Intent-Based Policy Control for Enterprise Data Traffic
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Solution Overview
Problem
Existing methods for managing enterprise data risks related to AI and ML network services are inadequate in determining user interaction intent and performing fine-grained control operations, leading to ineffective prevention or overly restrictive access blocking.
Innovation Solution
An Enterprise Data Management and Monitoring system that includes a network communication interface, processing unit, and memory unit with engines for intent detection and policy control, which analyze network traffic context and content to generate an intent indexer, allowing for context-aware interaction management and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full scale access blocking solutions are used to manage enterprise data risks, then data protection is improved, but user productivity deteriorates due to overly restrictive access blocking
Solution Approach 1:
The patent applies local quality by implementing fine-grained control that differentiates between various users, services, and data types. Instead of uniform blocking, the system applies specific policies to specific intents (e.g., allowing analytics services while blocking data exfiltration attempts), enabling selective data protection that preserves legitimate productivity while blocking actual threats.
Solution Approach 2:
The system changes the parameter of control granularity from coarse (full-scale blocking) to fine (intent-level control). By analyzing intent indexers that categorize user interactions into specific intents, the system dynamically adjusts access decisions based on the specific parameters of each interaction, allowing legitimate operations while blocking malicious ones.
2Reliability
If coarse-grained access blocking is implemented, then data security is improved, but control precision deteriorates due to inability to distinguish legitimate from malicious use
Solution Approach 1:
The patent segments the broad category of network access into distinct intent categories through the intent indexer. By breaking down user interactions into specific intents (e.g., data access, data modification, analytics, exfiltration), the system achieves precise control over each segment, allowing legitimate operations while blocking malicious ones with high measurement precision.
Solution Approach 2:
The system replaces mechanical blocking mechanisms with intelligent intent analysis. Instead of simple allow/deny rules, the system uses machine learning models to analyze interaction patterns, context, and behavior to determine intent, substituting crude mechanical control with sophisticated analytical control that achieves high precision.
3Ease of manufacture
If traditional user training and access control methods are used, then implementation simplicity is maintained, but effectiveness deteriorates due to insufficient handling of AI and ML network services
Solution Approach 1:
The patent introduces an intermediary intent analysis layer between users and network services. This intermediary system (comprising intent detection engines, machine learning models, and policy decision points) mediates all interactions, analyzing intent before allowing access. This maintains implementation simplicity by providing a unified control point while dramatically improving effectiveness through intelligent analysis of AI and ML service interactions.
Data Source
AI summary
A system may include a network communication interface configured to receive network traffic, a processing unit, and a memory unit storing instructions for various engines that are executable by the processing unit. The various engines include a data policy enforcement engine that includes an intent detection engine configured to receive context data and content data relating to the network traffic and generate an intent indexer for the network traffic based on the context data and the content data. The intent indexer documents an operation request and a subject of the operation request that are connected to the network traffic. The various engines also include an intent policy control engine configured to administer aspects of the network traffic based on the intent indexer and the context data.