Enhancing Project Security
Marwan Alshar’e, Abdallah Mohd Abualkishik, Khaled Abuhmaidan, Ahmad K. Kayed · 2024
The widespread adoption of Artificial Intelligence (AI) has led to significant changes in various technical domains. However, this has also led to significant challenges in project security. This study aims to explore the relationship between trust, interpretability and explainability in AI systems, which are crucial factors impacting project security. Trust is a highly valued currency in the technology domain, and establishing trust in AI systems is essential for secure project management. Transparency in AI algorithms and models promotes trust, promoting reliability. Interpretability is a crucial aspect of making stakeholders&s; understanding of AI&s;s decision-making procedures visible and intelligible. This helps in improving security assurance and fostering confidence in AI-driven projects. The integration of these components enhances security measures and fosters a perception of confidence. Explainability is a powerful method for identifying and reducing security vulnerabilities in AI systems. It provides security professionals with the ability to identify vulnerabilities, mitigate threats, and make well-informed decisions, enhancing the resilience of AI-driven projects. The issue of project security within AI presents a complex and multi-dimensional challenge, including data security, adversarial assault protection and AI system reliability preservation. This study aims to examine the integration of trust, interpretability and explainability in AI systems to address these challenges and ensure a secure, morally sound and forward-thinking technological landscape.