Harnessing GenAI for Simplified Data Governance
The plethora of GenAI applications we have come across like chatbots, image creation, and natural language processing (NLP) interfaces considerably enhance data’s value, leading to increased revenue, efficiencies and cost savings. They can also deeply complicate data governance. Many GenAI data training sets have fairness, transparency, bias, and ethical concerns. GenAI also increases the risk of department-level staff inadvertently breaking compliance frameworks by sharing internal data on public platforms.
Data teams are super stretched in building data dictionaries, tracking compliance reports, and keeping core systems up and running. It’s no surprise they are likely to see GenAI as both an opportunity and a potential huge burden to an already full workload.
Usually the GenAI/data governance conversation tends to be one-sided and overly focused on challenges. However, GenAI can benefit in lightening data workloads and shift precious time to value-added tasks.
There is a strong push for a new data governance operating model, one that incorporates GenAI into data governance processes and scales effectively alongside GenAI’s adoption curve.
A data governance operating model that leverages GenAI embraces the power of automation and scales analysis beyond what data teams can do alone. Here are just a few areas where GenAI can enhance data governance:
One example is data classification and tagging. The data sets needed for AI and GenAI are huge, and no team can manually manage governance processes for those data volumes. GenAI can automate the categorization and tagging of contextual data through NLP. It can help drive active learning and check for explainability and transparency, optimizing the labeling process for the most informative data points. It can also help with metadata analysis, identifying characteristics based on data type, format, or sensitivity. Applying GenAI to these efforts saves time and helps organize data to meet compliance regulations such as HIPAA, GDPR, and CCPA.
Another area is policy creation, where GenAI can assist with new policy development based on historical data, organizational structures, and regulatory requirements. It also can help data teams better understand adherence, classify risk levels and map to associated policies and policy frameworks, with recommendations on adjustments related to new data patterns to ensure proper data use.
GenAI can also help in data discovery by creating metadata, context, and lineage for each data asset and generating descriptions through natural language to help users understand content, quality and value.
There are many other potential applications, including: explaining lineage of a dataset to enhance trust; enabling dynamic data access based on roles, permissions, user persona and usage context; and creating synthetic data for training data discovery models.
The data demands of GenAI can create data governance challenges if we let them. However, applying GenAI to core data governance needs like data classification, data quality, policy and remediation, data privacy, and compliance can help alleviate pressures and recoup precious time for teams to tackle more important strategic priorities.