Border Security
AI and name matching technology improve border security and watchlist screening to facilitate legitimate traffic while reducing risk.
What is border security?
The United States Department of Homeland Security defines border security as the act of “protecting … borders from the illegal movement of weapons, drugs, contraband, and people, while promoting lawful trade and travel.”[1] The United Kingdom Border Force describes it as any action that “secures the border and promotes national prosperity by facilitating the legitimate movement of individuals and goods, whilst preventing those that would cause harm from entering the UK.”[2] In South Africa, “border security is a comprehensive process that involves all functions to regulate and manage the movement of people and goods across borders.”[3] In India, border management agencies seek “border region development, communication, and coordination with neighbouring states and programs to enhance the national interests of India.”[4]
Although their wording differs, the intent of these definitions is consistent. Around the world, countries now look at their borders as national front doors. Nations want to provide easy passage to most would-be visitors who seek entry for legitimate purposes. This easy flow of people and goods is vital to national economies that increasingly depend on tourism and international commerce.
However, national security concerns demand that border security agencies also halt the flow of irregular migration. Defined as any unauthorized, undocumented border crossing, irregular migration is undertaken for a variety of reasons. Historically, people have illegally crossed borders to find economic opportunity. Increasingly, though, nations must face the possibility of mass irregular migration: throngs of people seeking escape from war, persecution, climate change, or natural disaster.
Most illegal immigrants intend no harm to the nations they enter. Still, many nations believe their presence threatens national economies and cultures. A handful present imminent, significant threat: terrorists, drug traffickers, human traffickers, arms dealers, and members of international crime rings.
Mass irregular migration can act as a cloak for these criminals. Consider this: Between April and December 2022, Poland logged more than 8.19 million border crossings from neighboring Ukraine.[5] These were Ukrainians seeking refuge after the Russian invasion. It’s easy to see how wrongdoers could have slipped into their ranks unnoticed. Similarly, significant numbers of immigrants from Mexico, Central America, and South America seek to illegally enter the United States via its border with Mexico.
Small boat crossings across the English Channel undertaken by people from Eritrea, Afghanistan, and other countries, cause concern in the United Kingdom. (Roughly 40,000 people entered England via small boats in 2025.[6]) In mainland Europe, the Schengen Area allows open border crossings among its 29 countries. Once in the Schengen, these open borders make it easy for illegal immigrants to travel from one country to another. This has caused friction among Poland, Lithuania, and Germany, among other countries, with Poland recently instituting temporary border controls.[7]
Learn the basics
eBook
The Honest Guide to AI for Border Security
How Babel Street can help enhance border security operations
Data Sheet
Stay Ahead of Border Screening Challenges with Babel Street Match
Longer examination as to why Match is a uniquely designed solution for border security
Webinar
Bridging Borders: How AI and Public Information Sources Enhance Integrated Border Management
News
Improving name matching in high-stakes, high-velocity scenarios
Improving border security through integrated management
Increasingly, border security agencies look toward integrated border management (IBM) to concurrently facilitate legitimate passage and improve national security.
In many countries, a broad array of agencies operating from federal to local levels of government assume some responsibility for securing land crossings, airports, and maritime borders. IBM is an approach for integrating border-related systems, processes, people, and data. It requires significant collaboration among these often-siloed agencies. This collaboration most often takes place among different agencies working at different levels of one country’s government. However, it can also occur among neighboring countries.
Who’s in charge here?
Nations often divide responsibility for border security among a plethora of different agencies. Take the United States, as an example. Even after integration efforts forged in the wake of the 9/11 terrorist attacks, border security agencies still include:
Federal agencies: U.S. Customs and Border Protection secures and facilitates operations at 328 ports of entry across the county, processing nearly 300 million visitors each year.[8] The Department of State issues visas, including nearly 11 million non-immigrant visas annually.[9] They are joined in their efforts by the Department of War; by the Department of Justice/Federal Bureau of Investigation; and by Department of Homeland Security agencies including Immigration and Customs Enforcement, the United States Border Patrol, the Federal Emergency Management Agency, and the Transportation Security Administration.
State agencies: State agencies involved with border security and customs enforcement include state police departments, the National Guard, and state departments of public policy.
Local and regional agencies: These include county sheriff’s offices, municipal police departments, and regional port and airport authorities.
The United States is not alone in its approach to customs and border security. In the United Kingdom, at least 27 different agencies have some role to play in border management.[10] These include security, policy, health, immigration, and customs agencies.
New technologies to better match names and glean insight from open-source intelligence (OSINT) aid IBM efforts — empowering border officials from disparate agencies to more efficiently and collaboratively screen people and businesses.
Learn more about border security
Case Study
USCBP: Strengthening U.S. Borders with Intelligent Name Matching
How US Customs and Border Protection uses Babel Street Match
Case Study
Enabling Stronger, Safer Borders with AI-powered Screening by Babel Street
How a major border security agency used Babel Street to help process hundreds of thousands of international travelers daily.
Blog
Address These Five Name Matching Issues to Improve Border Security
Customs and border security have never been more important. Around the world, professionals from a broad variety of federal, state, and local governments strive...
Blog
Why Customs and Border Security Need AI-powered Natural Language Processing
Customs and border security is a massive job, undertaken by a broad array of agencies worldwide. They all work toward a single goal: enabling the efficient cros...
Legacy technology hinders border security
Technology now used at many border security organizations inhibits the development of IBM processes. In worst-case scenarios, technological limitations may even keep border officials from effectively performing everyday tasks.
How?
Consider name matching. In border security and other mission-critical name matching situations, a false negative can have devastating consequences — leading to terrorists or other criminals entering a country. On the other hand, too many false positives result in slower-than necessary border operations — stalling legitimate trade and tourism.
To make these determinations at speed and at scale, border security organizations need identity risk intelligence systems that combine people-centric screening and investigations with continuous vetting. These systems unmask aliases and resolve identities across systems and languages
Let’s look at some of the shortcomings of existing technologies, and how modern identity risk intelligence systems can help overcome them.
Inadequate name matching capabilities
Too many customs and border security agents rely on outdated search platforms to match names in structured text, such as during watchlist screening. Returning only exact or near-exact matches, search platforms are fuzzy enough for general searches, but not expansive or fast enough for optimized name matching. Many accommodate only a limited number of languages, making it difficult to match for translated names, transliterated names, and names rendered in non-Latin scripts. They fail to spot aliases, nicknames, misspellings, honorifics, or out-of-order names. Binary processes also fail to accommodate the various naming conventions used in different parts of the world: not every country follows First Name/Middle Name/Last Name models.
Due to these inadequacies, border officials miss too many matches, allowing entry of criminals and contraband. Conversely, by returning too many false positives, these systems lead to unnecessary security alerts. Border officials must spend time investigating these unnecessary alerts, slowing the movement of legitimate travelers and goods.
AI-powered identity risk intelligence solutions can help border security professionals overcome the challenges of matching names in structured text. Automated algorithms use a variety of criteria to quickly, accurately, and intelligently match and disambiguate names of people, organizations, and locations across a broad array of languages, scripts, and databases.
Suboptimal entity resolution
Entity resolution is the process of distinguishing between similarly named entities appearing in unstructured text, then matching those names to entities appearing in a public knowledge base or the knowledge bases maintained by your organization.
Why is this capability important? Without entity resolution capabilities, State Department officials issuing B-1 business visas will struggle to distinguish between “Peter Smythe,” a history teacher in the United Kingdom hoping to take a camping vacation in the Western United States, and the “Peter Smythe” drug runner with significant cartel ties.
Entity resolution capabilities offered by modern identity risk intelligence systems automatically append identifying data to each name in order to distinguish one Peter Smythe from another. These identifiers include age, gender, street addresses, email addresses, and telephone numbers. Identifiers can also include information on the traveler’s family members, employment, and education.
Similar capabilities distinguish between corporate identities. Many companies have similar names. In addition, the corporate world commonly uses initialisms and nicknames: “PennyLuck Pharmaceuticals” may often be referred to as “PennyLuck Drugs.” You may regularly dine at your favorite chain restaurant, “Bobby D’s.” The right name matching and entity resolution solutions can link this initialism to the company’s official name, “Robert D’Amico Foods, Inc.”
Limited insight from open-source intelligence
Open-source intelligence (OSINT) is any insight gleaned from the analysis of publicly available information (PAI) or commercially available information (CAI). This data includes social media posts, news stories and videos, government data, and information appearing on surface web sites, along with sites on the deep and dark web. Border security officials can use modern identity intelligence systems to detect and track illegal cross-border activity; monitor the movements of individuals and groups of interest; and obtain real-time threat intelligence.
While OSINT technologies are now used by certain border security agencies, these systems are too often outdated, suffering from:
- Poor data quality — The PAI searched can be incomplete or outdated.
- Inaccurate data — Improperly curated PAI can contain errors, biases, and inconsistencies that negatively affect the validity of insights derived from it.
- Insufficient data sources — Too many PAI systems collect data from a limited number of English-only sources, leading to limited insight.
- An inability to accommodate data volume — PAI data is generated in huge volumes — too much for some OSINT technologies to appropriately search, process, and analyze.
Without cutting-edge technology to search, monitor, and analyze PAI in real time, border security officials are left without the insight needed to both ease passage for legitimate travelers and ban questionable people from entering their countries. Concurrently, modern identity risk intelligence systems help border officials better address both potential and present threats.
Siloed legacy systems
Efforts to integrate border management are further hampered by legacy systems’ inability to communicate with each other. One outdated system simply cannot work well with another to find and present the data needed to pre-screen travelers and businesses, then communicate findings among agencies. Replacing these systems, or retooling them to communicate better, can be prohibitively expensive.
The solution? Modern identity risk intelligence systems that work via application programming interfaces (APIs). Each API sits atop an agency’s existing systems. This helps organizations avoid the need to replace expensive systems to improve border security.
Lack of speed
When checking names and searching PAI, either from offices (as part of visa application processes) or at the border itself, many border security officials find investigative processes take too long. Old technology slows tourism and commerce. Worse, border agents charged with point of entry screening have only minutes to investigate each traveler. Slow, outdated technology adds pressure to an already challenging situation.
Powered by AI, modern risk intelligence solutions speed investigative processes. Tasks that might take a human investigator hours or even days to complete — such as detecting activities or information that might signal potential terrorism, drug trafficking, or other illegal activities — AI can do in minutes.
Why Babel Street?
Delivering mission-grade identity risk intelligence quickly and at scale, Babel Street sets a new standard for border security operations. Babel Street helps border security agencies meet their needs for better, faster identity risk intelligence. These capabilities bolster existing border security efforts and facilitate integrated border management.
Babel Street matches names across numerous languages and scripts, detecting aliases, nicknames, misspellings, and out-of-order names. To coalesce identities (to find the right “Peter Smythe” in a sea of “Peter Smythes”) our risk intelligence platform applies additional identifiers to each name examined. Similar capabilities help link corporate names to their nicknames and initialisms, and to names of their subsidiaries. In doing so, Babel Street both improves matching capabilities and dramatically reduces instances of false positives — saving investigative time. Clear confidence scores help users understand why our system has deemed two names a “match” or a “mismatch.” The system also empowers you to adjust match parameters according to your use case.
Babel Street collects and analyzes data from across all layers of the internet — including a broad array of web sites (including those hosted on the deep and dark web), social media sites, and real-word interactions generated on chats, in online comments, and in social media posts. Babel Street also searches its own large and diverse libraries of enriched data from commercially available sources. Conducting these searches and coalescing data, Babel Street matches names found in unstructured text to entities appearing in a public knowledge base or the knowledge bases maintained by your organization.
All the insight in the world does border officials no good if it’s presented in a language they can’t understand. This is obviously true for news articles, social media posts, and other pieces of PAI. It is also true for names. It’s a safe bet that very few American border officials would recognize the name “Владимир Путин” as “Vladimir Putin.” That’s why Babel Street automatically transliterates names and translates content from an array of different languages, helping border security officials to match names and monitor online content from around the globe.
Interoperability with legacy systems is a significant issue for those who want to deploy modern border solutions. Babel Street makes deployment easier. Our solution works on top of legacy systems to facilitate sharing from one application or data silo to another — helping users avoid the need to replace or re-tool older systems.
Working with Babel Street, border security agencies are often able to:
- Streamline border processing
- Dramatically reduce instances of false positive name matches while concurrently missing fewer matches
- Screen for threats with greater context and understanding
- More quickly respond to and mitigate threats
- Improve analysis and collaboration capabilities, forging a path toward integrated border management
Babel Street empowers border security officials with the critical capabilities needed to pierce data noise to obtain true insight. That’s why a majority of United States national security agencies along with similar agencies worldwide have partnered with us. And why our solutions are used for more than half a billion watchlist checks each day.
You might also like
Data Sheet
AI Solutions for Enhanced Border Security
How Babel Street's AI-powered name matching capabilities help border security agencies screen entities and reduce cross-border crime.
Use Case
Using Babel Street to Detect the Potential for Irregular Mass Migration
How Babel Street can be used to detect and help manage instances of mass migration
Use Case
Using Babel Street in the Fight Against Counterfeit Goods
Data Sheet
Babel Street Match
Benefits and features of Babel Street Match for intelligent name, address, and date matching.
Border security FAQs
Endnotes
1. U.S. Department of Homeland Security, “Border Security,” accessed December 2025, https://www.dhs.gov/publications-library/border-security#:~:text=Protecting%20our%20borders%20from%20the,economic%20prosperity%2C%20and%20national%20sovereignty
2. Gov.UK, “Border Force: About Us,” accessed December 2025, https://www.gov.uk/government/organisations/border-force/about#:~:text=Border%20Force%20secures%20the%20border,staff%20at%20ports%20and%20airports
3. Border Management Authority (South Africa), “About Us,” accessed December 2025, https://www.bma.gov.za/?page_id=5934
4. The Centre for Security Studies, “India’s Border Management,” accessed December 2025
https://jgu.s3.ap-south-1.amazonaws.com/jsia/India's+Border+Management.pdf
5. United Nations International Organization for Migration, “Poland – Ukraine Response 2022 – Crossing to Ukraine - End of Year Fact Sheet (12 April – 31 December 2022),” accessed December 2025, https://dtm.iom.int/reports/poland-ukraine-response-2022-crossing-ukraine-end-year-fact-sheet-12-april-31-december-2022?utm_source=chatgpt.com
6. BBC Verify, “Track UK's latest migration numbers - including asylum, visas and small boats,” accessed December 2025, https://www.bbc.com/news/articles/c70989jrdweo
7. Reuters, “Poland extends border controls with Germany and Lithuania until April 2026,” October 2025, https://www.reuters.com/business/finance/poland-extends-border-controls-with-germany-lithuania-until-april-2026-2025-10-01/#:~:text=The%20interior%20ministry%20confirmed%20to,Semczuk;%20Editing%20by%20Aidan%20Lewis
8. U.S. Customs and Border Protection, “Traveler and Conveyance Statistics,” accessed December 2025, https://www.cbp.gov/newsroom/stats/travel
9. U.S. Department of State, “Report of the Visa Office 2024,” accessed December 2025, https://travel.state.gov/content/dam/visas/Statistics/AnnualReports/FY2024AnnualReport/Table%20I.pdf
10. Smith, Tony, “Bridging Borders: How AI and Public Information Sources Enhance Integrated Border Management,” Babel Street webinar, accessed December 2025, https://www.babelstreet.com/landing/bridging-borders-how-ai-and-public-information-sources-enhance-integrated-border-management
Disclaimer
All names, companies, and incidents portrayed in this document are fictitious. No identification with actual persons (living or deceased), places, companies, and products are intended or should be inferred.