Retrieval-augmented generation (RAG) is a hybrid AI approach that combines retrieval-based methods with generative models to improve the quality and accuracy of generated content. This approach benefits tasks requiring factual accuracy and natural language generation, such as question-answering, summarization, or generating content based on specific knowledge. How RAG Works: RAG integrates two core components: Retrieval… Continue reading Retrieval-Augmented Generation (RAG)
Tag: EthicalAI
Cybersecurity Risks in AI Lifecycle
Aligning AI risks with LLMOps stages involves identifying where specific risks are most likely to arise and ensuring that each phase has appropriate controls to mitigate these risks. AI risks can indeed occur in multiple stages or phases, as many risks are pervasive and can impact different aspects of the AI lifecycle. 1. Model Development… Continue reading Cybersecurity Risks in AI Lifecycle
AI Cybersecurity Risks & Controls
Cybersecurity risks have become increasingly prominent in AI. Some of them are data poisoning, personal and confidential information in data, prompt injection, lack of data transparency, unreliable source attribution, and unexplainable outputs. These issues can compromise the integrity, security, and reliability of AI systems. Data Poisoning 1. Threat: Data Poisoning in AI Description: Data poisoning… Continue reading AI Cybersecurity Risks & Controls