Managing Risk of Public AI Tools
As large language models (LLMs) become more popular in the business world, they offer powerful new ways to analyze data and enhance productivity. But for board members and executives dealing with sensitive, proprietary, and even regulated content, these models come with substantial risks, especially when interacting with public LLMs or AI-driven websites. Ensuring AI tools meet the highest security standards is critical to safeguarding confidential information.
Public LLMs often process data externally, relying on third-party servers that may not meet an organization’s security and compliance standards. This poses significant privacy concerns, as any proprietary or regulated data uploaded to these models could inadvertently be stored, cached, or even used to further “train” the AI. When this happens, there’s a real possibility that fragments of confidential information could resurface in unrelated contexts, potentially exposing strategic plans, financial insights, or regulated data to unintended users, or even to the public domain.
Such risks underscore the importance of using AI tools that meet enterprise-level security standards and align with the company’s internal IT policies. Solutions like Aureclar are designed with privacy as a core feature, ensuring that data processing occurs securely within an organization’s firewall and adheres to all relevant compliance protocols. Unlike public AI platforms, Aureclar’s infrastructure ensures that proprietary information is never sent to external servers or used to “train” generalized AI models, minimizing any risk of sensitive data being accidentally released or accessed.
In a time when data breaches are increasingly sophisticated, boards and executives must be vigilant. By choosing secure, compliant AI solutions that align with internal IT and governance standards, organizations can harness the benefits of LLMs while maintaining control over their proprietary and regulated data. For boards, this is a critical step in adopting AI responsibly, ensuring that confidential content remains private and compliant while enabling directors to make informed, strategic decisions.
Key Hurdles
Public AI models pose data privacy and compliance risks, as they process data on third-party servers.
Sensitive information could unintentionally surface in public domains, exposing proprietary data.
Limited control over information security when proprietary content is processed externally.
Suggested New Ways of Working
Enable secure, in-house data processing, ensuring proprietary information is protected.
Meet internal IT standards, safeguarding sensitive boardroom content and ensuring compliance.
Operate privately, eliminating the risk of accidental exposure or unauthorized access to sensitive data.