20 Expert Tips For Effective And Secure Enterprise AI Adoption

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Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.

As organizations explore ways to harness artificial intelligence, including the large language models that power generative AI, it’s essential to be prepared for both “misfires” and security risks. AI tools’ capacity for bias and returning false or misleading information necessitates careful training and prompting. Further, enterprise AI use cases often rely on interfacing with essential systems and accessing sensitive data, so robust security controls are critical.

It’s crucial to ensure generative AI models do not inadvertently expose or reveal confidential data used during training or prompting. Data governance protocols such as encryption, anonymization and access controls can protect data and maintain compliance. Monitoring and auditing AI system activities can also prevent security breaches, ensuring confidentiality and the integrity of data in the AI life cycle.

It’s important to keep in mind that the operators of free AI tools are provided with data that users voluntarily give them. Therefore, it is important to establish precise rules for providing corporate data to free tools. The situation is different if a company uses an AI product developed specifically for its needs; in this case, precise conditions for the use of the data can be agreed upon with the supplier.

The risk of AI model poisoning, which refers to the intentional manipulation of a model’s training data or learning process to reinforce one’s hypothesis or display biased behavior, should be a security concern. This could lead to suboptimal decisions, unintended discrimination against some user groups, and/or the exposure of sensitive information, resulting in violation of security protocols. -

Adversaries will try to manipulate AI-driven systems with injection-like attacks, embedding malicious instructions in legitimate user inputs. Since AI language models work within natural conversations, distinguishing data from syntax is more challenging than in a more familiar structured query language injection. Plan an adaptive process for filtering and sanitizing inputs to enhance security. -

 

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