There are several risk factors to avoid when implementing gen AI. The most important ones are accuracy and hallucination. Despite the technological advancements, models hallucinate extensively and there are currently no good solutions for addressing this. Solutions like LLM-as-a-judge or RAG are incomplete, and many organizations put humans in the loop to label and tweak answers. This can also help with solving issues like data privacy, toxicity and bias.
On the business level, enterprises must be aware that gen AI comes with unique risks that require a comprehensive strategic approach. Some of the most critical strategic initiatives are:
Data governance: Implement robust data governance practices to ensure the quality, integrity, and security of data used by Gen AI systems. This includes data collection, storage, processing, access control, and compliance with data protection regulations.
Security measures: Implement robust security measures to protect Gen AI systems from cyber threats, data breaches, unauthorized access, and malicious attacks. This includes encryption, access controls, authentication mechanisms, secure coding practices, and regular security audits.
Continuous monitoring and evaluation: Continuously monitor and evaluate the performance, effectiveness, and impact of Gen AI systems in enterprise applications. Identify emerging risks, trends, and issues and take proactive measures to address them.
For an overview and a few examples of gen AI risks, read the blog Implementing Gen AI in Practice
For a detailed approach, check out the book “Implementing MLOps in the Enterprise” by Yaron Haviv and Noah Gift.