AI Data Gateway
Enables secure, auditable access to RAG, vector, and enterprise databases in AI ecosystems via AI-powered tagging and IAM-integrated access control.
主な機能
AI Intelligent Data Tagging
Utilizes large models to automatically understand the content of unstructured documents (PDFs, Word documents, Markdown, web slices, and other RAG corpus) and structured data tables, identifying and tagging sensitive levels, business classifications, and compliance attributes. It supports custom tag systems and rule coverage, replacing high-cost manual tagging with automated classification and storage of massive data assets.
Unstructured RAG Document Access Control
Connects with knowledge bases, vector databases, and file storage, embedding a permission filtering layer in the RAG retrieval link. It dynamically decides "which documents can be retrieved and which fragments can be returned" based on document tags and user/Agent identities, preventing sensitive knowledge from being accidentally leaked to unauthorized users or AI Agents during RAG recall.
Fine-Grained Access Control for Structured Databases
Implements fine-grained access policies at the table, row, column, and field levels for databases. Supports dynamic policies based on data tags (e.g., "automatically mask columns marked as 'financially sensitive' for outsourced roles"), forming a linkage with data tagging capabilities to achieve tag-driven permission automation.
Linked and Unified Identity Authorization
Deeply integrates with the enterprise IAM platform, binding data access permissions to a unified identity system (users, roles, departments, AI Agents). Supports ABAC (Attribute-Based Access Control) policies to dynamically calculate authorization results based on user attributes, data tags, and access context, achieving fine-grained authorization linking "Identity × Data × Scenario."
Data Access Auditing and Risk Monitoring
Fully records data access behaviors (human users + AI Agents), including access subjects, accessed data objects, returned content summaries, and timestamps. Built-in risk identification rules provide real-time alerts for abnormal behaviors such as massive exports, unauthorized access, and high-frequency invocation of sensitive data. Supports audit report exports to meet compliance checks and post-event traceability needs.
AI Intelligent Data Tagging
Utilizes large models to automatically understand the content of unstructured documents (PDFs, Word documents, Markdown, web slices, and other RAG corpus) and structured data tables, identifying and tagging sensitive levels, business classifications, and compliance attributes. It supports custom tag systems and rule coverage, replacing high-cost manual tagging with automated classification and storage of massive data assets.
Unstructured RAG Document Access Control
Connects with knowledge bases, vector databases, and file storage, embedding a permission filtering layer in the RAG retrieval link. It dynamically decides "which documents can be retrieved and which fragments can be returned" based on document tags and user/Agent identities, preventing sensitive knowledge from being accidentally leaked to unauthorized users or AI Agents during RAG recall.
Fine-Grained Access Control for Structured Databases
Implements fine-grained access policies at the table, row, column, and field levels for databases. Supports dynamic policies based on data tags (e.g., "automatically mask columns marked as 'financially sensitive' for outsourced roles"), forming a linkage with data tagging capabilities to achieve tag-driven permission automation.
Linked and Unified Identity Authorization
Deeply integrates with the enterprise IAM platform, binding data access permissions to a unified identity system (users, roles, departments, AI Agents). Supports ABAC (Attribute-Based Access Control) policies to dynamically calculate authorization results based on user attributes, data tags, and access context, achieving fine-grained authorization linking "Identity × Data × Scenario."
AI Intelligent Data Tagging
Utilizes large models to automatically understand the content of unstructured documents (PDFs, Word documents, Markdown, web slices, and other RAG corpus) and structured data tables, identifying and tagging sensitive levels, business classifications, and compliance attributes. It supports custom tag systems and rule coverage, replacing high-cost manual tagging with automated classification and storage of massive data assets.
Unstructured RAG Document Access Control
Connects with knowledge bases, vector databases, and file storage, embedding a permission filtering layer in the RAG retrieval link. It dynamically decides "which documents can be retrieved and which fragments can be returned" based on document tags and user/Agent identities, preventing sensitive knowledge from being accidentally leaked to unauthorized users or AI Agents during RAG recall.
Fine-Grained Access Control for Structured Databases
Implements fine-grained access policies at the table, row, column, and field levels for databases. Supports dynamic policies based on data tags (e.g., "automatically mask columns marked as 'financially sensitive' for outsourced roles"), forming a linkage with data tagging capabilities to achieve tag-driven permission automation.
Linked and Unified Identity Authorization
Deeply integrates with the enterprise IAM platform, binding data access permissions to a unified identity system (users, roles, departments, AI Agents). Supports ABAC (Attribute-Based Access Control) policies to dynamically calculate authorization results based on user attributes, data tags, and access context, achieving fine-grained authorization linking "Identity × Data × Scenario."
Data Access Auditing and Risk Monitoring
Fully records data access behaviors (human users + AI Agents), including access subjects, accessed data objects, returned content summaries, and timestamps. Built-in risk identification rules provide real-time alerts for abnormal behaviors such as massive exports, unauthorized access, and high-frequency invocation of sensitive data. Supports audit report exports to meet compliance checks and post-event traceability needs.
製品技術の特徴
●Traditional data governance relies on manual configuration of classification rules and cannot understand document semantics. The platform uses large models as its tagging engine, capable of truly "reading" document content and automatically identifying sensitive information, extending data governance from structured metadata to the unstructured content layer.
●Adopts multiple AI tagging policies and manual review mechanisms. It first matches intelligent rules to define data asset tags according to industry standards, then optimizes the enterprise RAG knowledge base retrieval and recall strategy to adapt to enterprise business data, and finally relies on an exclusive AI tagging engine combined with F1 scores and manual rating to improve tagging accuracy.
●Tagging results serve directly as input for permission policies. When data tags change, access permissions automatically adjust without secondary manual configuration, achieving real-time synchronization between data security policies and data content status.
●While IAM manages human identities and authentication, and PAM controls privileged accounts, the AI Data Governance platform manages access and content security at the data layer. The three collaborate to build a complete zero-trust data security system from "who the person is" to "what data they can view."
ユースケース
ユースケース
Access Control for Sensitive Content in AI Knowledge Bases
Enterprises import sensitive materials into RAG knowledge bases for LLM invocation, risking recall by unauthorized users or AI Agents due to a lack of content-level permission control. The platform ensures every knowledge retrieval remains within authorized limits through AI tagging and RAG link permission filtering.
Differentiated Data Authorization for Multi-Role AI Applications
A single AI application serves internal employees, outsourced personnel, and external partners, with different knowledge base scopes and data fields accessible to each role. Combining IAM role systems with data tag policies achieves differentiated data views from a single portal without maintaining separate data copies for each role.
Compliant Access and Desensitization of Structured Databases
Business databases contain highly sensitive fields like personal information and financial data, requiring compliance with regulations like GDPR. The platform automatically applies desensitization policies to sensitive fields, combined with row-level permission filtering, satisfying the principle of least privilege without affecting business query efficiency.
Auditing and Control of AI Agent Data Access
When executing tasks on behalf of users, AI Agents frequently invoke data APIs. The platform creates separate profiles for Agent identities, records their data access behaviors in full, and issues real-time alerts for abnormal access, meeting AI compliance audit requirements.
ビジネス上のメリット
● Making "Data Usage" Safer for AI Applications: Adds identity verification and content-level permission filtering to every data invocation, ensuring data flow within the AI ecosystem remains within authorized boundaries and fundamentally preventing sensitive data leakage.
● Eliminating Data Governance Blind Spots: Extends governance scope from structured databases to unstructured data like RAG documents, establishing a unified tag and permission system across data types.
● Tag-Driven Policy Automation: Transforms tedious data classification and permission configuration from manual operations to automated processes, significantly reducing maintenance burdens and improving execution consistency.
● Meeting Compliance Requirements: Full access logs, abnormal behavior alerts, and compliance reports support industry data security supervision and internal/external audit requirements, making data usage visible, controllable, and traceable.
● Building a Data Security Foundation: Links with IAM, PAM, and NHI platforms to integrate data access control into a unified zero-trust security architecture, providing a trusted data security foundation for enterprise AI transformation.
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