AI Security

Retrieval-Augmented Generation (RAG) is a technology designed to enhance the effectiveness of large language models (LLMs) by utilizing tailored data. RAG leverages specific data or documents as context for the LLM to improve effectiveness, support current information, or provide domain-specific expertise. In simple words, it allows large language models (LLMs) to answer questions about data they weren't trained on.

Data poisoning is a type of cyber-attack that aims to corrupt the training data of machine learning models. By introducing malicious or misleading data into the dataset, attackers can manipulate the behavior and outcome of these models.

Ensuring the security and privacy of data, preventing leaks, and thwarting malicious tampering with the model are critical aspects, much like with large language models (LLMs).

AI governance involves a comprehensive range of frameworks, policies, and best practices that serve as guardrails for the development and use of AI technologies. AI governance brings together stakeholders from all corners of an organization.