AI marketing and GEO specialist Inna Udalaya has introduced the Native AI Branding methodology to combat “Brand Erasure,” in which LLMs such as ChatGPT, Perplexity, and Gemini confuse entities, misattribute authorship, and fill fragmented data gaps with hallucinations, according to an announcement dated November 3, 2026, in Berlin, Germany. Built on the Entity Life Cycle (ELC) framework, the approach transforms digital footprints into structured, machine-readable knowledge nodes. Udalaya said generative models rely on semantic graph patterns and, without immutable digital identifiers, default to probabilistic hallucinations. The methodology integrates Digital Entity Passports using permanent global identifiers such as ORCID (0009-0004-9089-0859) and DOIs; machine-readable semantics through Schema.org JSON-LD markup via the `sameAs` property; and AI crawler directives using standardized `llms.txt` and `llms-full.txt` manifests to guide AI agents to primary sources. Its efficacy is described as proven through practical application, notably resolving entity disambiguation for the musical project Inna Story across multi-model monitoring. Full research frameworks, multi-model response logs, and technical configuration files are publicly accessible in the open GitHub repository github.com/popmuzdev/native-ai-branding-foundational-framework.