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Justice & Policing

Edinburgh digital forensics firm announces new counter‑terrorism capabilities 

Photograph: Tero Vesalainen/ Shutterstock

An Edinburgh digital forensics firm has announced new counter-terrorism capabilities to help global law enforcement agencies deal with rising threats online.

Cyacomb has launched a suite of tools which help investigators rapidly identify known terrorist material on digital devices as online radicalisation creates new challenges for law enforcement.

The tools are designed to support faster action against extremists and radicalised individuals who are progressing towards, or in support of, violence.

Chris Johnson, CEO of Cyacomb, said: “Technology has completely changed the environment in which radicalisation can take place. Traditionally becoming involved with an extremist organisation would require physical contact, but today an individual can access huge volumes of extremist material without leaving their bedroom.

“This makes the job of counter-terrorism authorities increasingly difficult. Someone can consume extremist propaganda, communicate with terrorists and progress towards violence behind closed doors. Identifying these warning signs as early as possible is essential.”

Cyacomb has developed the new counter-terrorism capabilities in partnership with subject matter experts, Human Cognition, to help law enforcement officers rapidly identify known terrorist material on suspect devices – no matter the nature or language of the content. This enables law enforcement to make quick sense of digital files, determining which are terrorist authored and therefore require special scrutiny.

The technology uses Human Cognition’s Terrorist Authored Material (TAM) Contraband Filter, which is built from a curated dataset of modern and historical terrorist material, and is human verified as being produced by recognised terrorist groups. Suspect devices can be checked against the material using secure matching technology, allowing investigators to identify known content without having to manually search through large volumes of unrelated information.

The new capability has been developed from an archive of over 3.3 million files circulating in terrorist online ecosystems over the last three decades. Of these, over 1 million files have been identified as terrorist authored materials.  

Human Cognition’s approach ensures only material produced by terrorist groups has been drawn from this archive to be included in the tool, drawing on over a decade of collecting near-real-time data of terrorist networks. This combination gives the tool the accuracy, breadth and depth to allow law enforcement to identify known terrorist content at speed and scale.   

“The speed, agility and resilience of the terrorist networks that share content online, and the mobility of potential terrorists in the physical world, requires tools that enable law enforcement to rapidly identify content on digital devices,” said Dr. Ali Fisher, Explorer of Extreme Realms and co-founder at Human Cognition.

Johnson added: “Digital devices can provide important insight into illegal or harmful material an individual has been accessing, but investigators may be faced with thousands of files and huge volumes of data when time is critical. The ability to rapidly establish whether known terrorist authored material is present on a device can help investigators understand the potential risk posed by an individual faster. Technology has given those seeking to spread extremist material new ways to reach people, law enforcement needs technology that helps it keep pace.”

The technology can support law enforcement when examining devices connected to individuals of concern, including cases where there are fears that a person is consuming extremist material or progressing towards more serious activity.

Home Office Prevent Statistics released in August showed that referrals to the UK’s counter-terrorism programme in England and Wales reached a record high of 10,293 referrals (involving 9,957 individuals) last year, marking a 39% increase compared to the previous year.

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