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      "text": "For GenAI to reach its full potential business value and adoption, it must be trusted and secure. Attempting to scale without accounting for trust in data and the machine that consumes it can have implications for regulatory compliance, finance and strategy, cybersecurity and privacy, adoption and change management, and brand reputation—the consequences of which can limit or even erase GenAI's intended value. Risk and trust need to be considered and addressed across the GenAI lifecycle, from design and development through deployment and scaled implementation. This includes validation processes and feedback loops for human oversight to manage solution performance and accuracy. It also includes guardrails to ensure privacy, drive ongoing compliance, and promote agility in proactively responding to emerging risks. Data security is particularly essential. Differentiated GenAI applications are fueled by sensitive, proprietary enterprise data. Thus, training and usage can potentially expose or leak business-critical data and create risks to the organization. This is not a one-time event—organizations must make this part of regular work, rather than a separate consideration.",
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