# AI-Based RPA's Work Automation Operation to Respond to
## 4 Experiments and Results
### Results and Performance Analysis
#### 4.2.2 Qualitative Performance Evaluation of the Proposed System  
The integration of AI-based cyber threat prediction and analysis in our system represents a significant advancement over traditional reactive incident response methods. Unlike conventional approaches that rely on post-incident mitigation, our system facilitates proactive defense, addressing the growing complexity and frequency of cyber threats. By providing real-time prediction, analysis, and dissemination of attack patterns, the system minimizes response times and enhances overall resilience. As illustrated in Figure 17, the system captures and integrates cyber threat logs from multiple security platforms in real-time, storing them in a unified database. This consolidated approach facilitates efficient analysis and prediction, significantly improving data processing speed, accuracy, and automation. The use of standardized log for mats addresses inconsistency issues that have been a major limitation in traditional cybersecurity approaches. Previously, inconsistent data formats from multiple security solutions hindered log collection and analysis, causing delays, and inaccuracies, and reduced the effectiveness of real-time responses. Each security solution operated in isolation without standardized data integration, limiting overall efficiency. Our system standardizes and stores log data in a consistent format, creating a robust dataset for efficient analysis and addressing issues like prolonged processing times and high error rates. This standardization provides a reliable foundation for machine learning models to effectively learn from diverse security events. The system consolidates data into an integrated database, accessible by authorized institutions in real-time via a Flask server. This approach reduces the need for manual file exchanges, which are prone to errors and delays, and enhances data accessibility, allowing seamless collaboration between organizations. As a result, institutions can share critical threat information instantly, enabling rapid and coordinated responses to cyber threats. This ability for cross-institution collaboration represents a significant advancement in coordinated cyber defense, particularly given the increasing sophistication of cyberattacks targeting multiple organizations. By leveraging AI for cyber threat prediction and analysis, the proposed system enables proactive responses that go beyond traditional reactive methods. Machine learning algorithms for attack pattern recognition and predictive analysis empower the system to detect potential threats at an early stage, allowing for a timely response and mitigation. Real-time data sharing and predictive analysis help institutions anticipate future attack patterns, enhancing the robustness and scalability of defenses. The integrated database supports real-time attack prediction and AI model training, advancing security technologies and fostering a proactive approach to cybersecurity. Real-time data normalization, feature encoding, and preprocessing ensure high-quality, consistent data for AI models, leading to higher prediction accuracy and reduced false positives. In conclusion, the proposed system represents a substantial evolution in cybersecurity defense, offering a scalable, automated, and proactive solution to address advanced cyber threats. By leveraging real-time data processing, AI-based analysis, and institutional collaboration, the system provides a robust framework that addresses the limitations of legacy systems and prepares organizations for future cybersecurity challenges. The experimental results demonstrate the superior performance of this system compared to traditional manual systems, particularly in terms of log processing speed, prediction accuracy, error reduction, and operational efficiency. This capability allows for the prompt analysis and prediction of attack patterns.