Executive Summary

Artificial Intelligence (AI) presents a transformative opportunity for businesses of all sizes, promising enhanced efficiency, innovation, and competitiveness. However, this potential is intrinsically linked with significant and novel security and privacy risks that demand careful management.¹ AI systems introduce unique vulnerabilities, such as data poisoning, model inversion, and prompt injection, which differ markedly from traditional software security challenges and necessitate specific mitigation strategies.³ Proactive AI governance, guided by frameworks like the NIST AI Risk Management Framework (RMF) and cognizant of regulations such as the EU AI Act, is crucial for responsible development and deployment.⁴ Alongside governance, the adoption of Privacy Enhancing Technologies (PETs) is becoming essential for balancing innovation with data protection.⁶ Among these, tokenization emerges as a particularly practical and valuable technique, especially for Small and Medium Enterprises (SMEs), by replacing sensitive data with non-sensitive tokens, thereby reducing data exposure and simplifying compliance efforts.⁷ SMEs face distinct challenges in adopting AI and managing its risks, including resource constraints, expertise gaps, and data handling complexities.¹¹ Nevertheless, addressing these AI risks is not merely a compliance burden but a competitive necessity. This report recommends that SMEs adopt a risk-based approach, leverage accessible governance frameworks and security tools, prioritize foundational data protection measures like tokenization, and foster a culture of continuous learning to navigate the evolving AI landscape securely and effectively.