Samenvatting

NarcoView aims to improve finding narcotic waste dumpsites by utilizing a methodology that reduces reliance on current reporting methods. The project faces challenges in data- and




main question is: How does the NarcoView project ensure secure handling of data used on the platform and customize it to meet the data security
requirements of the stakeholders?
The main design goal is to create a comprehensive data management plan that addresses potential risks associated with data handling. The research questions focus on assessing data risks, understanding data origin, data intention, specific risks, stakeholder security requirements, and potential solutions to minimize these risks in the NarcoView platform.
The research design method by Piet Verschuren and Hans Doorewaard involves a systematic approach with several phases. The methodology emphasizes the iterative nature of research, allowing for adjustments and careful documentation of methods and decisions throughout the process.
This research delves into understanding and managing data risks, identifying five key theoretical perspectives: data profiling, threat modelling, data modelling, data security legislation, and best practices. Abedjan 's data profiling methodology, emphasizing systematic data classification, is employed. Threat modelling combines OWASP and STRIDE for an example see 4.4 Results question 4. STRIDE categorizes threats comprehensively. While no laws directly apply to NarcoView 's data collection, best practices like 2FA and ISO 27001 can ensure data security and personal screening can avoid that possible malicious individuals get access to sensitive data . These frameworks serve as a guide in effectively managing data risks.
The NarcoView platform uses a mix of internal and external data sources, primarily for monitoring, predictions, and crime detection. The platform 's data is analyzed using OWASP and STRIDE methodologies, revealing various risks, related to API and external data sources. Stakeholders prioritize a secure platform that aligns with legal standards and effectively mitigates identified risks. The NarcoView project should integrate a mix of theoretical approaches and practical solutions, aligning with stakeholder expectations and requirements.
This research provides an understanding of data security in innovation projects. It focusses on regulatory frameworks like the EU 's AI Act. The research has limitations, such as reliance on the research group 's control over data security measures and limited stakeholder engagement. Future research should explore the integration of advanced AI and machine learning techniques for predictive analytics in data security and the human aspect of data security, considering user behaviour and organizational culture. The research provides a comprehensive understanding of data security challenges and opportunities within the NarcoView project.
This research recommends regularly updating and assessing data sources for risks, staying informed about laws, and implementing user access levels. These measures ensure data relevance, legal compliance, and enhance data security. Server security involves both online and physical aspects, with an emphasis on avoiding personal data storage. These recommendations are crucial for handling non-personal data.