Washington HB 1170 (AI content provenance)

Binding law — not yet in force or stayed

Embed tamper-resistant provenance data in AI-generated images, video and audio (Washington HB 1170)

Laws 2026, ch. 167 (E2SHB 1170), Sec. 2(1) · official text · Enacted, not yet applying: applies from 1 Feb 2027 · Washington (US-WA)

From 2027-02-01, a covered provider (a person or entity that creates, codes or otherwise produces a generative AI system with over 1,000,000 monthly users that is publicly accessible in Washington to consumers for personal use) must, to the extent commercially and technically reasonable, include provenance data in video, image or audio content its system creates or materially alters, so a user can assess whether the system created or materially altered it, and make the data difficult to remove or tamper with; a commonly supported standard such as the C2PA specification is compliant (Laws 2026, ch. 167, Secs. 1-2). Detect media-generation code that writes or serves output with no C2PA manifest or watermark.

Who it applies to

• Duty falls on: provider
• Covered providers: generative AI systems (generating images, audio or video) with over 1,000,000 monthly users, publicly accessible in Washington to consumers for personal use. Applies from 2027-02-01, to the extent commercially and technically reasonable. Excludes governments, business-to-business distribution, exclusively video-game or interactive products, and upscaling, noise-reduction or compression systems. How monthly users are counted, and whether an application built on another company's model is itself a covered provider, need human determination.
• Not covered:
  • State, local and tribal governments are not covered providers (Sec. 1(2))
  • Business-to-business uses, sales, licensing or distribution of generative AI systems (Sec. 3(2))
  • Products, services, websites or applications that provide exclusively video game or interactive experiences (Sec. 5(1))
  • Systems used solely for upscaling, noise reduction or compression (Sec. 5(2))
  • Minor modifications (brightness, contrast or color, sharpening, saturating, filters, resizing, scaling, cropping, format conversion, resampling, denoising, removing background noise in audio) do not 'materially alter' content (Sec. 2(4))
  • No duty to put information about an identified or reasonably identifiable individual into the provenance data (Sec. 2(3)), or to disclose trade secrets or confidential design information (Sec. 3(1))
• Whether it applies depends on facts outside the code; a person has to decide.

image = pipe(prompt).images[0]   # StableDiffusionPipeline
image.save(out_path)

image = pipe(prompt).images[0]
content_id = uuid.uuid4()
bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
enc = WatermarkEncoder()
enc.set_watermark('bytes', content_id.bytes[:4])   # 32-bit id, detectable later
cv2.imwrite(tmp_path, enc.encode(bgr, 'dwtDct'))
sign_c2pa(tmp_path, out_path, content_id=content_id)   # our helper around c2pa.Builder.sign

Standards that recommend the same control

• Businesses providing AI should develop and, as needed, implement technology to identify AI-generated content, such as watermarks (Japan, soft law) (Japan AI appropriateness guideline (AI Promotion Act Art. 13) · 人工知能関連技術の研究開発及び活用の適正性確保に関する指針 2(3) 十分な安全性の確保 (safety, including technology to identify AI-generated content))
• GenAI systems should employ content-provenance methods and measure their effectiveness (NIST GenAI Profile) (NIST GenAI Profile (AI 600-1) · NIST AI 600-1, Sec. 2 risk list (Information Integrity))

Rule id wa-hb1170.genai-media-provenance-data · review status: primary source derived