The field of AI-driven data management and compliance is moving towards developing more robust and scalable solutions to tackle the challenges of data drift, missing data, and regulatory compliance. Researchers are exploring the use of machine learning, natural language processing, and graph-based models to improve the accuracy and efficiency of data analysis and decision-making. A key focus area is the development of policy-driven AI systems that can ensure privacy, performance, and compliance with regulatory frameworks. Another important trend is the use of embeddings and semantic similarity modeling to automate the association of AI incident reports and improve the maintenance of AI incident databases. Noteworthy papers in this area include:

• A paper that proposes a comprehensive taxonomy for classifying privacy-preserving and policy-aware AI techniques, offering a clear framework for practitioners and researchers to navigate trade-offs.
• A paper that presents an embeddings-driven graph for linking millions of artifacts in enterprise environments, improving recall and query match rates for compliance AI agents.
• A paper that develops a retrieval-based framework for automating the association of new AI incident reports with existing incidents, achieving superior performance using transformer-based sentence embedding models.