Ontological Data Security

In Discovered mode, Lattix builds the ontology organically from the ground up. As users tag data across the organization, the platform observes patterns, identifies recurring categories, and automatically infers relationships between tags. Over time, a structured ontology emerges that reflects how the organization actually thinks about its data — not how a consultant assumed it should be categorized. Machine learning models detect semantic similarities between tags, suggest consolidations, and surface gaps in coverage. Teams retain full control to accept, modify, or reject suggested ontology structures. This bottom-up approach is ideal for organizations that don't yet have a formal classification taxonomy or want to modernize an outdated one without disrupting existing workflows.

In Enforced mode, the ontology is defined top-down by security architects and compliance teams before any data is tagged. A formal taxonomy specifies the exact set of permitted tags, their hierarchical relationships, allowed combinations, and inheritance rules. Users tagging data are constrained to select from the approved ontology — no ad-hoc tags, no inconsistencies, no drift. The enforced ontology maps directly to regulatory requirements, ensuring that every tag applied has a defined compliance meaning. Tag validation rules prevent contradictory classifications (e.g., data cannot be simultaneously 'Public' and 'ITAR Controlled'). This top-down approach is essential for organizations in regulated industries, defense, and government where classification consistency is a legal requirement.

Hybrid mode combines the best of both approaches. Security architects define the core ontology structure — the required categories, mandatory tags, and compliance-critical classifications. Within that structure, users can discover and propose new tags that extend the ontology organically. Proposed tags enter a review workflow where they can be approved, merged with existing tags, or rejected. Approved extensions automatically inherit the parent category's security policies and compliance mappings. The ontology evolves continuously, staying current with how the organization's data landscape changes while maintaining the governance guardrails that compliance demands. Machine learning monitors for ontology drift, alerting when user-proposed tags begin diverging from the established structure.