Structure-Preserving Encapsulation

Definition

Structure-preserving encapsulation is the transformation step in an AI enablement data layer that converts operational data into AI-ready context. It tokenizes operational identifiers (device IDs, site IDs, asset references, customer segments) while preserving the relational structure the LLM needs to reason — table layout, log sequence, document hierarchy, configuration tree, topology graph.

Why structure preservation matters

An LLM asked to draft an RCA from an alarm sequence cannot reason if the sequence is destroyed. An LLM asked to compare two configuration trees cannot compare if the tree relationships are flattened. Structure preservation is what makes the capsule useful to the model, not just safe.

What gets tokenized

• Operational identifiers: DEVICE_ID, SITE_ID, CIRCUIT_ID, ASSET_ID, MISSION_REF
• Customer references and segments
• Free-text fields with mixed identifiers (NER masking)
• Sensitive attribute values (under DP budget)

What gets preserved

• Time order of events
• Cause-effect relationships in incident sequences
• Document structure (sections, lists, tables)
• Configuration tree (parent-child, dependencies)
• Topology graph (nodes, edges, paths)

Used together with

• Differential privacy — bounds inference risk on the capsule
• State vault for restoration — restores tokens to original values inside the enterprise
• Two execution paths — Path A or Path B per workflow policy

Reference statement

Structure-preserving encapsulation makes the capsule useful. Differential-privacy-based protection makes it defensible. State vault makes it restorable. Together, they constitute the AI enablement data layer.