While we have covered different aspects of data-related risks in some of our earlier articles, this whitepaper provides a more comprehensive view of data and AI risks across the entire spectrum of business activities. We will walk you through examples of the different business areas that generate the most amount of risk, including new product development, predicting customer behavior, and customer nano-segmentation.
Looking at these examples, we analyze the specific reasons for the increased data and AI risks, including issues with data security, metadata, data access, and data quality. For each example, we provide a set of methodologies and best practices for addressing the posed risks including, automated testing, a review of your AI/ML model, and data security policies.
Additionally, we address considerations such as security in the cloud and automated vs. semi-automated decision-making. Finally, we discuss some of the roadblocks you may face with implementing a risk management plan and how you may overcome them.
This whitepaper serves as a guide to understanding and addressing some of your business’s data and AI risks. As many organizations struggle to demonstrate the value of risk management, aside from viewing it as an insurance policy, Adastra has helped our clients develop a business case for risk prevention that demonstrates ROI and identifies tangible business benefits.