GAO Warns AI Bias, Privacy Risks in US Financial Services

AI technologies are increasingly integrated across various sectors, but their deployment raises significant operational and security challenges. In software development, 'quiet AI' tools operate unobtrusively in the background to automate and optimize workflows, improving efficiency without demanding user attention. However, in cloud-based AI environments, excessive privilege—where users or services have more access than necessary—poses a critical security risk, potentially leading to data breaches or model theft that can propagate across multiple platforms undetected. The financial sector faces risks from AI as well, such as bias in lending decisions and data privacy concerns, with AI models potentially reinforcing discriminatory practices due to poor-quality or biased training data. Regulatory bodies are urged to develop new risk management guidelines to mitigate these issues, emphasizing the importance of addressing bias, cybersecurity threats, and the ethical deployment of AI. Meanwhile, concerns about the use of generative AI in academic assessments highlight broader debates about AI's role and risks in evaluative contexts.