Machine Learning Asset Risk Profiling for Holistic Enterprise Controls
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional enterprise systems lack a holistic view of enterprise resources, failing to provide optimal mechanisms for strategic planning and risk-based decision making, particularly in protecting against potential risks and compromises of confidentiality, integrity, and availability.
Innovation Solution
A computing platform uses machine learning classifiers to determine asset risk profiles (ARPs) based on risk factors, assigning weights and impact ranks, and tailoring risk control schemes accordingly to manage enterprise resources effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional enterprise systems focus on resource availability to minimize downtime, then system availability is improved, but holistic risk assessment capability deteriorates
Solution Approach 1:
The system segments risk assessment into multiple dimensions including confidentiality, integrity, and availability (CIA triad), allowing comprehensive risk evaluation while maintaining focus on availability. Each risk factor is independently assessed and weighted, enabling holistic view without compromising availability optimization.
Solution Approach 2:
An artificial intelligence intermediary processes risk information from multiple sources and generates risk profiles that bridge the gap between availability-focused operations and comprehensive risk assessment. The AI system synthesizes diverse risk data into actionable insights without disrupting availability-critical operations.
2Measurement precision
If comprehensive risk information is collected from multiple sources, then risk assessment accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The system employs self-learning artificial intelligence that automatically processes, analyzes, and weights risk information from multiple sources without requiring manual configuration. The AI model autonomously adapts to new data patterns, reducing the operational complexity of managing comprehensive risk data collection.
Solution Approach 2:
The system dynamically adjusts risk factor weights and assessment parameters based on learned patterns from historical data. This adaptability allows accurate risk assessment across varying conditions without increasing system complexity, as the AI automatically optimizes parameters based on incoming information.
3Reliability
If risk control schemes are customized based on risk levels, then protection effectiveness is improved, but implementation complexity deteriorates
Solution Approach 1:
The system implements dynamic risk control schemes that automatically adjust based on real-time risk assessments. Control measures are continuously optimized according to changing risk profiles, enabling effective protection without manual intervention. The AI-driven automation manages the complexity of customized control implementation.
Solution Approach 2:
The system incorporates feedback loops where risk assessment results automatically trigger appropriate control measures, and the effectiveness of these controls is continuously monitored. This closed-loop system ensures effective protection while automating the complexity of implementing customized risk responses.
Data Source
AI summary
Aspects of the disclosure relate to using machine learning for asset risk profiling. A computing platform may receive risk information from an enterprise system. The computing platform may determine a ranking criterion for a plurality of risk factors. The computing platform may use a machine learning classifier to determine a weight corresponding to each risk factor and determine an asset risk profile (ARP) score for the enterprise system. Based on the ARP score, the computing platform may determine a risk control scheme and provide the risk control scheme to an enterprise control server.