The rise of Embeddings as a Service (EaaS) has brought a new challenge: model theft. Attackers can essentially clone these powerful AI models, costing providers their intellectual property and potentially hindering innovation. Existing watermarking methods, designed to protect EaaS by subtly altering the embeddings, have proven vulnerable to sophisticated removal techniques. Imagine a thief skillfully erasing a barely visible mark from a stolen artwork. That’s what’s happening in the digital realm with stolen AI models. However, researchers have developed a new approach called ESpeW (Embedding-Specific Watermark). This technique inserts unique, almost undetectable watermarks into *each individual embedding*. Think of it like giving every pixel in a digital image its own unique, invisible signature. This makes identifying and removing the watermark incredibly difficult. Tests show ESpeW is highly effective, even against aggressive removal attacks, while preserving the quality of the embeddings for downstream applications. This breakthrough could help secure the future of EaaS, encouraging more providers to offer their valuable models and fostering a thriving AI ecosystem. However, the efficiency of ESpeW faces challenges with the increasing size of LLMs and their embeddings. Future research aims to refine ESpeW, balancing its robustness with the computational demands of ever-larger models, paving the way for more secure and accessible AI in the years to come.