Artificial Intelligence threat Reporting and Incident response System (IRIS)

Reference framework

The digital public services offered by the City Council provide for more effective and efficient services for city residents. These services require a resilient infrastructure, with a precise operation and maintenance centred on data security and protection. IMI decided to take part in the Secure societies - Protecting freedom and security of Europe and its citizens Horizon 2020 European Framework Programme, in the call for Intelligent security and privacy management projects, with the proposed artificial Intelligence threat Reporting and Incident response System (IRIS). Acting on behalf of Barcelona City Council, IMI is taking part in this European project, along with 18 other European partners, and led by Portugal’s INOV.


The goal of the IRIS project is to integrate a unique platform aimed at the Internet of Things (IoT) which, using artificial intelligence (AI), will provide support for computer emergency response team centres (CERT)  to assess, detect, respond to and share information on threats and vulnerabilities in ICT systems.

The IRIS platform will be tested in three European cities (Barcelona, Helsinki and Tallin), with trials centring on IoT, AI and cross-border dimensions in the three environments in these cities involving computer incident response teams (CERT/CSIRT), whether national, governmental, affiliated, cybersecurity authorities or municipalities.

The IMI will be tasked with building, operating and maintaining the test zone for the project in Barcelona.

Test scenarios will include cyber threats based on real life, to be developed through trials at all levels (local, national and cross-border) to demonstrate the versatility of the IRIS solution. The first test in Barcelona will use technology developed to improve safety and prevent potential accidents involving vulnerable passers-by (pedestrians and people with personal-mobility vehicles such as bicycles and scooters.) 



This project has received funding from the European Union’s H2020 research and innovation programme under grant agreement number 101021727

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