Addressing Privacy and Security Challenges in AI for Enterprise
As the trend of integrating generative AI solutions into corporate operations gains momentum, numerous businesses are exploring the broader applications of Gen-AI beyond basic chatbots and automation tools. This transformative movement is merely in its infancy.
With the increasing interest and uptake of AI solutions, businesses are seeking customized implementations and widespread deployment of language models (LLMs), all while prioritizing compliance and security measures.
Despite the growing adoption of AI, several challenges persist, particularly concerning privacy and security. Establishing trust in LLM-generated responses and ensuring their accuracy are paramount. At Genie, we’re at the forefront of overcoming these hurdles through advanced RAG capabilities that tether our LLMs to enterprise datasets, enhancing search outcomes, and delivering responses that are robust, highly pertinent, and sourced. This fosters a more dependable, verifiable, secure, and ethical use of AI.
Adhering to enterprise-grade security and data privacy standards is pivotal in the deployment of AI solutions. This includes utilizing SaaS APIs within preferred cloud providers, opting for private deployments in Virtual Private Clouds (VPCs) across various hyperscale cloud platforms, or leveraging on-premise servers and infrastructure.
Genie offers:
- Private deployment of Genie´s LLMs with accompanying frameworks tailored for enterprise usage.
- Retrieval augmented generation to elevate the relevance and reliability of LLM-generated outcomes rooted in enterprise data.
- Customization of LLMs of varying model sizes, reducing fine-tuning and inference costs.
Recognizing the potential complexities businesses may encounter in adopting generative AI solutions, our collaborative team with consulting experts in Generative AI for enterprise has progressed beyond the experimental phase. We’ve reached a stage where companies can swiftly and efficiently implement these solutions securely.
