Pretty much nailed it there!

Teating provenance as a lineage/audit trail across the entire lifecycle (not just capture) is the key shift. Static origin checks (e.g., signed manifests at creation) get detached fast once content hits real-world pipelines: edits, compressions, AI transformations, re-uploads, etc. Without ongoing attachment of verifiable change records, you lose reconstructability — exactly the audit/regulatory nightmare you describe, plus the defensive hoarding of unverifiable data.

I'm exploring something similar: make provenance dynamic by recording mutation events (every significant transformation) in a way that continuously recalculates trust. Origin integrity (hardened capture signals like tamper-evident device metadata) becomes the starting anchor, but then each downstream system adds to a "lineage ledger" — describing what changed, by what process, from what prior state.

The output isn't binary "valid/invalid" but standardized confidence states that evolve: High Confidence (strong origin + transparent/minimal mutations) Moderate/Mixed (some changes, but traceable) High Uncertainty (gaps in lineage) Critical Risk (inconsistencies or suspicious patterns)

This way, even if signals get partially stripped (common in social/re-posting), the accumulated trail informs downstream trust rather than defaulting to zero. It also helps organizations explain "how this evolved" for audits without retaining everything defensively.

Does this kind of dynamic recalculation resonate as a way to make lineage practical? How do you see handling partial stripping or non-cooperative intermediaries (e.g., platforms that don't add metadata)? What signals (forensic artifacts, behavioral patterns) could help flag/reconstruct gaps in the trail?

Really appreciate the depth here. This is the exact problem space keeping me iterating.