Closed-Loop Cybersecurity Control Prioritization From Threat Reports
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Solution Overview
Problem
Conventional cybersecurity systems face inefficiencies due to data overload, inability to process unstructured threat intelligence, static analysis, and fragmented frameworks, leading to wasted resources, delayed responses, and inaccurate risk assessments.
Innovation Solution
A dynamic, closed-loop system that integrates Natural Language Processing (NLP) for unstructured data ingestion, temporal scoring, and a hybrid MCDA-ML prioritization engine to automate threat analysis, providing explainable and adaptive resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cybersecurity systems process all security alerts manually, then comprehensive threat analysis is achieved, but alert fatigue increases and critical threats are missed
Solution Approach 1:
The system automatically prioritizes security alerts by analyzing historical data and predicting future threats, enabling the system to serve itself rather than relying entirely on human analysts to process all alerts manually
Solution Approach 2:
The system uses feedback from historical security data and actual threat outcomes to continuously improve its prioritization algorithm, creating a closed-loop system that learns from past performance to enhance future alert triage accuracy
2Loss of information
If the system analyzes all vulnerability data from public sources, then comprehensive threat intelligence is obtained, but processing time increases and response speed decreases
Solution Approach 1:
The system performs preliminary processing of vulnerability data by pre-analyzing historical patterns and pre-ranking potential threats based on predicted severity, so that when new alerts arrive, the system can quickly reference pre-computed priorities rather than analyzing everything from scratch
Solution Approach 2:
The system extracts and focuses only on the most critical threat indicators from the vast amount of vulnerability data, filtering out low-priority items and concentrating resources on analyzing and responding to high-severity threats first
3Ease of manufacture
If the system uses static risk frameworks, then implementation is simple, but adaptability to evolving threats is insufficient
Solution Approach 1:
The system transitions from static risk frameworks to dynamic prioritization by continuously updating threat models based on historical data and actual outcomes, allowing the system to adapt its risk assessment criteria in real-time based on evolving threat landscapes
Solution Approach 2:
The system changes its risk evaluation parameters dynamically by adjusting weights and criteria based on learned patterns from historical security data, enabling adaptation to new threat vectors while maintaining a structured framework approach
4Loss of information
If the system processes unstructured threat reports, then comprehensive threat intelligence is achieved, but data processing complexity increases
Solution Approach 1:
The system introduces an intermediary processing layer that automatically parses, normalizes, and structures unstructured threat reports into standardized formats, simplifying the complexity of handling diverse data sources while preserving comprehensive threat intelligence
Data Source
AI summary
A computer-implemented system and method for dynamic, explainable, and adaptive prioritization of cybersecurity controls is disclosed. The system ingests unstructured threat reports and employs a natural language processing (NLP) module to automatically extract adversary tactics, techniques, and procedures (TTPs). A scoring module applies a mathematical time-decay function to the extracted intelligence. A novel hybrid prioritization engine provides explainability-by-design by computationally integrating these objective, data-driven scores with organization-specific context within a transparent multi-criteria decision analysis (MCDA) model. Critically, the system establishes a self-optimizing closed feedback loop; it receives real-world control effectiveness metrics from the operational environment and uses this data as new ground-truth labels to continuously and automatically retrain internal machine learning models. This adaptive mechanism improves the computer's own predictive accuracy and resource allocation efficiency over time, representing a tangible technical improvement.