The role of vector databases in enhancing Large Language Model (LLM) guardrails is crucial for ensuring accuracy, compliance, and reliability in AI-powered legal tech applications. Vector databases enable retrieval-augmented generation (RAG), allowing LLMs to retrieve real-time legal data from external sources before generating responses. This enhances knowledge validation, fact-checking, and compliance assurance, while mitigating prompt manipulation risks and enforcing domain-specific constraints. By integrating vector databases, legal AI systems can provide more accurate, compliant, and context-aware responses, reducing misinformation and fostering trust in AI-assisted legal workflows.