Plagiarism, cheating, and other forms of academic dishonesty have become commonplace, making academic misconduct an increasingly urgent problem to mitigate in higher education. Additionally, AI-assisted writing technologies will increase, significantly impacting sustainable strategies to adapt to such technological revolutions. This chapter analyses the efficacy of AI detection tools in publicly available software, using Actor-Network Theory (ANT) as a framework to explore the complex relationships between students, educators, AI technologies, and institutional policies. This chapter examines recent studies on AI detection with a special focus on their objectives, approaches, and sampling techniques used in recent studies on AI detection efficacy, primarily to investigate the possibilities of varied results among these studies. The results of these studies point to promising results where the content is generated purely by GenAI. However, some studies found that the performance of AI detection is significantly impacted by adversarial techniques and obfuscation. Considering this, this chapter proposes the implementation of authorship attribution as a complementary technique in AI detection. By focussing on unique linguistic and stylistic characteristics of individual writers, authorship attribution offers a more reliable method of identifying academic misconduct. Integrating AI detection with authorship attribution proposes a more effective way to detect AI-generated content and strengthen academic integrity. This chapter advocates for a comprehensive approach that combines advanced detection techniques with institutional policies to meet the evolving challenges of generative AI technologies.