The emergence of artificial intelligence (AI) brings data governance into sharp focus because grounding large language models (LLMs) with secure, trusted data is the only way to ensure accurate responses.
So, what exactly is AI data governance?
Let’s define “AI data governance” as the process of managing the data product lifecycle within AI systems. To keep it simple, we can break down AI data governance into two main components.
The first is AI data privacy because any personally cambodia rcs data identifiable information (PII) or other sensitive data must be protected from unauthorized access and use, accessible to only one user (and nobody else), and comply with data protection laws like the California Privacy Rights Act (CPRA), the General Data Protection Regulation (GDPR), and Health Insurance Portability and Accountability Act (HIPAA).
In addition, bad actors keep trying to manipulate LLMs into giving away sensitive and PII data, by pretending to be someone else and asking the LLM for their credit card number or SSN. This makes data privacy even more important in the GenAI era.
The second component of AI governance is data quality, in two respects, since using data in AI systems is a two-way street: what goes in and what comes out.
What goes in is the data used for training and augmenting AI models, which needs to be clean, complete, and current in order to respond to user queries as accurately and responsibly as possible.