The rapid adoption of AI in various sectors faces significant challenges, particularly regarding data security, integration with legacy systems, and legal uncertainties. Data security is the foremost concern, with 55% of organizations highlighting risks associated with sensitive information such as patient records. Effective strategies include minimizing sensitive data handling, implementing encryption, and limiting third-party access. Additionally, integrating AI tools with outdated systems poses a hurdle for 39% of respondents, while 38% cite costs and skills gaps as barriers. Legal issues, especially around patient privacy and intellectual property (IP), are acute, with 66% expressing concerns about legal risks in AI implementation. Companies are increasingly establishing training programs, particularly in compliance with the EU AI Act, which mandates appropriate training for personnel handling high-risk AI systems. As legal frameworks evolve, firms must adopt flexible and clear documentation processes to navigate the complexities of AI governance and tight regulatory landscapes.
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