PREVISION H2020

PREVISION was a European research project conducted under the Horizon 2020 (H2020) framework from September 2019 to December 2021. It was designed to enhance the capabilities of law enforcement agencies (LEAs) by providing them with advanced tools for processing and analyzing large-scale, heterogeneous data streams. In an era where criminal activities have become increasingly complex, interconnected, and technologically sophisticated, the need for innovative solutions to combat cybercrime, terrorism, and organized crime has grown significantly. PREVISION aimed to bridge this gap by integrating artificial intelligence, big data analytics, and knowledge graph technologies into law enforcement operations.

Objectives and Core Technologies

The primary objective of PREVISION was to enable law enforcement agencies to efficiently analyze diverse sources of data in real-time. The project sought to overcome traditional limitations in crime investigation by utilizing state-of-the-art machine learning algorithms, predictive analytics, and natural language processing to extract meaningful insights from various data sources. The core technologies developed within PREVISION were designed to support decision-making, facilitate proactive crime prevention, and enhance digital forensic capabilities.

A significant aspect of the project was its emphasis on cross-source data fusion. Criminal activities often involve multiple domains, ranging from online communications and financial transactions to physical surveillance data and traffic records. PREVISION aimed to seamlessly integrate these data streams into a unified analytical framework, allowing investigators to establish connections between seemingly unrelated pieces of information. This approach provided law enforcement agencies with a more comprehensive understanding of criminal networks, enabling them to act swiftly and effectively.

Data Sources and Intelligence Integration

PREVISION leveraged a wide range of data sources to improve situational awareness and threat assessment. These included:

  • Social Media and Open Web Data: Criminals frequently use online platforms for communication, recruitment, and coordination. PREVISION employed automated monitoring and sentiment analysis to detect potential threats and illicit activities.
  • Darknet and Deep Web: The project developed tools for analyzing hidden online marketplaces, forums, and encrypted communication channels where illegal transactions and cybercrime activities often take place.
  • Surveillance and Video Analytics: By integrating machine learning algorithms with video feeds from security cameras, PREVISION enabled real-time object recognition, behavior detection, and anomaly identification.
  • Traffic and Mobility Data: The movement of suspects, vehicles, and goods was analyzed through traffic monitoring systems, geolocation tracking, and mobility pattern recognition.
  • Financial Transactions and Fraud Detection: The system analyzed banking transactions, cryptocurrency exchanges, and financial records to detect money laundering, fraud, and illicit funding of criminal organizations.

By consolidating data from these diverse sources, PREVISION provided law enforcement agencies with a holistic view of criminal activities, allowing them to trace connections, anticipate threats, and disrupt illicit operations before they escalated.

Knowledge Graphs and Predictive Analytics

One of the key innovations introduced by PREVISION was the development of dynamic knowledge graphs. These graphs allowed law enforcement to visualize and analyze the relationships between different entities, such as individuals, organizations, locations, and events. Unlike traditional database systems, which often struggle with unstructured or incomplete data, knowledge graphs dynamically adapted to evolving information, making it easier to uncover hidden links between criminal actors.

Additionally, PREVISION incorporated predictive analytics to forecast potential criminal activities. By identifying patterns in historical data, the system provided law enforcement agencies with risk assessments and early warning indicators. This capability was particularly valuable in counterterrorism efforts, where detecting suspicious behaviors and connections in advance could prevent attacks and save lives.

Use Cases and Real-World Applications

The project was structured around five real-world use cases, each demonstrating how PREVISION’s technologies could be applied in practical law enforcement scenarios. These use cases covered a variety of security challenges, including:

  1. Counterterrorism Operations – Tracking and analyzing extremist networks to prevent radicalization and potential attacks.
  2. Cybercrime Investigations – Detecting and dismantling criminal activities on the dark web, such as hacking forums and illicit online marketplaces.
  3. Organized Crime and Drug Trafficking – Mapping the operational structures of criminal organizations involved in human trafficking, narcotics distribution, and illegal arms trade.
  4. Financial Crime and Money Laundering – Identifying suspicious financial transactions and tracing illicit money flows across borders.
  5. Public Safety and Emergency Response – Enhancing law enforcement’s ability to respond to security threats in real-time through AI-powered analytics.

These use cases ensured that the solutions developed by PREVISION were not just theoretical but practical, adaptable, and tested under real operational conditions.

Ethical Considerations and Legal Compliance

Given the sensitive nature of law enforcement operations and the extensive use of data analytics, PREVISION placed a strong emphasis on ethical considerations and legal compliance. The project adhered strictly to European regulations on data privacy, human rights, and transparency to ensure that its technologies were used responsibly.

Key ethical principles incorporated into the project included:

  • Privacy Protection: Ensuring that data collection and analysis complied with GDPR and other relevant legal frameworks.
  • Non-Discrimination: Avoiding bias in AI algorithms to prevent unfair profiling or targeting of individuals.
  • Accountability and Transparency: Providing mechanisms for auditing and oversight to ensure that law enforcement agencies used PREVISION tools in a legally and ethically sound manner.

By integrating these safeguards, PREVISION balanced the need for security with the protection of fundamental rights, ensuring that law enforcement could operate within a legally and ethically responsible framework.

Consortium and Collaborative Efforts

PREVISION was executed by a consortium of 28 partners, including universities, research institutions, private technology firms, and law enforcement agencies from multiple European countries. The multidisciplinary collaboration ensured that the project benefited from cutting-edge research in artificial intelligence, cybersecurity, and big data analytics, while also incorporating the practical expertise of law enforcement professionals.

This collaborative approach allowed for the development of solutions that were both scientifically advanced and operationally viable. The active involvement of law enforcement agencies in the project’s design and testing phases ensured that the resulting tools met real-world policing needs and could be seamlessly integrated into existing workflows.

Impact and Future Prospects

Although PREVISION officially concluded in December 2021, its impact continues to be felt across European law enforcement agencies. The project successfully delivered a set of powerful tools and methodologies that have strengthened crime prevention, intelligence gathering, and digital forensic investigations. Many of the technologies developed during the project serve as a foundation for ongoing research and development efforts in security and law enforcement.

The success of PREVISION has also inspired follow-up initiatives, further advancing the use of AI and big data analytics in combating crime. As threats continue to evolve, the methodologies pioneered by PREVISION will remain highly relevant, ensuring that law enforcement agencies have the necessary technological edge to protect societies from emerging security challenges.

While the official PREVISION website is no longer active, the knowledge and innovations generated by the project live on, contributing to the broader goal of enhancing public safety through data-driven intelligence solutions.