and
fewer architectural choke points
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from long-term mission systems.
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But, uh, we're all kind of in it together
and, and kind of creating this whole
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vision for kind of simplifying AI.
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And just from a little history,
the company was started in 2016.
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Um, the whole idea was this
revolutionary new architecture
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for storage and for storing data.
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Um, it was really all about
the data, which is why we're
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Vast Data and not Vast Storage.
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Um, storage is a thing, but
managing the data and making data
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useful is what we're all about.
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The idea behind Vast was to
create this new architecture.
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We call it DASE, the Disaggregated
And Shared Everything.
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And that was, you know, kind of
counterintuitive to every other
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storage company, um, because they
were all about shared nothing
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implementations and still are today.
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While talking to Vast, we also saw
a shift from the, quote, "storage
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platform to the AI operating system."
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Because Vast has said that they're
not trying to win on storage anymore.
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It's all about collapsing multiple
AI infrastructure layers into
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a single operational platform.
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And their latest platform expansion
includes unified data stores at the file
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object and structured data levels, SQL
and vector databasing to allow for massive
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scale, Kafka native data streams, and
data engines that leverage functions,
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triggers, and event-driven automation,
while agent engines and insight
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engines use NVIDIA NIMs, GPU-assisted
embeddings, and inference workflows.
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So what that means for your environment
is it eliminates brittle multi-vendor
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pipelines, reduces security accreditation
scope, and enables real-time AI reactions
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to data retrieval And most importantly,
between all of these Sync Engine, Insight
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Engine, Agent Engine, having the same
security, so having them in this, you
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know, single data platform allows us to
manage who can use what agents, which
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users can go out and do inferencing or,
uh, embeddings and things like that,
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and make sure that those, uh, Insight
Engines and Agent Engine components
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can get access only to data that the
user is allowed to have access to.
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And so that's one of the
clear differentiators.
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But I also want to highlight some
non-hypothetical use cases that
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we discussed with VAST and the
expansion of its new platform.
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Those include cyber network security.
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The new VAST platform includes AFRL packet
capture and NetFlows, instant pivot from
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metadata to full packet, reduced Splunk
licensing footprints, and faster zero-day
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investigations Secondly, we talked
about citizen and analyst AI assistants.
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We discuss a recent IRS chatbot example
and continuous documentation updates.
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Thirdly, we discuss multi-tenant
agencies and how the VAST platform is a
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shared platform with logical isolation,
cost amortization, and role-based
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and attribute-based boundaries.
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In this example, I'm gonna use
some S3 buckets, and I have set
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up some triggers so that something
gets dropped into the S3 bucket.
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I can see that as a trigger, and this is
where we use our Kafka engine internally
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to impact that and, and, and basically
execute a function based on that.
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Um, with the Insight engine,
function could be to call out
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to a, a, a GPU and do, you know,
vector embeddings or other things.
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So the idea is that within the
VAST Data platform itself, we
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can now create triggers and
functions, minimal coding required.
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Uh, most of this stuff is
written in Python, but could be
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written pretty much in anything.
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All we need to do is take that code and
put a Docker wrapper around it, and now
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we have a Docker container that we can
execute as a trigger or, or, or function.
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Once you create those elements, then you
can actually go in and create pipelines.
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And that leads us into a
conversation about zero trust,
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ABAC, and security realities.
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This particular part of the
discussion emphasizes where AI
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platforms fail in government today
when security is bolted on later.
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And this is why VAST's new architecture
bakes it in from the beginning, because
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it includes native RBAC and ABAC, active
directory integrations, metadata-driven
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access controls, zero trust principles
embedded at the data layer with the same
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policies distributed across AI agents,
databases, object storage, and pipelines.
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As far as the data store and the database,
that's kind of where stuff's stored.
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I didn't talk too much about the
data space, but that gives us the
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ability to have this global namespace.
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So I have maybe something in Amazon,
something in Azure, something on-prem.
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Um, we can share data across those
different realms, have that as a single
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global namespace and, and again, manage
the security across that, move data
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only when necessary, give access to
data across wide area networks, so.
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We actually handle, uh, about 99% of the
zero trust without any external softwares.
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It's all built into the VAST
system, so there's a lot of
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security components built in.
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But ABAC is, uh, is a core component
of, of our security infrastructure and
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how we manage that and allow capturing
of, of lots of different metadata
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and giving it specific attributes
based on, uh, on need there, so.
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In our view, this is where VAST really
adds value in validating these controls
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against RMF, DoD zero trust scores,
and agency-specific security overlays.
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So in the end, the AI accelerates
approvals instead of blocking them.
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So what's the bottom line up front
of VAST's new AI operating system?
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It's all about data architecture,
security, and operational simplicity.
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Because federal AI isn't about flashy
models, and platforms like VAST
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show what's possible when storage,
databases, streaming, and AI pipelines
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are treated as one system instead
of six It means that AI success in
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government is a data problem first.
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Daze architectures remove scale
and performance bottlenecks,
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unified platforms reduce complexity
and accreditation friction, and
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real-time AI requires event-driven
data pipelines, while ABAC and zero
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trust must exist at the data layer.
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And in the end, success in this arena
still depends on integrators, integrators
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who understand federal networks,
zero trust, and mission constraints
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well enough to put this tech to work.
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So if you're exploring AI platforms,
data modernization, or zero trust
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00:09:51,470 --> 00:09:55,400
alignment and want to understand what
actually works in your environment, reach
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00:09:55,400 --> 00:09:59,799
out so we can help translate fast new
architecture into real mission outcomes.
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00:10:00,330 --> 00:10:07,970
Be sure to reach out to ATPGov today at
www.atpgov.com or email info@atpgov.com or
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00:10:07,970 --> 00:10:10,290
check us out on social media on LinkedIn.
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00:10:10,530 --> 00:10:13,700
Thanks for listening, and be sure to
subscribe to the Bottom Line Up Front
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00:10:13,700 --> 00:10:17,409
wherever you get your podcasts, and stay
tuned for more distilled insights from the
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00:10:17,409 --> 00:10:19,810
front lines of tech and national security.
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00:10:19,950 --> 00:10:24,098
So until next time, stay
secure, stay mission ready.
00:00:01,049 --> 00:00:05,440
Welcome to The Bottom Line Up Front, the
podcast that cuts through the noise to
2
00:00:05,440 --> 00:00:09,220
deliver distilled insights from today's
most important technical webinars,
3
00:00:09,220 --> 00:00:11,050
presentations, and demonstrations.
4
00:00:11,460 --> 00:00:15,020
Designed for federal and military
IT leaders, each episode breaks down
5
00:00:15,050 --> 00:00:19,629
complex technologies into mission-ready
takeaways so you get the key points fast.
6
00:00:19,829 --> 00:00:22,940
Whether it's cybersecurity, cloud
architecture, or emerging defense
7
00:00:22,940 --> 00:00:26,709
technologies, we highlight what matters
most and how trusted integrators
8
00:00:26,709 --> 00:00:30,659
like ATPGov can help implement
and operationalize these solutions
9
00:00:30,659 --> 00:00:32,400
across your agency or command.
10
00:00:32,720 --> 00:00:36,080
No fluff, no filler, just
the bottom line up front.
11
00:00:38,194 --> 00:00:41,805
Today's episode is all about AI
systems that actually work in
12
00:00:41,805 --> 00:00:45,284
federal environments, not just
lab demos or commercial hype.
13
00:00:45,475 --> 00:00:48,985
We're breaking down a deep technical
session with vast data looking at
14
00:00:48,985 --> 00:00:52,905
how modern AI workloads are reshaping
the data stack from storage to
15
00:00:52,905 --> 00:00:57,835
databases to real-time AI pipelines,
and what that means for DoD, IC, and
16
00:00:57,835 --> 00:01:01,354
civilian agencies that care about
security, scale, and automation.
17
00:01:01,595 --> 00:01:05,044
First, we have to talk about the problem
that the government is actually facing.
18
00:01:05,255 --> 00:01:10,805
If you had asked us in 2023, I would
say majority of our opportunities were
19
00:01:10,805 --> 00:01:14,994
based around this concept of universal
storage and being able to collapse
20
00:01:14,994 --> 00:01:16,674
tiers of data within a data center.
21
00:01:16,924 --> 00:01:21,005
Today, that has pivoted
tremendously towards the AI side.
22
00:01:21,264 --> 00:01:24,354
Federal agencies are no longer
struggling with, quote, "how much
23
00:01:24,354 --> 00:01:28,764
storage," but how fast, how current,
and how actionable their data really is.
24
00:01:29,119 --> 00:01:31,990
And that's because AI has
shifted from batch model training
25
00:01:31,990 --> 00:01:35,149
to real-time inference and
retrieval, also known as RAG.
26
00:01:35,340 --> 00:01:39,280
And the government use cases now demand
near real-time ingestion, continuous
27
00:01:39,280 --> 00:01:43,099
updates on knowledge bases, and
thousands of concurrent AI agents.
28
00:01:43,309 --> 00:01:47,929
But as we move into utilizing those kinds
of, hesitate to say applications, but the
29
00:01:47,929 --> 00:01:52,280
AI implementations, you know, it really
changes that it's not batch oriented.
30
00:01:52,280 --> 00:01:53,360
It's gotta be real time.
31
00:01:53,360 --> 00:01:56,290
As things get updated, you know,
these RAG pipelines and so forth that
32
00:01:56,290 --> 00:02:00,280
we'll talk a little about, have to
be very, very current, meaning within
33
00:02:00,280 --> 00:02:03,670
seconds of, you know, new documents
being added to a realm, being able
34
00:02:03,670 --> 00:02:05,720
to utilize those in these workflows.
35
00:02:05,769 --> 00:02:09,440
Very different requirements as
we move into this inference era.
36
00:02:09,589 --> 00:02:13,330
Maybe tens of thousands of agents will
be accessing this stuff, and it really
37
00:02:13,330 --> 00:02:17,170
changes the whole dynamic of what's
required for computing, and that's really
38
00:02:17,170 --> 00:02:19,170
what we are bringing to the forefront.
39
00:02:19,400 --> 00:02:22,749
Existing legacy architectures weren't
designed for streaming inference
40
00:02:22,749 --> 00:02:26,690
pipelines, multi-tenant AI workloads,
or zero trust data access at scale.
41
00:02:26,839 --> 00:02:28,959
And this is where a lot
of agencies get stuck.
42
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They don't fail on GPUs or models,
they fail on data movement,
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latency, and security boundaries.
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It's a very different conversation than
what capacity needs do you have or what
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fees and feeds and those kind of things.
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So we're really getting away from that
quite a bit, especially talking about
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the AI workflows as everyone's trying
to figure out how to do that, um, and
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we can help to simplify a lot of those.
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So I want to touch on the DAY's
architecture in case you're,
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uh, not familiar with this.
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As I said, it's, it's
a unique architecture.
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It's very different.
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Some of the limitations that even a
scale-out architecture built on HA
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pairs has were kind of glaring and,
and that's where the VAST system
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filled all of those gaps, if you will.
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This architecture is very different
in the, in the obvious part of being
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shared everything, and what that
really means is that if you think of
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a controller-based implementation,
controllers manage disks, right?
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Or, or Flash in, in our case.
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Um, and so we don't actually
have that same relationship.
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So what does disaggregated and
share everything actually mean?
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VAST DAY's architecture
fundamentally changes how
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performance and scale are delivered.
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In plain technical terms, that means
C nodes handle protocols and compute,
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those being NFS, S3, SMB, Kafka,
NVMe, and TCP, while D nodes store
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state and data with no controller
ownership and no HA pair bottlenecks.
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This also includes NVMe over Fabrics
connecting everything at low latency.
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And storage class memory absorbs
write pressures, and QLC flash is used
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efficiently and gently for long-term data.
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Having an understanding of storage and
integration experience really matters in
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this conversation because mapping mission
workloads to performance profiles while
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avoiding over-provisioning is an extremely
important part of this implementation.
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You do have to consider independent
scale of performance versus capacity,
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simpler life scale management, and
fewer architectural choke points
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00:04:14,124 --> 00:04:15,865
from long-term mission systems.
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But, uh, we're all kind of in it together
and, and kind of creating this whole
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vision for kind of simplifying AI.
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And just from a little history,
the company was started in 2016.
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Um, the whole idea was this
revolutionary new architecture
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for storage and for storing data.
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Um, it was really all about
the data, which is why we're
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Vast Data and not Vast Storage.
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Um, storage is a thing, but
managing the data and making data
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00:04:40,475 --> 00:04:42,375
useful is what we're all about.
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00:04:42,534 --> 00:04:45,884
The idea behind Vast was to
create this new architecture.
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We call it DASE, the Disaggregated
And Shared Everything.
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And that was, you know, kind of
counterintuitive to every other
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storage company, um, because they
were all about shared nothing
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implementations and still are today.
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00:04:57,585 --> 00:05:01,475
While talking to Vast, we also saw
a shift from the, quote, "storage
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platform to the AI operating system."
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Because Vast has said that they're
not trying to win on storage anymore.
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00:05:07,295 --> 00:05:10,515
It's all about collapsing multiple
AI infrastructure layers into
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a single operational platform.
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00:05:12,395 --> 00:05:16,734
And their latest platform expansion
includes unified data stores at the file
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00:05:16,765 --> 00:05:21,335
object and structured data levels, SQL
and vector databasing to allow for massive
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scale, Kafka native data streams, and
data engines that leverage functions,
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00:05:25,975 --> 00:05:29,475
triggers, and event-driven automation,
while agent engines and insight
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00:05:29,475 --> 00:05:34,284
engines use NVIDIA NIMs, GPU-assisted
embeddings, and inference workflows.
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00:05:34,579 --> 00:05:38,070
So what that means for your environment
is it eliminates brittle multi-vendor
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00:05:38,070 --> 00:05:42,849
pipelines, reduces security accreditation
scope, and enables real-time AI reactions
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00:05:42,920 --> 00:05:46,960
to data retrieval And most importantly,
between all of these Sync Engine, Insight
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00:05:46,960 --> 00:05:50,509
Engine, Agent Engine, having the same
security, so having them in this, you
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00:05:50,509 --> 00:05:55,349
know, single data platform allows us to
manage who can use what agents, which
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00:05:55,349 --> 00:05:59,470
users can go out and do inferencing or,
uh, embeddings and things like that,
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00:05:59,720 --> 00:06:03,249
and make sure that those, uh, Insight
Engines and Agent Engine components
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00:06:03,410 --> 00:06:07,680
can get access only to data that the
user is allowed to have access to.
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00:06:07,680 --> 00:06:09,349
And so that's one of the
clear differentiators.
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But I also want to highlight some
non-hypothetical use cases that
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we discussed with VAST and the
expansion of its new platform.
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00:06:16,559 --> 00:06:18,620
Those include cyber network security.
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00:06:18,800 --> 00:06:24,360
The new VAST platform includes AFRL packet
capture and NetFlows, instant pivot from
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00:06:24,360 --> 00:06:28,789
metadata to full packet, reduced Splunk
licensing footprints, and faster zero-day
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00:06:28,789 --> 00:06:33,700
investigations Secondly, we talked
about citizen and analyst AI assistants.
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00:06:33,819 --> 00:06:38,550
We discuss a recent IRS chatbot example
and continuous documentation updates.
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00:06:38,899 --> 00:06:42,939
Thirdly, we discuss multi-tenant
agencies and how the VAST platform is a
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00:06:42,939 --> 00:06:47,470
shared platform with logical isolation,
cost amortization, and role-based
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00:06:47,470 --> 00:06:49,099
and attribute-based boundaries.
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00:06:49,249 --> 00:06:52,919
In this example, I'm gonna use
some S3 buckets, and I have set
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00:06:52,919 --> 00:06:56,089
up some triggers so that something
gets dropped into the S3 bucket.
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00:06:56,239 --> 00:07:00,409
I can see that as a trigger, and this is
where we use our Kafka engine internally
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00:07:00,409 --> 00:07:05,009
to impact that and, and, and basically
execute a function based on that.
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00:07:05,129 --> 00:07:08,489
Um, with the Insight engine,
function could be to call out
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00:07:08,489 --> 00:07:12,259
to a, a, a GPU and do, you know,
vector embeddings or other things.
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00:07:12,259 --> 00:07:15,590
So the idea is that within the
VAST Data platform itself, we
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00:07:15,590 --> 00:07:19,070
can now create triggers and
functions, minimal coding required.
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00:07:19,140 --> 00:07:21,480
Uh, most of this stuff is
written in Python, but could be
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00:07:21,539 --> 00:07:22,570
written pretty much in anything.
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00:07:22,809 --> 00:07:27,210
All we need to do is take that code and
put a Docker wrapper around it, and now
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00:07:27,210 --> 00:07:31,030
we have a Docker container that we can
execute as a trigger or, or, or function.
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00:07:31,079 --> 00:07:35,190
Once you create those elements, then you
can actually go in and create pipelines.
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00:07:35,440 --> 00:07:38,110
And that leads us into a
conversation about zero trust,
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00:07:38,110 --> 00:07:39,930
ABAC, and security realities.
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00:07:40,169 --> 00:07:43,510
This particular part of the
discussion emphasizes where AI
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00:07:43,510 --> 00:07:47,180
platforms fail in government today
when security is bolted on later.
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00:07:47,260 --> 00:07:50,949
And this is why VAST's new architecture
bakes it in from the beginning, because
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00:07:50,949 --> 00:07:55,729
it includes native RBAC and ABAC, active
directory integrations, metadata-driven
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00:07:55,729 --> 00:08:00,010
access controls, zero trust principles
embedded at the data layer with the same
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00:08:00,010 --> 00:08:04,769
policies distributed across AI agents,
databases, object storage, and pipelines.
140
00:08:05,019 --> 00:08:08,040
As far as the data store and the database,
that's kind of where stuff's stored.
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00:08:08,199 --> 00:08:10,470
I didn't talk too much about the
data space, but that gives us the
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00:08:10,470 --> 00:08:12,269
ability to have this global namespace.
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00:08:12,269 --> 00:08:15,640
So I have maybe something in Amazon,
something in Azure, something on-prem.
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00:08:15,729 --> 00:08:19,559
Um, we can share data across those
different realms, have that as a single
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00:08:19,559 --> 00:08:23,610
global namespace and, and again, manage
the security across that, move data
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00:08:23,610 --> 00:08:27,890
only when necessary, give access to
data across wide area networks, so.
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00:08:28,099 --> 00:08:33,599
We actually handle, uh, about 99% of the
zero trust without any external softwares.
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00:08:33,669 --> 00:08:36,049
It's all built into the VAST
system, so there's a lot of
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00:08:36,049 --> 00:08:37,320
security components built in.
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00:08:37,320 --> 00:08:41,459
But ABAC is, uh, is a core component
of, of our security infrastructure and
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00:08:41,459 --> 00:08:45,319
how we manage that and allow capturing
of, of lots of different metadata
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00:08:45,319 --> 00:08:49,259
and giving it specific attributes
based on, uh, on need there, so.
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00:08:49,679 --> 00:08:53,699
In our view, this is where VAST really
adds value in validating these controls
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00:08:53,699 --> 00:08:58,490
against RMF, DoD zero trust scores,
and agency-specific security overlays.
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00:08:58,600 --> 00:09:01,750
So in the end, the AI accelerates
approvals instead of blocking them.
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00:09:02,229 --> 00:09:06,039
So what's the bottom line up front
of VAST's new AI operating system?
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00:09:06,500 --> 00:09:10,949
It's all about data architecture,
security, and operational simplicity.
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00:09:11,259 --> 00:09:15,149
Because federal AI isn't about flashy
models, and platforms like VAST
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00:09:15,149 --> 00:09:19,490
show what's possible when storage,
databases, streaming, and AI pipelines
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00:09:19,539 --> 00:09:24,190
are treated as one system instead
of six It means that AI success in
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00:09:24,190 --> 00:09:26,239
government is a data problem first.
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00:09:26,549 --> 00:09:29,419
Daze architectures remove scale
and performance bottlenecks,
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00:09:29,569 --> 00:09:32,920
unified platforms reduce complexity
and accreditation friction, and
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00:09:32,920 --> 00:09:36,880
real-time AI requires event-driven
data pipelines, while ABAC and zero
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00:09:36,880 --> 00:09:38,909
trust must exist at the data layer.
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00:09:39,169 --> 00:09:42,999
And in the end, success in this arena
still depends on integrators, integrators
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00:09:42,999 --> 00:09:46,019
who understand federal networks,
zero trust, and mission constraints
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00:09:46,019 --> 00:09:47,620
well enough to put this tech to work.
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00:09:47,730 --> 00:09:51,470
So if you're exploring AI platforms,
data modernization, or zero trust
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00:09:51,470 --> 00:09:55,400
alignment and want to understand what
actually works in your environment, reach
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00:09:55,400 --> 00:09:59,799
out so we can help translate fast new
architecture into real mission outcomes.
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00:10:00,330 --> 00:10:07,970
Be sure to reach out to ATPGov today at
www.atpgov.com or email info@atpgov.com or
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00:10:07,970 --> 00:10:10,290
check us out on social media on LinkedIn.
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00:10:10,530 --> 00:10:13,700
Thanks for listening, and be sure to
subscribe to the Bottom Line Up Front
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00:10:13,700 --> 00:10:17,409
wherever you get your podcasts, and stay
tuned for more distilled insights from the
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00:10:17,409 --> 00:10:19,810
front lines of tech and national security.
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00:10:19,950 --> 00:10:24,098
So until next time, stay
secure, stay mission ready.
