Runtime Governance Capabilities

Continuously evaluates and manages AI

EyeOn.AI continuously manages AI performance and responses in operational environments

EyeOn.AI

AI Runtime Governance Platform

Policy-Based Governance
Supports reliable and accountable AI service operations
EyeOn.AI helps AI generate consistent responses based on policy standards.
It evaluates AI behavior in real service environments in real time and detects responses or abnormal behaviors that deviate from policy standards at an early stage. It minimizes risks such as performance degradation, bias, and policy violations that may occur during operation, while helping AI operate according to consistent standards. Through this, it helps maintain a stable and trustworthy AI service environment.
Continuous Risk Monitoring
Tracks performance changes and potential risks of operational AI in real time
Continuously identifies and responds to performance changes and risks that may arise during operation.
It tracks changes in AI performance in real time in operational environments and detects early signs of anomalies such as drift, bias, and degraded response quality. Through this, it helps identify potential risks that may affect service quality in advance and maintain a stable AI operational environment.
Anomaly Detection & Response"
Detects anomalous behavior early and provides an automated response system
EyeOn.AI identifies abnormal response patterns of operational AI in real time.
It detects abnormal AI behavior or response anomalies early in operational environments and quickly identifies potential issues that may affect service quality. Through this, it helps minimize operational risks and maintain a stable AI service environment.

Runtime Governance Requirement

AI requires continuous control even after deployment

In operational environments, AI behavior may change differently from expectations depending on environmental changes.

AI Deployment Risk
Even after deployment, AI behavior
continues to change
Operational AI may experience changes in performance and response patterns depending on environmental changes.
Due to factors such as data changes, user input patterns, and model updates, AI response quality or behavior may change in ways that differ from expectations. These changes may lead to service quality degradation or policy violations.
Runtime Governance
Operational AI requires
continuous governance
AI requires ongoing evaluation and management even after deployment.
EyeOn.AI continuously evaluates AI responses and performance in operational environments to identify policy deviations or abnormal behavior at an early stage. Through this, it enables proactive responses to operational risks.

Runtime Governance Lifecycle

A governance process that manages the entire AI operation lifecycle

Continuously evaluates and controls the responses and performance of operational AI.

Runtime Governance Architecture

An AI governance architecture applicable to operational environments

EyeOn.AI is designed to apply policy-based governance to operational AI systems.

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