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.

Simply put, AI factory are environments designed specifically to train, deploy and operate AI models at scale. They're different from traditional data centers in that they’re optimized for continuous AI workloads by combining accelerated compute, data pipelines, orchestration and security all in a unified system. 

Proprietary AI implementations are common in industries where protecting sensitive data is critical, or even the law. These industries include finance, healthcare, telecommunications, and government. 

At the most basic level, secure key release means encryption keys are only given out when certain security checks are passed. Instead of handing out keys just because someone is logged in or on the right network, the system first checks whether the application is running in a trusted and approved way. If those checks fail, the key isn’t shared. This makes it harder for attackers or misconfigured systems to access sensitive data.