Federal agencies aren’t struggling to adopt AI because of a lack of models or compute power. In fact, the biggest barrier isn’t GPUs—it’s the data. In this episode of The Bottom Line Up Front, we break down what’s really changing in AI infrastructure and why legacy architectures are failing to keep up with mission demands. The shift isn’t subtle—it’s a complete rethinking of how data is stored, accessed, secured, and operationalized.
Just a few years ago, most federal conversations centered on storage consolidation and capacity planning. That’s no longer the case. Today, agencies are asking:
- How fast can we ingest and process data?
- How current is the data powering AI decisions?
- Can our systems support real-time inference at scale?
- Continuous ingestion of new data
- Instant updates to knowledge bases
- Thousands of concurrent AI agents
- Near-zero latency data access
Why Traditional Architectures Fall Short
- Data movement bottlenecks
- Latency across systems
- Fragmented pipelines
- Security boundaries that don’t scale
A New Model: Disaggregated, Shared-Everything Architecture
- Compute (C nodes): Handle protocols like NFS, S3, SMB, Kafka, NVMe
- Data (D nodes): Store data independently with no controller bottlenecks
- High-speed fabric: Enables low-latency communication across the system
- Independent scaling of performance and capacity
- Reduced architectural bottlenecks
- Simplified lifecycle management
- Improved efficiency across AI workloads
From Storage Platform to AI Operating System
- File, object, and structured data storage
- SQL and vector databases
- Streaming pipelines (Kafka-native)
- Event-driven functions and triggers
- AI inference and embedding engines
- Eliminates brittle, multi-vendor pipelines
- Reduces accreditation complexity
- Enables real-time automation and decision-making
Real-Time AI Requires Event-Driven Pipelines
- Data is ingested (e.g., into an S3 bucket)
- A trigger detects the change
- A function executes (e.g., generating embeddings or running inference)
- The pipeline updates instantly
Security Can’t Be an Afterthought
- Zero Trust principles
- RBAC (Role-Based Access Control)
- ABAC (Attribute-Based Access Control)
- Active Directory integration
- Metadata-driven policies
Real-World Use Cases in Federal Environments
- Cybersecurity and Threat Analysis: Real-time packet capture and NetFlow analysis, Instant pivot from metadata to full packet inspection, Faster zero-day detection and Reduced dependency on costly external tools
- AI Assistants for Analysts and Citizens: Continuously updated knowledge bases, Context-aware chatbot responses, and Improved decision support for mission teams
- Multi-Tenant Agency Environments: Shared infrastructure with logical isolation, Cost optimization through resource pooling, and Fine-grained access control via ABAC.
The BLUF
If there’s one takeaway from this episode, it’s this: AI success in government is a data problem first—not a model problem. To move forward, agencies need to:
- Adopt architectures that eliminate performance bottlenecks
- Consolidate fragmented systems into unified platforms
- Build real-time, event-driven data pipelines
- Embed zero trust and ABAC directly into the data layer
And perhaps most importantly— Technology Alone Isn’t Enough. Even with the right platform, success depends on execution. Federal environments have unique constraints:
- Complex networks
- Strict compliance requirements
- Mission-critical reliability
That’s where experienced integrators like ATPGov play a critical role—translating emerging architectures into operational, mission-ready solutions.
- AI platform modernization
- Real-time data pipelines
- Zero trust alignment
and want to understand what actually works in federal environments— Email info@atpgov.com or Connect on LinkedIn
Synopsis
This episode focuses on AI systems that work in federal environments by summarizing a technical session on how AI shifts government needs from storage capacity to real-time, secure, actionable data for RAG, streaming inference, and thousands of concurrent agents. It explains why legacy architectures fail on data movement, latency, and security, then outlines VAST Data’s DASE (Disaggregated And Shared Everything) architecture. We describe VAST’s expansion from storage to an “AI operating system” unifying file/object/structured data, SQL and vector databases, Kafka-native streams, triggers/functions, and agent/insight engines, emphasizing baked-in zero trust with RBAC/ABAC and a global namespace across clouds and on-prem. Use cases include cyber investigations, IRS-style chatbots, and multi-tenant agency platforms, and it closes by positioning integrators like ATPGov to operationalize these solutions.
- 00:00 Introduction
- 00:38 Why Federal AI Breaks
- 01:29 Real Time RAG Demands
- 02:19 Data Stack Bottlenecks
- 02:48 DASE Architecture Explained
- 03:23 Inside VAST Nodes
- 04:57 From Storage to AI OS
- 06:09 Proven Federal Use Cases
- 06:49 Triggers, Functions & Pipelines
- 07:35 Zero Trust ABAC Built In
- 08:05 Global Namespace Hybrid Data
- 09:02 Bottom Line Takeaways
- 09:39 Integrator Call to Action
- 10:10 Subscribe and Sign Off
This episode is brought to you by ATP Gov. Visit us online at www.atpgov.com or follow us on LinkedIn.
Transcript
[00:00:00] Welcome to The Bottom Line Up Front, the podcast that cuts through the noise to deliver distilled insights from today’s most important technical webinars, presentations, and demonstrations. Designed for federal and military IT leaders, each episode breaks down complex technologies into mission-ready takeaways so you get the key points fast.
Whether it’s cybersecurity, cloud architecture, or emerging defense technologies, we highlight what matters most and how trusted integrators like ATPGov can help implement and operationalize these solutions across your agency or command. No fluff, no filler, just the bottom line up front. Today’s episode is all about AI systems that actually work in federal environments, not just lab demos or commercial hype.
We’re breaking down a deep technical session with vast data looking at how modern AI workloads are reshaping the data stack from storage to databases to real-time AI pipelines, and what that means for DoD, IC, and civilian agencies that care about [00:01:00] security, scale, and automation. First, we have to talk about the problem that the government is actually facing.
If you had asked us in 2023, I would say majority of our opportunities were based around this concept of universal storage and being able to collapse tiers of data within a data center. Today, that has pivoted tremendously towards the AI side. Federal agencies are no longer struggling with, quote, “how much storage,” but how fast, how current, and how actionable their data really is.
And that’s because AI has shifted from batch model training to real-time inference and retrieval, also known as RAG. And the government use cases now demand near real-time ingestion, continuous updates on knowledge bases, and thousands of concurrent AI agents. But as we move into utilizing those kinds of, hesitate to say applications, but the AI implementations, you know, it really changes that it’s not batch oriented.
It’s gotta be real time. As things get updated, you know, these RAG pipelines and so forth that we’ll talk a little about, have to be very, very current, meaning [00:02:00] within seconds of, you know, new documents being added to a realm, being able to utilize those in these workflows. Very different requirements as we move into this inference era.
Maybe tens of thousands of agents will be accessing this stuff, and it really changes the whole dynamic of what’s required for computing, and that’s really what we are bringing to the forefront. Existing legacy architectures weren’t designed for streaming inference pipelines, multi-tenant AI workloads, or zero trust data access at scale.
And this is where a lot of agencies get stuck. They don’t fail on GPUs or models, they fail on data movement, latency, and security boundaries. It’s a very different conversation than what capacity needs do you have or what fees and feeds and those kind of things. So we’re really getting away from that quite a bit, especially talking about the AI workflows as everyone’s trying to figure out how to do that, um, and we can help to simplify a lot of those.
So I want to touch on the DAY’s architecture in case you’re, uh, not familiar with this. As I said, it’s, it’s a unique architecture. It’s very different. Some of the limitations that even a scale-out architecture built on HA [00:03:00] pairs has were kind of glaring and, and that’s where the VAST system filled all of those gaps, if you will.
This architecture is very different in the, in the obvious part of being shared everything, and what that really means is that if you think of a controller-based implementation, controllers manage disks, right? Or, or Flash in, in our case. Um, and so we don’t actually have that same relationship. So what does disaggregated and share everything actually mean?
VAST DAY’s architecture fundamentally changes how performance and scale are delivered. In plain technical terms, that means C nodes handle protocols and compute, those being NFS, S3, SMB, Kafka, NVMe, and TCP, while D nodes store state and data with no controller ownership and no HA pair bottlenecks. This also includes NVMe over Fabrics connecting everything at low latency.
And storage class memory absorbs write pressures, and QLC flash is used efficiently and gently for long-term data. Having an understanding of storage and integration experience really matters in this conversation [00:04:00] because mapping mission workloads to performance profiles while avoiding over-provisioning is an extremely important part of this implementation.
You do have to consider independent scale of performance versus capacity, simpler life scale management, and fewer architectural choke points from long-term mission systems. But, uh, we’re all kind of in it together and, and kind of creating this whole vision for kind of simplifying AI. And just from a little history, the company was started in 2016.
Um, the whole idea was this revolutionary new architecture for storage and for storing data. Um, it was really all about the data, which is why we’re Vast Data and not Vast Storage. Um, storage is a thing, but managing the data and making data useful is what we’re all about. The idea behind Vast was to create this new architecture.
We call it DASE, the Disaggregated And Shared Everything. And that was, you know, kind of counterintuitive to every other storage company, um, because they were all about shared nothing implementations and still are today. While talking to Vast, we also saw a [00:05:00] shift from the, quote, “storage platform to the AI operating system.”
Because Vast has said that they’re not trying to win on storage anymore. It’s all about collapsing multiple AI infrastructure layers into a single operational platform. And their latest platform expansion includes unified data stores at the file object and structured data levels, SQL and vector databasing to allow for massive scale, Kafka native data streams, and data engines that leverage functions, triggers, and event-driven automation, while agent engines and insight engines use NVIDIA NIMs, GPU-assisted embeddings, and inference workflows.
So what that means for your environment is it eliminates brittle multi-vendor pipelines, reduces security accreditation scope, and enables real-time AI reactions to data retrieval And most importantly, between all of these Sync Engine, Insight Engine, Agent Engine, having the same security, so having them in this, you know, single data platform allows us to manage who can use what agents, which users can go out and do inferencing or, uh, embeddings and things like that, and [00:06:00] make sure that those, uh, Insight Engines and Agent Engine components can get access only to data that the user is allowed to have access to.
And so that’s one of the clear differentiators. But I also want to highlight some non-hypothetical use cases that we discussed with VAST and the expansion of its new platform. Those include cyber network security. The new VAST platform includes AFRL packet capture and NetFlows, instant pivot from metadata to full packet, reduced Splunk licensing footprints, and faster zero-day investigations Secondly, we talked about citizen and analyst AI assistants.
We discuss a recent IRS chatbot example and continuous documentation updates. Thirdly, we discuss multi-tenant agencies and how the VAST platform is a shared platform with logical isolation, cost amortization, and role-based and attribute-based boundaries. In this example, I’m gonna use some S3 buckets, and I have set up some triggers so that something gets dropped into the S3 bucket.
I can see that as a trigger, and this is where we use our Kafka engine [00:07:00] internally to impact that and, and, and basically execute a function based on that. Um, with the Insight engine, function could be to call out to a, a, a GPU and do, you know, vector embeddings or other things. So the idea is that within the VAST Data platform itself, we can now create triggers and functions, minimal coding required.
Uh, most of this stuff is written in Python, but could be written pretty much in anything. All we need to do is take that code and put a Docker wrapper around it, and now we have a Docker container that we can execute as a trigger or, or, or function. Once you create those elements, then you can actually go in and create pipelines.
And that leads us into a conversation about zero trust, ABAC, and security realities. This particular part of the discussion emphasizes where AI platforms fail in government today when security is bolted on later. And this is why VAST’s new architecture bakes it in from the beginning, because it includes native RBAC and ABAC, active directory integrations, metadata-driven access controls, zero trust principles embedded at the data layer with the [00:08:00] same policies distributed across AI agents, databases, object storage, and pipelines.
As far as the data store and the database, that’s kind of where stuff’s stored. I didn’t talk too much about the data space, but that gives us the ability to have this global namespace. So I have maybe something in Amazon, something in Azure, something on-prem. Um, we can share data across those different realms, have that as a single global namespace and, and again, manage the security across that, move data only when necessary, give access to data across wide area networks, so.
We actually handle, uh, about 99% of the zero trust without any external softwares. It’s all built into the VAST system, so there’s a lot of security components built in. But ABAC is, uh, is a core component of, of our security infrastructure and how we manage that and allow capturing of, of lots of different metadata and giving it specific attributes based on, uh, on need there, so.
In our view, this is where VAST really adds value in validating these controls against RMF, DoD zero trust scores, and agency-specific security overlays. So in the end, the AI [00:09:00] accelerates approvals instead of blocking them. So what’s the bottom line up front of VAST’s new AI operating system? It’s all about data architecture, security, and operational simplicity.
Because federal AI isn’t about flashy models, and platforms like VAST show what’s possible when storage, databases, streaming, and AI pipelines are treated as one system instead of six It means that AI success in government is a data problem first. Daze architectures remove scale and performance bottlenecks, unified platforms reduce complexity and accreditation friction, and real-time AI requires event-driven data pipelines, while ABAC and zero trust must exist at the data layer.
And in the end, success in this arena still depends on integrators, integrators who understand federal networks, zero trust, and mission constraints well enough to put this tech to work. So if you’re exploring AI platforms, data modernization, or zero trust alignment and want to understand what actually works in your environment, reach out so we can help translate fast new architecture into real mission outcomes.[00:10:00]
Be sure to reach out to ATPGov today at www.atpgov.com or email info@atpgov.com or check us out on social media on LinkedIn. Thanks for listening, and be sure to subscribe to the Bottom Line Up Front wherever you get your podcasts, and stay tuned for more distilled insights from the front lines of tech and national security.
So until next time, stay secure, stay mission ready.
About this Podcast
The Bottom Line Up Front, is ATP Gov’s podcast that cuts through the noise to deliver distilled insights from today’s most important technical webinars, presentations and demonstrations designed for federal and military IT leaders. Each episode breaks down complex technologies into mission ready takeaways, so you get the key points.
Fast.
Whether it’s cybersecurity, cloud, architecture, or emerging defense technologies, we highlight what matters most and how trusted integrators like ATP Gov can help implement and operationalize these solutions across your agency or command.
No fluff. No filler, just the bottom line up front.
