Where to look for generative AI risks

Generative AI’s Dual Impact on Business: While generative AI offers significant productivity and growth prospects, it also substantially increases business risk, particularly during full-scale deployment.
Two Types of Generative AI Risk: The research by MIT Center for Information Systems Research identifies two types of risk: embedded risks, inherently tied to the technology (influenced by model behavior and training data), and enacted risks, shaped by organizational choices in deployment (system prompts, safeguards, and agent permissions).
Components Leading to Risk: Potential risks illustrate from a hiring manager using AI tools to draft a job description, including issues with training data, foundation models, user prompts, and system prompts.
Advanced AI Uses Introduce Additional Threats: Sophisticated deployments, such as retrieval-augmented generation and AI agents, can expand the risk space through data quality issues, control gaps in vector databases, and challenges in accountability due to autonomy creep.
Action Plan for Mitigating Risk: Organizations should map exposure, adopt dual approaches to risk management, establish clear ownership, and maintain audit trails to understand and mitigate the generative AI risk space effectively.

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