AI Governance & Security
Pain Points
Pain Points
Over-Authorization Risks Caused by Complex Tasks
In enterprise workflows, Agents often need to act on behalf of users across multiple business systems to perform substantive operations, frequently involving the reading and modification of sensitive data. Traditional control systems struggle to understand natural language intentions, failing to ensure high-risk complex tasks are executed securely and accurately.
Data Leakage Hidden Dangers of Intelligent Knowledge Bases (RAG)
Enterprise knowledge bases accumulate massive amounts of sensitive information such as salaries, contracts, and strategies. Traditional file-level permission control can no longer meet the fine-grained requirements of vector retrieval scenarios, easily leading to unauthorized reading and leakage of confidential data by AI during response generation.
Key Management Black Hole in Multi-Model Mixing
When enterprises invoke a mix of cloud-based commercial large models and local private models, developers often hardcode API Keys into the Agent's code. This not only introduces extreme key leakage risks but also fragments interface protocols, making invocation consumption difficult to track and audit.
Lack of Machine Identity Lifecycle Management
With the surging number of AI Agents acting as a "digital workforce," traditional static API Keys or long-term accounts easily lead to permission proliferation. Enterprises lack an automated full-lifecycle governance system—from registration and onboarding, permission circulation, to deregistration and offboarding—for machine identities, unlike the way they manage human employees.
Solution Overview
No.1 Panoramic Vision Large Model Gateway
Acts as the sole entry point for all large model invocations, acting as a reverse proxy and providing unified interface standards compatible with OpenAI. It can dynamically encapsulate and convert the enterprise's legacy RESTful APIs into standard MCP Tools for large models to invoke, implementing centralized authentication at the gateway layer.
No.2 Dynamic Perception Permission Engine
Breaks through traditional RBAC limitations by integrating ReBAC relational graphs and ABAC dynamic attributes (like time and position) to achieve automatic downward inheritance and real-time computation of permissions in complex organizational structures, ensuring absolute accuracy.
No.3 Zero Trust Data Guardrails
Implements vector chunk-level control and pragmatic filtering prior to RAG retrieval, physically blocking unauthorized reads. Simultaneously, equips the output end with real-time guardrails to intercept and desensitize Personally Identifiable Information (PII).
No.4 Intelligence-Driven Workflow Credential Circulation
Fully adopts the OAuth 2.1 token exchange mechanism to generate dual-identity tokens containing "User ID + Agent ID" for cross-node tasks. The system supports dynamic permission shrinking and downgrading based on current sub-task requirements to ensure global security.
No.1 Panoramic Vision Large Model Gateway
Acts as the sole entry point for all large model invocations, acting as a reverse proxy and providing unified interface standards compatible with OpenAI. It can dynamically encapsulate and convert the enterprise's legacy RESTful APIs into standard MCP Tools for large models to invoke, implementing centralized authentication at the gateway layer.
No.2 Dynamic Perception Permission Engine
Breaks through traditional RBAC limitations by integrating ReBAC relational graphs and ABAC dynamic attributes (like time and position) to achieve automatic downward inheritance and real-time computation of permissions in complex organizational structures, ensuring absolute accuracy.
No.3 Zero Trust Data Guardrails
Implements vector chunk-level control and pragmatic filtering prior to RAG retrieval, physically blocking unauthorized reads. Simultaneously, equips the output end with real-time guardrails to intercept and desensitize Personally Identifiable Information (PII).
Business Values
● Significantly Reduce Costs: Through high-frequency semantic caching at the gateway layer and fine-grained (department/Agent-based) quota scheduling, it effectively intercepts invalid redundant calls, substantially cutting large model Token inference costs and API procurement expenses.
● Exponential Efficiency Boost: Relational authorization (ReBAC) enables permissions to automatically follow organizational structure inheritance, significantly reducing tedious manual authorization and auditing workloads, while compressing the cycle for business personnel to gain data insights from hours to seconds.
● Zero-Vulnerability Security: The SPIFFE keyless architecture combined with millisecond-level network-wide blocking of anomalies builds an impeccable line of trust defense, eliminating massive regulatory fines triggered by unauthorized scraping and non-compliant data synchronization.
● 100% Holographic Compliance: Achieves 100% audit traceability and decision explainability across the entire lifecycle—from the user's initial intent input to complex node task distribution—clearing compliance blind spots for large-scale enterprise AI deployment.
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