Consider building construction specifications, which often follow the CSI MasterFormat document standard. CSI MasterFormat has 50 divisions, such as general specifications, electrical, and plumbing. Now consider access controls for this document, given that security is covered in two separate divisions and may require different classifications than other sections, such as equipment. But even that’s not sufficient context, as a general contractor should have different policies for accessing the specifications for a nuclear power plant than for a small office building.
Complex classification challenges are being addressed with AI and advanced algorithms. “Enterprises are shifting toward commodity-driven, API-driven governance accelerators, especially in areas like classification, taxonomy management, and domain-specific labeling,” says Nandakumar Sivaraman, senior vice president and chief architect of enterprise data at Bridgenext. “Instead of manually applying categories, rules, and policies across thousands of assets, companies are now using AI-driven classification APIs to auto-tag and categorize data. They use machine learning–based pattern detection to assign taxonomies, product hierarchies, or entity domains, and implement lightweight governance microservices for real-time classification in ingestion pipelines.”
Another approach uses vision language models (VLMs) to analyze the document’s visual structure for additional contextual clues. Harpreet Sahota, hacker-in-residence at Voxel51, says VLMs can classify documents without training data, but the bigger issue is that most organizations don’t have consistent taxonomies to begin with. “A first step is to treat documents as images rather than just extracting text, which preserves layout information that is important for understanding structure,” recommends Sahota.
