Artificial Intelligence and Interoperability for Solving Challenges of OSINT and Cross-Border Investigations

Abstract

The major investigation challenges are summarised as multiple-identity, fraudulent actions, lack of interoperability and absence of an effective technical solution for exchanging Cross-Border information, and complexity of OSINT investigations. The EU published Regulations (EU) 2019/817 and 2019/818 for establishing a framework for EU interoperability be-tween information systems in the field of borders and visa information systems, police and judicial cooperation, asylum, and migration. Existing systems such as EURODAC, SIS / SISII, and VIS must share data, and new systems such as ECRIS-TCN, EES, and ETIAS also need to follow these guidelines. Although the eu-LISA will implement the interoperability framework in 2023, new challenges will emerge, such as investigating multiple-identity and identity frauds due to the different formats and structures of data, low quality of biographic and biometric data, and low accuracy of matching algorithms.
Furthermore, the Open Source Intelligence (OSINT) investigation process is not automated, consumes a lot of
time, and is overwhelming. When border security and law enforcement officers use methods of OSINT to investigate terrorism and serious crime, it is very difficult to match and link the identity-related data and facial images of the suspects stored in the EU systems, Cross-Border systems, and open sources.
The paper argues different Artificial Intelligence (AI) methods and algorithms and interoperability could be the
optimum solution for the challenges mentioned above. The paper highlights a Person-Centric approach using
Artificial Intelligence and interoperability to solve the challenges that emerge during investigations, such as multiple-identity, identity frauds, exchanging Cross-Border information, and the complexity of OSINT investigations.