3 ways to strike the right balance with generative AI
To find the sweet spot where innovation doesn’t mean sacrificing your security posture, organizations should consider the following three best practices when leveraging AI.
Implement role-based access control
In the context of generative AI, having properly defined user roles to control who can access the AI system, train models, input data, and interpret outputs has become a critical security requirement. For instance, you might grant data scientists the authority to train models, while other users might only be permitted to use the model to generate predictions.
Robust role-based access control can also help ensure sensitive data is properly segregated and restricted to the right people. This not only reduces the risk of unauthorized access and potential data breaches, but also provides an added layer of protection by ensuring that each user can only perform actions within their designated privileges.
Secure the AI training process
During their training phase, AI models can be vulnerable to attacks designed to exploit and disrupt the training process.
Such threats might involve introducing subtly altered inputs into the system crafted to mislead the AI model into making incorrect predictions or decisions. While seemingly innocuous, these modified inputs can cause the AI to behave erratically or inaccurately. And since many AI models leverage user feedback to improve the model’s accuracy, there’s a real risk that bad actors can manipulate this feedback mechanism to alter the model’s predictions for malicious purposes.
Understanding the data flow within the training model is crucial to maintain data integrity. This means understanding how data is collected, processed, stored, and utilized within the AI model. It’s likewise important to have a clear understanding of every step in this data pipeline, to identify and mitigate any potential risks or vulnerabilities that could compromise data integrity.
Ensure AI models are explainable
Perhaps the greatest challenge posed by AI models is their potential to function as “black boxes,” with their inner workings shrouded in mystery. This opacity makes it challenging to discern the model’s decision-making process, rendering it difficult to identify instances when a model is behaving maliciously or acting inappropriately.
Emphasizing explainability and interpretability in AI models can be a powerful tool to mitigate these risks. Explainability tools and techniques can decode the complexities of the model, providing insights into its decision-making process. Such tools can also help identify the specific variables or features that the model deems significant in its predictions, thereby offering a level of transparency into the model’s operations.
Conclusion
The acceleration of technological innovation has led to many new AI applications being developed and introduced to the market at an unprecedented pace. However, the immense potential of AI will only be fully and sustainably realized when security is treated as a fundamental component rather than an afterthought.
IT leaders would be wise to prioritize implementing the right security controls before going “all-in” on their ambitious AI initiatives.