Reviewing the 5 Major AI Risks (Part II of II)
As companies increasingly integrate AI into their operations, understanding the associated risks becomes crucial. This article explores five primary risk areas when utilizing AI in supportive or assistance-based roles, rather than algorithmic-based use cases.
1. Data Protection and Cybersecurity
The use of AI tools may involve handling sensitive data, including confidential, personal, or regulated data. If employees incorporate such data into AI vendor tools, there are risks of retention, transfer, or disclosure without proper controls. For instance, uploading attorney-client privileged material into a third-party AI tool could jeopardize the confidentiality of that information.
Moreover, if data is transferred across borders via an AI tool, this may violate applicable data regulatory requirements for pre-approval or waivers. Companies must ensure compliance by identifying AI tool usage and providing guidance on acceptable data types for entry into these tools, potentially requiring specific vendor compliance provisions to prevent unintended data exposure.
2. Third-Party Vendor Risks
Understanding how employees access and utilize AI tools is essential, especially when tools are procured informally. If a company engages third-party AI services or learns that a third party uses AI in service provision, compliance must identify and mitigate the associated risks through procedures and contractual provisions.
Risks can vary significantly; for example, employees might use widely available AI tools without approval from the company’s IT department or compliance team. Additionally, the data handling practices of third-party vendors may not be transparent, raising the risk of violating data privacy policies. Conducting thorough due diligence on AI providers is critical, particularly in regulated sectors like healthcare and financial services.
3. Misinformation and Intellectual Property
One of the key areas of concern is the risk of misinformation. For example, lawyers relying on AI for legal research face significant dangers, as AI-generated materials often have high error rates and may cite irrelevant cases. Beyond legal research, companies must be cautious about relying on potentially inaccurate information that could lead to liability issues, including defamation or intellectual property infringement.
To mitigate these risks, companies should implement content review controls to ensure accuracy and protect against disinformation. In the age of information, where accuracy is not guaranteed, organizations must assess misinformation risks across various market sectors and develop multi-level validation processes to ensure the integrity of the information they disseminate.
4. Workplace Risks
HR professionals are increasingly using AI tools for various employment functions, such as employee monitoring and AI-generated evaluations. While tempting, such practices may create disparate impacts and require careful disclosure and review to avoid actionable compliance issues.
Even if AI is not directly involved in key hiring decisions, its use in generating documentation and evaluations must be scrutinized to ensure compliance with employment regulations and to prevent potential legal repercussions.
5. AI Regulations
The landscape of AI regulations is rapidly evolving, with federal, state, and local governments implementing comprehensive frameworks. While some federal arguments advocate for a light regulatory touch, state and local governments are taking a more proactive approach. Companies must continuously monitor these developments to update their compliance programs accordingly.
In conclusion, navigating the risks associated with AI requires vigilance, proactive compliance measures, and an understanding of the regulatory landscape. It is essential for companies to maintain a robust framework to address these challenges effectively.
