UACI Framework™: A Unified Architecture for Atomic, Inference-Time Provenance Across AI-Generated Modalities

The proliferation of generative artificial intelligence across text, image, audio, video, and code has outpaced the infrastructure required to verify the origin, integrity, and accountability of AI-generated content. This paper introduces the UACI Framework™ (Universal AI Content Identification System), a middleware architecture that resolves three structural gaps simultaneously: post-hoc versus inference-time provenance binding, single-modality versus cross-modal coverage, and the absence of encrypted per-asset provenance vaults in existing systems. UACI executes a single inference-time pipeline producing: (i) a BLAKE3-128 cryptographic content identifier (UACI Protocol); (ii) a transformation-resilient invisible watermark (StealthResist™, DCT spread-spectrum with BCH error correction); and (iii) an AES-256-GCM encrypted provenance capsule (P3 Capsule) sealed at generation time. The framework introduces four novelty claims absent from all current production platforms: atomic three-layer inference-time binding, code-modality watermarking, per-asset GDPR-compliant key-destroy erasure, and tri-jurisdictional regulatory pre-alignment across EU AI Act Article 52, NIST SP 800-53, and ISO/IEC 42001. The architecture is designed as a complementary layer to C2PA (ISO/IEC 22144), not a replacement. Preliminary benchmarks indicate median end-to-end embedding latency of 52 ms on CPU hardware, with image watermark recovery exceeding 99.5% after triple JPEG compression at quality factor 30.