User Intent Profile for Access Control
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Solution Overview
Problem
Current natural language processing (NLP) systems lack the ability to effectively filter user interactions based on customer intention, disregarding latent preferences and intentions that govern user behavior, and fail to detect risky user intentions, leading to inadequate access control to sensitive information.
Innovation Solution
A method that builds a user profile by analyzing language choices and historical searches to detect shifts in user intent, using a natural language classifier to determine current user intent and update the profile, which is then used to refine access control and generate alerts for potential security threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional NLP systems are used to process natural language queries, then basic query response functionality is provided, but the systems cannot detect user intent shifts or implement nuanced access control based on user behavior
Solution Approach 1:
The system performs preliminary analysis by building user profiles that capture sentiment, emotion, and tone characteristics before processing actual queries. This preliminary characterization enables the system to detect intent shifts and enforce access control policies proactively rather than reactively, improving reliability without proportionally increasing complexity during query processing
Solution Approach 2:
The system continuously monitors user interactions and updates user profiles based on detected sentiment, emotion, and tone changes. This feedback loop allows the system to adapt access control decisions dynamically based on evolving user intent, ensuring reliable security while maintaining manageable complexity through iterative learning
2Measurement precision
If comprehensive user profile analysis is performed to detect user intent, then access control accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
User profiles containing sentiment, emotion, and tone characteristics are built and updated in advance during idle periods or background processing. This preliminary action stores processed user behavior data that can be quickly retrieved during query processing, enabling accurate intent detection without adding significant processing time to the main query execution path
Solution Approach 2:
The system applies access control filtering based on user intent at different levels of granularity. For routine queries, lighter-weight checks are performed using pre-computed profile data, while more intensive analysis is applied only when access control decisions require deeper inspection, optimizing the balance between accuracy and processing time
3Reliability
If the system filters documents based on user intent using cognitive analysis, then access control to sensitive information is enhanced, but the complexity of document processing increases
Solution Approach 1:
The system extracts and applies pre-computed user intent characteristics from user profiles to filter document access. By separating the complex cognitive analysis of user sentiment, emotion, and tone from the document processing itself, the system enhances security through intent-based filtering while keeping document processing complexity manageable through reusable profile data
Solution Approach 2:
User profiles serve as an intermediary layer between raw user interactions and document access decisions. The profiles capture complex user intent characteristics that mediate between user behavior and access control policies, simplifying the document processing complexity by pre-processing user characteristics before they interact with the document collection
Data Source
AI summary
Natural language processing is enhanced by linguistically extracting intelligence about a user. A history of user queries is analyzed by a natural language classifier to determine various user intents, and these intents are combined to form a user intent profile. The profile includes elements of sentiment, emotion and tone. The profile can be used in various ways including restricting access to documents in a collection, or refining a cognitive analysis of a query. For access restriction, a determination is made that the user intent is inconsistent with a document, and the user is denied access to the document. This determination involves a user intent score which is compared to a score of the document. For cognitive analysis, searching of reference documents is filtered by excluding documents based on the user intent. The searching includes a comparison of meta-data tags of the documents to the user intent.