On the 22nd of November 2022, the European Law Enforcement Research Bulletin of the CEPOL (European Union Agency for Law Enforcement Training) published a research paper titled “Artificial Intelligence and Interoperability for Solving Challenges of OSINT and Cross-Border Investigations”. The paper describes a newly proposed Person-Centric OSINT approach using Artificial Intelligence (AI) 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. This is a series of blog posts that describes the research.
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 between 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. The EU interoperability components include the European Search Portal (ESP), in addition to Europol and Interpol data; the Shared Biometric Matching Service (sBMS); the Common Identity Repository (CIR), and the Multiple Identity Detector (MID) (European Council: Council of the European Union, 2019). 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 recent global threats such as the increase of illegal immigration, the high risks of terrorism and serious crime, the COVID-19 pandemic, and the war between Russia and Ukraine created the essential need for exchanging Cross-Border information for preventing, detecting, and investigating terrorism and serious crime across Europe and the neighbouring countries.
Finally, 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.
- multiple-identity multiple identity OSINT interoperability Cross-border interoperability biometrics Fraud facial recognition Surveillance cybercrime cyber cybersecurity machine learning big data AI Artificial Intelligence EU law enforcement networks border immigration law enforcement border security Fight against terrorism terrorism combating terrorism serious crime organized crime combating serious crime Russia ukraine Ukraine war war in Ukraine