Generative AI Trust Layer for Configurable Content Moderation
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Solution Overview
Problem
Existing AI platforms lack user-configurable trust layers for generative applications, leading to generic and inflexible content moderation policies that fail to address diverse user needs and regulatory compliance, resulting in mistrust and legal risks.
Innovation Solution
A user-configurable foundational trust layer for generative AI applications that allows customization of data privacy, content moderation, and regulatory compliance settings at organization, application, prompt, and model levels, with real-time moderation of sensitive and unsafe information using a large language model gateway and content moderation service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional AI platforms apply one-size-fits-all content moderation policies, then implementation simplicity is maintained, but user trust and regulatory compliance deteriorate
Solution Approach 1:
The content moderation system is segmented into multiple configurable layers including organization-level policies, application-level settings, prompt-level controls, and model-level parameters. This segmentation allows each layer to be independently configured and optimized, enabling both ease of implementation and high reliability through targeted customization rather than blanket policies.
Solution Approach 2:
The trust layer implements dynamic configurability where moderation policies can be adjusted in real-time based on user preferences, regulatory requirements, and contextual factors. Configuration parameters are transparent and modifiable, allowing the system to adapt to changing compliance needs and user expectations without requiring complete reimplementation.
2Device complexity
If generic content moderation is used, then device complexity is reduced, but adaptability to diverse user needs and regulatory environments deteriorates
Solution Approach 1:
The trust layer is designed as a universal framework that can operate across multiple organizational contexts, application types, and regulatory environments. Through standardized configuration interfaces and modular policy structures, the same core system adapts to diverse requirements without requiring separate implementations for each use case or jurisdiction.
Solution Approach 2:
The system achieves adaptability through configurable parameters at each layer of the moderation hierarchy. Organization-level settings control broad policy directions, while application, prompt, and model levels allow fine-tuning of specific behaviors. This parameter-driven approach enables the system to conform to different regulatory frameworks and user preferences without changing its fundamental architecture.
3Reliability
If transparent and configurable moderation settings are implemented, then user trust and compliance are enhanced, but configuration and management complexity increases
Solution Approach 1:
The configuration complexity is managed by introducing a hierarchical dimension to the trust layer architecture. Instead of a flat, monolithic configuration system, policies are organized across multiple levels (organization, application, prompt, model), allowing complex compliance requirements to be broken down into manageable, context-specific settings that can be configured independently and组合ed systematically.
Data Source
AI summary
A computer-implemented method is disclosed for predicting, based on a previous usage of a cloud-based computing resource by a number of users, a future usage of the cloud-based computing resource and then predicting, based on the predicted future usage, an anomaly event at the computing resource. The method also includes identifying a top contributing user that is responsible for the anomaly event and throttling an access of the top contributing user to the computing resource. The method further includes evaluating a speed of data requests received at the computing resource from the top contributing user after the throttling, and a utilization level of the computing resource. The method also includes dynamically adjusting the speed of data requests received at the computing resource, based on the evaluation of the utilization level of the computing resource, to maintain the utilization level of the computing resource within a predetermined target range.