AI-powered innovation : safeguarding data within organisations in an era of generative AI

Abstract

This dissertation examines the strategic integration of generative artificial intelligence (AI) within organisational frameworks, emphasising the importance of fostering AI-driven innovation while safeguarding sensitive data against inherent vulnerabilities that come with it. The purpose of the study is to explore how IT security professionals are adapting their strategies to accommodate the growing presence of generative AI technologies and to identify effective strategies and best practices for mitigating risks while maximising potential benefits for organisational innovation and efficiency. Utilising a qualitative research design the investigation makes use of semi structured interviews with IT security professionals across various global locations, providing a detailed exploration of their experiences and the complex challenges they face with generative AI integration. The general findings indicate a significant inclination towards adopting generative AI with organisations integrating these technologies at varying levels while also emphasising the development of governance frameworks to effectively manage associated risks. Specific findings highlight strategies such as the development of comprehensive AI governance frameworks, AI risk management strategies, AI training programs and continuous monitoring to adapt strategies as deemed necessary. These strategies suggest that while generative AI offers many opportunities for innovation and operational efficiency it also introduces information protection challenges that create the necessity of careful management and strategic adaptation. The study helps both researchers and professionals by providing a balanced perspective that supports the responsible use of AI's potential, ultimately concluding that successful integration is reliant upon establishing robust security measures and developing ongoing ethical guidelines and policies.