Description:The invention proposes a novel AI-powered cybersecurity framework that integrates machine learning models with uncertainty quantification to detect, evaluate, and respond to cyber threats in real time. The system uses a hierarchical confidence-guided orchestration engine that dynamically adapts mitigation strategies based on the confidence level of predictions generated by ensemble models. Components include telemetry ingestion, feature engineering, risk scoring, fusion of uncertainty-aware predictions, policy-based action routing, and a feedback loop for analyst trust calibration. The invention enables automated and explainable threat response across complex enterprise environments and is applicable to Zero Trust enforcement, insider threat detection, and SOC workflow automation. , C , C , C , C , Claims:We Claim
A computer-implemented system for predictive cybersecurity threat detection and response orchestration, comprising:

a data ingestion module configured to collect and normalize telemetry from diverse security sources;

a feature engineering module to extract structured threat indicators from raw telemetry;

a prediction engine using one or more machine learning models with uncertainty quantification;

a fusion layer that combines multiple prediction outputs weighted by confidence levels;

a contextual risk scoring module that generates a threat risk score with associated uncertainty;

a policy-based orchestration module that selects mitigation strategies based on the prediction confidence;

a feedback loop that captures analyst decisions to continuously recalibrate prediction trust.

The system of claim 1, wherein the uncertainty quantification includes both epistemic and aleatoric uncertainty, derived through deep ensembles and Monte Carlo dropout techniques.

The system of claim 1, wherein the fusion layer dynamically adjusts model weightings based on the respective confidence scores of each prediction model.

The system of claim 1, wherein the policy-based orchestration module applies the following logic based on predefined thresholds:

High-confidence risk predictions → automatic blocking;

Medium-confidence → escalation to a human analyst;

Low-confidence → passive monitoring or deferred action.

The system of claim 1, wherein the feedback loop captures SOC analyst inputs and uses them to refine trust scoring and prediction calibration using reinforcement or supervised learning.

The system of claim 1, wherein the system further includes an explainability interface that visualizes:

prediction certainty,

contributing features,

and model influence for each alert—enabling analysts to assess the credibility and reasoning behind decisions.