Unleash the Power of Galileo XAI for Explainable AI
Table of Contents:
- Introduction
- Overview of Galileo XAI
- Galileo XAI Features
- Explainable AI with Fujitsu Deep Tensor
- Integration with Lincurius
- The Problem with Manual Analysis
- The Deep Tensor Process
- Training the Model
- Validating the Model
- Cross-Validation
- Evaluating Explainable AI
- The Explainable Table
- Visualizing Results
- Using Deep Tensor on the Complete Data Set
- Saving Time and Resources with Galileo XAI
- Conclusion
- Frequently Asked Questions (FAQ)
Overview of Galileo XAI
Galileo XAI is a graph platform for data analysis that utilizes artificial intelligence, network science, and other innovative approaches to make information and knowledge more understandable and reliable. It is designed to support decision-making, define new strategies, automate processes, and Create new business opportunities.
Galileo XAI Features
Explainable AI with Fujitsu Deep Tensor
Galileo XAI incorporates a feature called Explainable AI, which is implemented with Fujitsu Deep Tensor. This technology allows users to Visualize and analyze each alert as a graph, making it easier to identify Patterns of suspicious or abnormal activity. By changing the layout of the graph, users can gain a better understanding of potential cyber attacks.
Integration with Lincurius
Galileo XAI also integrates with Lincurius, a tool that helps analysts investigate alerts. By using Lincurius, analysts can assign patterns to themselves, visualize and investigate graphs, and determine whether the alerts are significant or not. This integration helps streamline the review and completion of activities, reducing the time and resources required for manual analysis.
The Problem with Manual Analysis
Manual analysis of alerts is a time-consuming and resource-intensive task. It requires a team of analysts to investigate each alert and confirm or dismiss them. This process can be expensive and inefficient. Galileo XAI aims to address this problem by leveraging artificial intelligence to automate and expedite the analysis process.
The Deep Tensor Process
The Deep Tensor process is a crucial component of Galileo XAI. It involves training the model, validating the model, and performing cross-validation to ensure the accuracy and reliability of the AI system.
Training the Model
The training phase of Deep Tensor involves feeding the model with Relevant data to teach it how to recognize patterns of cyber attacks. This phase requires sufficient data and can be retrained periodically to incorporate new information and improve accuracy.
Validating the Model
Once the model is trained, it undergoes a validation phase where it is tested against a different subset of data. This process helps verify the model's ability to accurately detect and classify cyber attacks.
Cross-Validation
Cross-validation is performed to identify overfitting of the training data. By using a separate subset of data, Galileo XAI ensures that the model's performance is not biased or skewed by the training data, ensuring the system can generalize well to new and unseen cases.
Evaluating Explainable AI
Galileo XAI provides an Explainable Table that allows users to investigate the results produced by the tensor. This table enables users to select subgraphs and determine whether they represent attacks or not. By using various visualization algorithms and color-coded palettes, users can assess the significance of different elements in the decision-making process.
Using Deep Tensor on the Complete Data Set
Once the model is trained and validated, it can be applied to the complete data set. Galileo XAI calculates scores and provides an expandable view of all alerts that have not yet been classified by humans. This helps analysts prioritize their workload and focus on cases that require further investigation.
Saving Time and Resources with Galileo XAI
By leveraging Explainable AI and Deep Tensor, Galileo XAI significantly reduces the time and resources required for manual analysis. Analysts can rely on the AI system to classify alerts, allowing them to focus on more advanced analysis and decision-making tasks. This automation and efficiency ultimately result in cost savings and improved productivity.
Conclusion
Galileo XAI is a powerful platform that combines artificial intelligence, graph analysis, and explainability to enhance data analysis and decision-making. By using Explainable AI with Fujitsu Deep Tensor, Galileo XAI provides users with a clear visualization of alerts and patterns, empowering them to make informed decisions and take swift action against potential cyber threats. With its integration with Lincurius and its ability to automate the analysis process, Galileo XAI offers significant time and resource savings, making it a valuable tool for organizations seeking to enhance their cybersecurity capabilities.
Highlights
- Galileo XAI is a graph platform for data analysis Based on artificial intelligence and network science.
- Explainable AI with Fujitsu Deep Tensor enables users to visualize and analyze alerts as graphs.
- Integration with Lincurius streamlines the review and investigation of alerts.
- The Deep Tensor process involves training, validating, and cross-validating the AI model.
- Galileo XAI saves time and resources by automating the analysis of alerts.
- The Explainable Table helps users evaluate the results produced by the tensor.
- Galileo XAI enhances data analysis, decision-making, and cybersecurity capabilities.
Frequently Asked Questions (FAQ)
Q: Can Galileo XAI detect all types of cyber attacks?
A: Galileo XAI is designed to detect patterns of suspicious or abnormal activity, including DOS-Type cyber attacks. However, the effectiveness of detection depends on the availability and quality of data used for training the model.
Q: Can Galileo XAI be customized to fit specific business needs?
A: Yes, Galileo XAI allows users to define rules and specify business logic in a declarative manner. This flexibility allows customization to suit specific business requirements and analysis goals.
Q: How long does it take to train the Deep Tensor model?
A: The training time for the Deep Tensor model depends on factors such as the size of the data set and the complexity of the patterns being trained. However, Galileo XAI provides recommended configurations to expedite the training process.
Q: Can Galileo XAI be used in conjunction with other cybersecurity tools?
A: Yes, Galileo XAI can integrate with other tools, such as Lincurius, to enhance cybersecurity capabilities. This integration allows for more comprehensive analysis and investigation of alerts.
Q: Does Galileo XAI eliminate the need for human analysts?
A: No, Galileo XAI is designed to assist human analysts by automating certain tasks and reducing manual analysis efforts. The expertise and decision-making capabilities of human analysts are still crucial for evaluating and responding to potential cyber threats.
