Ontological Data Security
Architecture Direction · Supported Workflows EvolvingSecurity That Understands Context.
Governed semantic context for data and AI-mediated actions.
Flat labels describe what data is called. Ontological security describes how data types, purpose, lineage, regulatory requirements, and business context relate — then turns approved relationships into policy inputs. Model-assisted discovery can propose meaning; governed review and policy enforcement decide what becomes trusted.
The semantic layer separates discovery from authority. Proposed relationships can inform review, while only governed context participates in policy-bound security decisions.
/01From Signal to Enforcement
Model-assisted discovery
Automated analysis can surface candidate relationships, classifications, and semantic signals. Candidates remain proposals until governed review accepts them.
Canonical semantic context
Approved relationships become versioned, tenant-scoped context with provenance and clear ownership of the meaning being applied.
Policy-bound constraints
Semantic context can be compiled into explicit, bounded security constraints for supported data and AI-mediated workflows.
Independent enforcement
Verification produces evidence for the authorization path. Policy enforcement and protected-data controls remain the final authority.
/02Three Operating Modes
Discovered
Use model-assisted analysis and observed data relationships to propose an ontology that reflects how the organization actually works.
Enforced
Start from an approved taxonomy with explicit relationships, allowed combinations, and governance rules for regulated workflows.
Hybrid
Keep compliance-critical structure governed while allowing new relationships to be proposed, reviewed, accepted, or rejected over time.
/03Semantic Security for AI
A Qualified Neurosymbolic Security Architecture
Lattix combines model-assisted interpretation with explicit semantic representations, rules, and repeatable verification. This is a systems architecture, not a claim that model output itself is deterministic or that every model behavior is formally proven.
Context-aware policy
Policies can reason over relationships between data type, purpose, lineage, regulatory context, and the workload requesting access.
Reviewable evolution
Semantic suggestions are separated from authoritative state, giving security teams a controlled path to accept, modify, or reject change.
Bounded verification
Supported AI-mediated actions can be checked against explicit constraints rather than relying on model confidence alone.
Portable meaning
Security context can travel with governed data and remain useful across systems, trust zones, and AI workflows.
Semantic context helps explain why a security decision applies. It does not become authority by itself.
Verification is evidence about a supported action. Final authorization, key release, and protected-data enforcement remain separate controls.
Put Meaning Behind Your Data Policies
See how governed semantic context can strengthen classification, data-centric policy, and AI security workflows.