As organizations adapt to evolving regulations and heightened security needs, they must also address the current risks inherent in their operational landscapes. Data acts as the critical foundation for AI and emerging technologies, yet it is also a prime target for cybercriminals and other malicious entities. 企业面临的一个关键挑战是确保合法涉众和系统的数据可访问性,同时实现健壮的安全控制.
开始有效 风险管理 包括采用既定的 控制框架 如NIST CSF, ISO 27001或NIST 800-53. These 网络安全 frameworks aid in assessing how and where data is stored, enhancing the security measures across the organization. 实现安全, cloud-based environment helps safeguard data, allows essential access for necessary personnel and systems, 并防止数据被隔离在脆弱的位置,如本地桌面或外部驱动器.
保护敏感资料, 企业必须实施严格的控制,限制授权人员和软件的访问. Many organizations are familiar with the principles “least privilege” and “zero trust,” which dictate that access to information resources is granted solely based on necessity. The introduction of AI technologies complicates these dynamics, necessitating more nuanced control mechanisms.
AI operates as effectively as the data it processes, 强调高质量的必要性, 其功能的相关数据. Should AI systems access or analyze irrelevant or inaccurate data, 由此产生的输出可能存在缺陷. Such errors have the potential to infiltrate decision-making and compliance reports, 导致不良后果.
此外,第三方应用程序的集成引入了额外的复杂性. 与外部实体协作通常需要共享对其操作所必需的特定数据和系统的访问权限. 比如内部过程, 对于企业来说,确保这些第三方只被授予访问他们真正需要的数据的权限是至关重要的, maintaining stringent oversight to protect organizational integrity and compliance.