GovCon Bid and Proposal Insights
GovCon Bid and Proposal Insights
Personnel Security Adjudicator Support and Analytic Platform - Department of Homeland Security-Immigration and Customs Enforcement
In this episode, we explore the upcoming $20M–$50M DHS–ICE MA-IDIQ: Personnel Security Adjudicator Support and Analytic Platform (ASAP) opportunity. Set aside for small businesses under NAICS 541511 (Custom Computer Programming Services), this multi-award contract aims to enhance personnel security adjudication through advanced analytics and secure digital platforms.
Tune in as we break down the scope, requirements, and strategies.
Contact ProposalHelper at sales@proposalhelper.com to find similar opportunities and help you build a realistic and winning pipeline.
If you've ever thought about how the government, you know, vets the people responsible for national security, you probably picture piles of paperwork, right? Interviews, analysts digging through data.
SPEAKER_01:Aaron Powell Exactly. It's meticulous, slow work, personnel security investigations, vetting employees, contractors, officers. These people are the gatekeepers for really sensitive information.
SPEAKER_00:Aaron Powell And that process, that slow, careful process, it creates bottlenecks, huge ones.
SPEAKER_01:Oh, absolutely. The volume of cases has been a massive challenge for years. Just keeping up is tough.
SPEAKER_00:Aaron Powell, which is exactly why we need to talk about this document we found. We were diving into the federal acquisition planning forecast system.
SPEAKER_01:Aaron Powell Right, the APFS.
SPEAKER_00:And we found what looks like, well, a public declaration of war on that exact bottleneck. We're talking about U.S. immigration and customs enforcement, ICE, specifically their Office of Professional Responsibility, OPR. Trevor Burrus, Jr.
SPEAKER_01:And this forecast, it's essentially putting out an urgent call for a very specific, high-powered solution. They're looking to buy something called the Personnel Security Adjudicator Support and Analytic Platform. They call it ASAP.
SPEAKER_00:ASAP.
SPEAKER_01:That's the range.$20 million to$50 million, a huge investment.
SPEAKER_00:$50 million to basically inject advanced AI, you know, large language models into one of the most sensitive jobs an agency does, deciding who gets a security clearance. So our mission today really is to unpack what this ASAP thing is, understand why ICE seems to need it so desperately fast, and maybe explore what happens when an LLM starts filtering national security risks.
SPEAKER_01:Aaron Powell And the timing is fascinating here, isn't it? This forecast hit the system in early October 2025.
SPEAKER_00:Right.
SPEAKER_01:And it doesn't mess around with timelines. It explicitly says the government has an urgent requirement. They need an existing pre-vetted AI or LLM solution.
SPEAKER_00:Aaron Powell So they're not looking to build it from scratch.
SPEAKER_01:Trevor Burrus No. Off the shelf. Buy it, plug it in, basically, get it running immediately.
SPEAKER_00:Aaron Powell An urgent requirement for a potentially$50 million AI platform. That doesn't sound like typical government procurement speed at all.
SPEAKER_01:Aaron Powell It's definitely accelerated.
SPEAKER_00:So why the big rush? Why the uh hyperacceleration here?
SPEAKER_01:Aaron Ross Powell Well it seems to connect directly to a bigger mandate, right? Yeah. The forecast actually links ASAP to the current administration's push for enhancing government efficiency and expanding the use of AI technologies.
SPEAKER_00:Aaron Powell Ah, okay. So this isn't just ICE needing to clear a backlog internally.
SPEAKER_01:Aaron Powell It seems bigger than that. It looks like this is a concrete example of a major federal AI policy actually, you know, hitting the ground in the security world.
SPEAKER_00:Aaron Powell And they're under pressure to show results fast.
SPEAKER_01:Aaron Powell That seems to be the implication. And wanting a pre-existing solution. That tells you they want to skip the usual years of development and testing.
SPEAKER_00:Aaron Powell Yeah, jump the queue. And they're using a contract vehicle they already have set up.
SPEAKER_01:Exactly. An existing multiple award IDIQ contract. That just streamlines the whole process, makes awarding the contract much, much faster. They need to start running like yesterday. They really do. I mean, the current manual way of doing things, it's just too slow for the sheer number of people needing checks. It creates these backlogs that frankly OPR can't afford when you're talking about integrity and security vetting.
SPEAKER_00:Okay. So let's dig into the platform itself then. If you're dropping up to$50 million on an existing AI, what specifically does ICE need ASEP to do in these background checks?
SPEAKER_01:Right. The core mission, according to the document, is automating and speeding up the initial assessment phase. That first look.
SPEAKER_00:First look.
SPEAKER_01:And this is where it gets really interesting, moving from just procurement talk to how AI gets involved in, well, human judgment, high-stakes judgment. The document lists five critical, very specific things this ASEP tech needs to do. And these requirements, they're designed to directly shape, maybe even partially replace, that initial human analysis.
SPEAKER_00:Okay, let's hear them. What's the first one?
SPEAKER_01:First, the platform must be capable of efficiently evaluating risk profiles.
SPEAKER_00:Evaluating risk profiles. Okay, that's uncentral. That's a judgment call, right?
SPEAKER_01:It's a massive one. Basically means the AI needs to take in all the data from a background check credit stuff, who they know, where they've traveled, maybe social media scans, all of it, and boil it down into some kind of risk score.
SPEAKER_00:Aaron Powell So the AI makes the first judgment call?
SPEAKER_01:Aaron Ross Powell Effectively, yes. The first assessment of risk.
SPEAKER_00:Hmm. But wait a second. A signing risk? That's incredibly complex. What if the historical data you feed the AI has, I don't know, built-in biases about certain groups or places people travel?
SPEAKER_01:Aaron Powell That's the huge question, isn't it? The algorithm might efficiently spit out a risk profile.
SPEAKER_00:Right.
SPEAKER_01:But the human adjudicator looking at that summary might have no idea why the AI flagged it. Can you come a bit of a black box influencing that crucial first impression?
SPEAKER_00:Aaron Powell A very towerful black box. Okay. What's requirement number two?
SPEAKER_01:Number two flows right from that. The platform needs to sort and prioritize investigative cases for human review.
SPEAKER_00:Ah. So if the AI flags a file as high risk.
SPEAKER_01:It jumps the cue, exactly. This shifts the power of triage, basically deciding who gets looked at first from a human manager to the algorithm.
SPEAKER_00:Aaron Powell That's a major operational decision being automated right there.
SPEAKER_01:Huge. And it connects directly to the third point. The AI will assign investigations to human adjudicators.
SPEAKER_00:So the system decides this case is hot and it decides you. Adjudicator number three, you get this one.
SPEAKER_01:Pretty much. The AI acts like a chief dispatcher for the investigators. Making sure the highest priority cases, as it defines priority through its risk scoring, get attention first. It's all geared towards speed and getting through the workload.
SPEAKER_00:Maximum throughput. Okay, number four. You said this one really gets to the heart of using LLMs here.
SPEAKER_01:Yeah, number four is crucial. The AI must generate concise summaries of critical information.
SPEAKER_00:Concise summaries. Okay.
SPEAKER_01:This is where the machine's ability to synthesize meets human reliance. Think about it. A full background file can be enormous. Thousands of pages, maybe, lots of fragmented data, sometimes conflicting stuff. Right. And the AI's job is to read all that noise and condense it down into maybe a a few key paragraphs, the critical information for the human to then review.
SPEAKER_00:Okay. I see the aha moment there. For the human adjudicator who's swamped with cases. Exactly. They're now trained to rely on this machine-generated summary. Their starting point, their whole initial framing of the case, is based on what the AI chose to pull out and emphasize.
SPEAKER_01:Aaron Powell Precisely. And if that LLM summary misses something subtle but important, or worse, if it, you know, hallucinates a connection that sounds plausible but isn't quite right.
SPEAKER_00:Aaron Powell The human analyst under pressure might just run with it. They might never dig deep enough to catch it because they started with the AI's narrative.
SPEAKER_01:Aaron Powell That dependence is absolute key. The goal here isn't necessarily to replace the human adjudicator entirely.
SPEAKER_00:Not yet, anyway.
SPEAKER_01:Right, but it's to shift their role. They become more like an editor or a reviewer of an AI-generated first draft.
SPEAKER_00:Trevor Burrus, Jr. Which dramatically cuts down the time they spend on each case.
SPEAKER_01:Aaron Powell Which brings us neatly to the fifth and final objective stated in the document: significantly reducing the overall time required to address key security issues. Speed.
SPEAKER_00:So it all lines up the urgency, the big budget, these very specific functions. It's all about speeding up that front end of the security vetting process using AI for synthesis and triage.
SPEAKER_01:Yep. It's a high-speed train driven by those top-level administrative priorities, which means the clock and the budget are definitely major factors.
SPEAKER_00:Right. And speaking of the budget and speed, let's just revisit those numbers. We said the estimate is$20 million up to$50 million. That's serious money.
SPEAKER_01:That figure alone tells you this isn't some small pilot program or experiment. This is funding for a full-scale, operational, probably quite sophisticated platform.
SPEAKER_00:They wouldn't put that kind of money down if they weren't serious about deploying it wide and fast.
SPEAKER_01:Exactly. The money reflects how critical they judge this need to be. It really underscores the government's push to make this AI transformation happen now, not five years down the road.
SPEAKER_00:And the timeline they laid out in the forecast backs that up, right? It's aggressive.
SPEAKER_01:Extremely forecast published early October, estimated solicitation release date, just days later, October 16th, 2025.
SPEAKER_00:Wow. They basically had the request for proposals ready to go.
SPEAKER_01:Looks like it. They knew what they wanted and had the funding lined up. And they expect to award the contract in the first quarter of fiscal year 2026.
SPEAKER_00:So vendor selected, contract signed within just a few months of this forecast going public.
SPEAKER_01:That's the plan. And get this the contract completion date is set for November 4th, 2026.
SPEAKER_00:Aaron Powell, wait, completion. So less than a year from putting out the call to having this AI system fully deployed.
SPEAKER_01:That seems to be the goal. Less than a year to implement a whole new AI architecture for national security vetting.
SPEAKER_00:That is lightning fast for government tech, especially for something this sensitive.
SPEAKER_01:It really is. The document does say competition is expected competition. Yeah, yes. And the place of performance is listed as remote NA, which makes sense for a software platform deal these days. They even list the points of contact, like a Greg Hermson and the small business specialist Samuel Thompson.
SPEAKER_00:So all the details are there. This isn't just aspirational.
SPEAKER_01:No, this looks like a concrete, fully planned, actively managed procurement. They've got the structure, the money, the tight schedule. It's a clear signal to industry that the government is serious about shifting these critical, very human processes onto AI platforms much faster than maybe we expected.
SPEAKER_00:Okay, so let's try and wrap this deep dive up with the main takeaways for you listening. What we've uncovered here is an urgent, very high value requirement, potentially up to$50 million from IC's Office of Professional Responsibility.
SPEAKER_01:Aaron Powell Right. And the goal is to quickly buy and deploy this thing called the ASP platform. It's a pre-existing AI, likely using large language models.
SPEAKER_00:Aaron Powell Specifically designed to automate and dramatically speed up that initial assessment phase of personal security investigations, background checks.
SPEAKER_01:Aaron Powell And the immediate relevance, I think, is pretty clear. AI is no longer just in the lab or doing back office tasks. It's moving rapidly into really sensitive, high-stakes, decision support roles within federal security.
SPEAKER_00:Aaron Powell Yeah, this acquisition shows a huge commitment backed by serious money to achieving, well, unprecedented speed and scale in how people are vetted. It fundamentally changes what a background check even looks like internally.
SPEAKER_01:Aaron Powell And it definitely changes the job for the human adjudicator, doesn't it? Their very first encounter with potentially life-altering information about someone is now filtered, summarized, and risk-grated by an algorithm.
SPEAKER_00:Yeah. Mediated by the machine.
SPEAKER_01:Which leads us to that final, maybe provocative thought to leave you with. If the main driver behind this$50 million platform is speed, the need to significantly reduce time, what does that trade-off really entail? When an AI generates those concise summaries of critical information that the human has to rely on, how does that pressure for speed for automation change how thoroughly we vet people for critical roles?
SPEAKER_00:Does efficiency start to redefine what we even mean by thoroughness in national security?
SPEAKER_01:That's the core challenge, isn't it? What happens when speed becomes the overriding goal in such a high stakes process?
SPEAKER_00:A question worth pondering as these systems roll out. Thanks for digging into this with us today.
SPEAKER_01:Always fascinating to explore the source material.