# Provenance Marking for AI-Designed Biology

As generative protein design becomes routine, labs and regulators push for embedded provenance in the designed molecule itself rather than in the file, so expect recurring watermarking and attribution methods validated in wet-lab assays, arguments over function cost and removability, and biosecurity screening pipelines starting to assume marked designs.

- Conviction: 27 / 100 (weakening)
- 7-day move: -7
- Horizon: Emerging (watchlist)
- Tracking since: 2026-10-01T00:00:00.000Z
- Last updated: 2026-10-12T14:07:13.413Z
- Canonical: https://polylog.news/ai/trends/biodesign-provenance-marking
- Publisher: Polylog
- Affected regions: Global

## Recent score history

- 2026-10-11: 28
- 2026-10-12: 27

## Recent evidence

- [confirms] DeepMind Watermarks AI-Designed Proteins Without Measurable Loss of Binding Function (2026-10-01): DeepMind reported watermarking AI-designed proteins with no measurable loss of binding function, with wet-lab tests against VEGF-A, the SARS-CoV-2 spike receptor-binding domain and PD-L1 showing marked designs matching unmarked ones on hit rate and affinity.