Benji Hutchinson is the CEO of Babel Street, and has over 20 years of experience in the identity, biometrics, AI, and computer vision industries supporting commercial and public sector customers globally.
In homeland security, artificial intelligence is no longer a distant promise — it’s already reshaping how risk is understood and managed every day. Agencies that once relied on labor‑intensive “big data” workflows, manually scraping and filtering open‑source information, are now turning to agentic AI systems that operate continuously in the background.
These AI “workers” help government operators cut through exponential data growth and disinformation, surfacing the most relevant insights for missions ranging from identity screening and vendor vetting to threat intelligence and complex investigations. The result is not the replacement of human judgment, but the pursuit of true data dominance: giving decisionmakers trustworthy, explainable intelligence at the speed modern threats demand.
The homeland security enterprise has never faced a more complex information environment. From global supply chains and cross‑border travel to online extremism and fentanyl trafficking, operators are inundated with data at a scale that would have been unimaginable even a decade ago. At the same time, the information environment is increasingly polluted by disinformation, partisan noise, and media sensationalism that can obscure real risk.
Against this backdrop, artificial intelligence is no longer a theoretical future capability. It is a present‑day force reshaping how risk is detected, assessed, and acted upon across the Department of Homeland Security (DHS) and the broader national security community.
But while “agentic” AI and risk intelligence tools are changing the way government operators work— it’s important to unpack what’s needed to build trustworthy, operationally sound AI for homeland security missions.
Moving Beyond “Big Data” Analytics
For years, government and industry have framed the information overload challenge as a “big data” problem: collect as much information as possible, then equip analysts with better filters, search terms, and workflows to find the signal in the noise. This is no longer adequate. Threat actors are exploiting publicly available information and flooding the environment with synthetic media and automated deception. Traditional investigative platforms and methods cannot keep pace, creating urgent demand for AI systems capable of closing this growing intelligence asymmetry.
Traditionally, investigators and operators would go out onto the internet and other open sources, manually scraping, downloading, and collecting data. They would then apply search techniques, algorithms, and filters in an effort to understand what was happening and where the risk lay.
Today, that workflow is being transformed by agentic AI — systems composed of AI-based agents that can continuously operate in the background doing 24/7 data collection and parsing, performing entity detection and linking (people, organizations, locations, networks), and automated surfacing of relevant signals.
Rather than spending the bulk of their time fiddling with filters, operators are increasingly able to focus on higher‑value judgment and action.
Three Core Use Cases for AI in Homeland Security
Babel Street approaches the homeland security and government market through three primary use cases that map directly to pressing mission needs:
- Identity
Identity is the cornerstone of many homeland security missions. AI‑enabled risk intelligence is being used to support:
- Screening and background checks
- Executive protection
- Due diligence and vetting
These capabilities help answer not only “Who is this person?” but also “Who are they connected to?” and “Who is appearing around their workplace or residence?” — questions that are increasingly critical in a world of global mobility and online radicalization.
- Vendor and Supplier Vetting
As globalization shifts toward greater onshoring and more complex supply chains, the question of who we do business with is becoming a national security issue.
AI‑driven vendor vetting and supply chain intelligence help organizations:
- Identify fraudulent or high‑risk entities
- Avoid “funding the enemy” or inadvertently enabling adversarial networks
- Support secure international trade and procurement
In this context, risk intelligence tools are used to screen suppliers and partners at scale, flagging derogatory information and anomalous patterns that would be difficult to detect manually.
- Threat Intelligence and OSINT
The third pillar is traditional threat intelligence and open‑source intelligence (OSINT). Here, AI is used to map and monitor:
- Networks of narco‑traffickers
- Suppliers of drug precursors such as fentanyl components
- Transnational criminal organizations and other adversaries
Investigators use AI‑driven tools to construct network views — who is connected to whom, where they operate, and how those patterns change over time. In an environment where data volume grows exponentially, this kind of automation is becoming essential.
Data Dominance, Not Human Replacement
In a world of exponential data growth and sophisticated disinformation, “fighting fire with fire” by using AI to counter AI‑enabled adversaries is becoming a necessity. But that must be done in a way that preserves human oversight, especially in sensitive operational contexts.
A central theme in AI conversations today is the role of the human in AI‑enabled operations. Despite the speed and scale AI offers, the objective is not to remove the investigator or operator from the equation.
Rather the goal is to shift their time away from manual collection, basic filtering, and repetitive workflows toward higher‑order judgment, decision‑making, and mission execution.
Babel Street refers to this objective as achieving “data dominance” — the ability for an operator to sit down and have relevant, reliable data already organized and prioritized at their fingertips.
Trust and Data Provenance
If AI is going to shape real‑world outcomes — from border decisions to criminal prosecutions — trust is non‑negotiable. That trust is built not just on performance, but on data provenance and transparency. Two things are necessary in this regard.
- High‑quality, reputable data sources: Babel Street spends significant effort working with data providers to secure sources that are rich in metadata and suitable for evidentiary use.
- Traceability to sources: AI‑generated leads or work plans must be traceable back to original data. When an investigator takes a case to court, it cannot rest on black‑box “hallucinations” or unverifiable outputs.
In practice, this means that every AI‑assisted insight still has a clear chain of evidence behind it. The AI may help find the needle, but the operator must still be able to show the haystack.
Behold the Emerging “AI Worker” Architecture
We are seeing the emergence of the “AI worker” — a powerful concept at the heart of agentic AI.
These AI workers are configurable to specific missions (e.g., identity vetting for aviation vs. vendor risk for procurement); tailored to individual users and roles; and designed to interact with other agents across DHS components, interagency partners, and even multinational and foreign partners, when and where appropriate.
The vision is that an identity or risk framework could be running continuously in the background. An operator could come in, grab a cup of coffee, open their system — or a mobile device in the future — and find decision points already teed up by the AI workers that have been operating overnight, exchanging information with other agents and systems.
Realizing this vision requires:
- Deep understanding of customer workflows and pain points
- Modernization of legacy processes
- Identification and sourcing of missing data
- Careful design of interfaces between human decisionmakers and semi‑autonomous agents
It will not be a quick or simple transformation; this will be a multi‑year effort.
For homeland security, this makes industry–government collaboration indispensable. As we at Babel Street often emphasized, ensuring that government and industry are talking early and often — well ahead of adversaries — is “mission number one.”
In this emerging era of AI workers and agentic risk intelligence, no single actor has all the answers. But the direction is clear: those who can combine trusted data, AI, and empowered human operators will be best positioned to secure the homeland in a world where both threats and tools are evolving at unprecedented speed.
