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Ready to take your AI strategies to the next level? Enterprises are turning to retrieval augmented generation (RAG) frameworks to scale up large language models (LLMs) and meet the increasing demand for integration while ensuring contextual security.
Ensuring Contextual Security for RAGs
Traditional access control methods fall short when it comes to protecting RAG frameworks. Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) have limitations in adapting to contextual requests and maintaining scalability. That’s where Context-Based Access Control (CBAC) comes in.
Developed by Lasso Security, CBAC offers a dynamic approach to evaluating access requests to LLMs. By focusing on contextual intelligence, CBAC ensures comprehensive security, prevents unauthorized access, and maintains high-security standards.
Contextual intelligence plays a crucial role in safeguarding sensitive information from LLMs, providing a much-needed layer of security.
Exploring Retrieval-Augmented Generation (RAG)
Defined as a way to enhance large language models, Retrieval-Augmented Generation (RAG) allows access to enterprise data for processing by LLMs. Gartner highlights the practicality of RAG in overcoming the limitations of traditional LLMs.
Lasso Security’s Approach with CBAC and RAG
Lasso Security’s CBAC seamlessly integrates with Active Directory and their gen AI security suite, providing protection for various AI-based applications in production environments. By monitoring interactions and identifying anomalies, CBAC ensures a secure environment.
Utilizing supervised machine learning algorithms, CBAC continuously learns from user behavior patterns to enforce access control policies effectively.
Addressing Security Challenges with CBAC
As RAG adoption grows, the need for dynamic access control becomes essential. CBAC tackles the permissions hurdles faced by LLMs, offering contextual insights to enhance security measures.
With contextual intelligence at its core, CBAC revolutionizes how organizations protect and scale their AI strategies without compromising performance.
