Context rehydration is the process of rebuilding a conversation or application state from data that was previously compressed, summarized, or stored. In AI systems, this may involve restoring messages, metadata, and other state so a model can continue a conversation without retaining the full original history in active context.
Reconstruction matters for security because rehydration can alter how information is represented or which protections apply. If sensitive or restricted content is included in a summary, decoded incorrectly, or handled without its original safeguards, it could become visible to components or users that should not access it. Defenses include preserving access controls and sensitivity labels across transformations, validating reconstructed data, and testing that hidden or protected content stays protected throughout compaction and restoration.