LLM & GenAI Security

Protect Enterprises from misuse and exfiltration of Data through LLMs with Policy Based Access Control

Secure Policy-Driven AI Data Access

Protect sensitive data and enforce compliance with PlainID’s Policy-Based Access Control (PBAC). Whether through AI agents (NHIs) or GenAI-driven insights, access must be tied to the user’s identity, ensuring permissions align with roles and policies. With real-time authorization, PlainID safeguards proprietary data across Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs), maintaining Zero Trust and regulatory compliance.

Control AI-Generated Responses and AI Agents to Prevent Data Leaks and Ensure Compliance

PlainID’s policy points govern an input guardrail that authorizes the prompt and retrieval, and an output guardrail that filters the LLM response.

Secure AI Query Input

Enforce query authorization to ensure users can only ask questions within their authorized scope. Prevent unauthorized attempts to extract sensitive or restricted information.

Control Data Retrieval

Apply policy-based controls to restrict which data and documents AI can retrieve to generate insights from Retrieval-Augmented Generation (RAG) systems.

LLM Response Filtering and Masking

Enforce authorization at the response layer, ensuring that even after data is retrieved, AI-generated outputs are controlled to prevent expose of sensitve data.

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Whitepaper cover: Enhancing Data Security with Dynamic Authorization — A Guide to Mitigating Vulnerabilities in LLMs

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Feb 13, 2025

OWASP Top 10 for LLM and GenAI Security with PBAC

Generative AI (GenAI) is actively reshaping how enterprises extract business insights and drive operational efficiencies. At the heart of this transformation is Retrieval-Augmented Generation (RAG), a technique that powers Large Language Models (LLMs) with real-time data. However, as enterprises rush to implement GenAI, they face serious data security and access control challenges – especially in the context of new gaps emerging within a Zero Trust architecture.

Greg Berg

Dec 5, 2024

Data Access Authorization for GenAI and Cybersecurity

The rapid rise of Generative AI (GenAI), especially Retrieval-Augmented Generation (RAG) implementations, has redefined how enterprises extract value from their data. These systems, which augment Large Language Models (LLMs) with real-time data retrieval, fuel innovative applications in customer service, decision-making, and knowledge management. Thus, the adoption of RAG models raises significant questions about data security, access control, and compliance.

Kobi Gol

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