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Taming information overload for effective border controls

Cabrini Tan / 3 min read.
June 29, 2017
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By Dr. Enrique Segura, Speaker at INTERPOL World 2017, CEO & President, Securiport

In 2016, global outbound travel exceeded $2 trillion, for the first time ever. Despite a marked increase in security threats, people all around the world still desired to travel and take vacations. However, the sheer volume of people crossing through borders daily has made it very difficult for immigration security analysts to do their jobs of investigating crime at or across borders.

In the age of big data, authorities tasked with border security have too much information to deal with, yet are responsible for quickly finding threats before they affect their country. Trained, experienced analysts are essential yet scarce resources. Training a new analyst is a process that spans months and years, and that requires significant investment on the part of the border security authority.

The answer to this has traditionally been to provide the analysts with ever more complex tools. These analytic systems provide the equivalent of a workshop, materials, and tools for a trained, seasoned, and practicing analyst who can then build anything they want from the available data.

For a new analyst, however, these systems can be overwhelming, as they present huge amounts of data and palettes of analytics to choose from, usually with little supporting training or tutorials. Even for a trained analyst, knowing which analytics to use to answer a question is daunting and leads to analysis paralysis.

We need a way to provide all analysts with a guided path to their objective, while enabling advanced analysts to operate unguided based on their developed tradecraft.

Principles of guided analytics

Systems developed with guided analytics focus on aiding border security officials with a clear set of starting points and then steering them towards a set objective. Guided analytics is built upon the following four principles: ‘


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Start with the objective, not the data

We interact with a system with a purpose in mind. It’s easy to lose that purpose when confronted with data do we start with the traveller that matches our narcotrafficking profile, or the organization that they may belong to, or the address of the hotel they stayed at while in country? With guided analytics, security officials must first define their end goal. The system then suggests data that helps meet that objective saving officials from scouring through hundreds of data points to connect.

Set realistic limits and expectations

Guided analysis isn’t going to magically produce an answer in all cases. For example, trying to establish a connection between travellers, a jihadist organization and prison radicalization will logically need intermediate data such as prison visitation records so that correlations can be made.


Automate a common path

Experienced analysts will develop tradecraft ways of reliably getting from their question to an answer. These can be identified and automate into the guided analytics system. The best path is one which reliably produces an answer with data that we are likely to have. As a best practice, there should not be too many options or customizations to prevent confusion.

Provide an off-ramp

An experienced user may discover their own path to the objective, or even identify a completely new objective, while being guided. The system should provide a way to pursue this new objective, and if possible mark the point of departure so that the user can return to the guided path.

The ease of international travel has led to greater flow of information, goods, people and business across borders. Inadvertently, these advancements have also made it easier for criminals to exploit security gaps to their advantage. While technology continues to provide officials with more intelligence, a guided analytics system presents a smarter, more efficient way for immigration officials to navigate the mounting swamp of data at border controls so that any anomalies can be detected swiftly and more accurately.

Categories: Big Data, Cybersecurity
Tags: analytical culture, analytics software, big data analytics, big data security, Data security, national security, security analytics

About Cabrini Tan

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