On 16 October 2019, eu-LISA held its Annual Conference “The New Information Architecture as a Driver for Efficiency and Effectiveness in Internal Security”, in Tallinn, Estonia. The event was organised under the auspices of the Finland’s Presidency of the Council of the European Union.
This was the Agency’s 6th annual conference, but the first to take place since the provision of an extended mandate to the Agency through the approval of the new eu-LISA regulation. The enlarged mandate provides for the development of several new large-scale IT systems – the Entry/Exit System (EES), European Travel Information and Authorisation System (ETIAS) and the European Criminal Records Information System for Third Country Nationals (ECRIS-TCN) – as well as the implementation of Interoperability. Together, the new capabilities offered and the new approach to data organisation and information provision constitute a new information architecture at EU level. As the Agency embarks on the elaboration of this architecture, working closely with Members States, EU Agencies and the wider stakeholder community, the time is right to reflect on how work can be best be carried out and to define desirable milestones and end states that best fulfil the stated goals of improving the efficiency and effectiveness of internal-security related efforts. By convening up to 180 delegates from this stakeholder community and providing a forum for debate and discussion, the conference will be an important contribution to these efforts to define optimal future outcomes.
Following the keynote presentations in which senior decision makers provided their perspectives on the future of internal security and opportunities presented by the new information architecture, separate panels examined the future roles of current and new IT systems and the overall information ecosystem being created on law enforcement and border management activities. The consolidation of large-scale IT systems also leads to better possibilities to examine data together and take into account the context offered by broader views. The possibilities of applying machine learning and artificial intelligence capabilities to derive better insight from system data than is currently the case are clearly enhanced in the future state being created.
