Project 1: An Aged-care Reporting Co-pilot Using LLM and Anomaly Detection
Project Overview
This project is designed to assist data analysts in aged-care organizations by providing an AI-driven tool that enhances data reporting and anomaly detection capabilities, thereby streamlining compliance and operational efficiency.
Project Objectives
The primary goal is to develop an AI-assisted reporting tool that utilizes Large Language Models (LLMs) and anomaly detection to scrutinize financial and operational data of aged-care service providers. This tool aims to enhance data analysis precision and efficiency, assisting in identifying discrepancies and valuable insights that support compliance and reporting standards.
Project Approach
1. Data Collection and Preparation: Aggregate comprehensive datasets encompassing financial transactions, operational metrics, and other pertinent data from the aged-care service provider. Cleanse and structure the data for analytical processing.
2. Integration of LLMs: Implement LLMs to interpret and analyze textual and numerical data. These models will be adapted to recognize and process industry-specific jargon and contexts, thus refining their analytical outputs.
3. Anomaly Detection: Apply sophisticated anomaly detection algorithms designed to work in conjunction with LLMs. These algorithms will detect outliers and unusual data patterns that could suggest operational mishaps, potential fraud, or areas needing attention.
4. Interactive Reporting Interface: Develop an intuitive interface that enables data analysts to seamlessly interact with the AI, access generated reports, and delve into detailed analyses of detected anomalies.
5. Iterative Feedback Loop: Incorporate mechanisms for analysts to provide feedback on the AI's performance and outputs, which will facilitate ongoing refinement of the AI models.
Project Delivery
1. Increased efficiency and accuracy in data analysis and reporting tasks.
2. Proactive identification and management of irregularities.
3. Enhanced compliance with regulatory obligations through precise and comprehensive reporting.
Project 2: Developing Safeguard Mechanisms for CareGPT
Project Overview
A sister project to the Reporting Co-pilot, this initiative focuses on the critical aspect of cybersecurity, developing robust protective measures for the CareGPT system to ensure the security and integrity of sensitive aged-care data.
Project Objectives
This project aims to fortify the security of CareGPT, an AI system handling sensitive aged-care management data. The focus is on implementing cutting-edge cybersecurity measures to safeguard data privacy and integrity, ensuring that CareGPT adheres to the highest security standards.
Project Approach
1. Cybersecurity Research: Conduct an exhaustive survey of the latest cybersecurity techniques, especially those pertinent to LLMs and AI applications in healthcare.
2. Custom Security Solutions: From the research findings, develop bespoke security strategies tailored to the specific needs of CareGPT. This may include implementing robust encryption methods, secure data storage technologies, and sophisticated user authentication protocols.
3. Implementation of Security Protocols: Integrate these security measures into the CareGPT system to protect against data breaches and unauthorized access.
4. Continuous Security Evaluations: Establish protocols for periodic security audits to ensure the effectiveness of the security measures and adapt to new cyber threats.
5. Regulatory Compliance and Documentation: Ensure all security measures comply with industry standards and legal requirements, maintaining thorough documentation and transparent reporting processes for audit and compliance purposes.
Project Delivery
1. Robust security framework ensuring the protection of sensitive data.
2. Compliance with data protection laws, enhancing the system's credibility and reliability.
3. Establishment of CareGPT as a secure and trusted tool within the aged-care industry.
Project 3: Improving Digital Cybersecurity and Resilience
Project Overview
This PhD research project focuses on social engineering attacks, attack surfaces, and community training and awareness. This project explores the dynamic interaction between attack surfaces, human behaviour, and community-wide training and awareness efforts to develop effective countermeasures for social engineering, a major threat to individuals, organisations, and systems.
Project Objectives
1. Examine Evolving Attack Surface and Human Vulnerability:
a. Analyze the expanding digital attack surface, considering new technologies and communication methods.
b. Investigate psychological and behavioral factors contributing to social engineering vulnerabilities.
2. Evaluate Community Training and Context-Aware Countermeasures:
a. Assess the effectiveness of community-wide training and awareness programs in reducing social engineering attacks.
b. Design context-aware cybersecurity measures, including adaptive policies and real-time threat detection.
3. Test Countermeasures in Dynamic Conditions:
a. Evaluate the performance of countermeasures under changing attack surface conditions.
b. Measure their effectiveness in preventing and mitigating social engineering attacks.
Project Approach
1. Theoretical Framework Development: Review literature on social engineering, cybersecurity, attack surfaces, and behavioural psychology.
2. Empirical Research: Utilize surveys, trials, and case studies to uncover evolving attack surfaces, human vulnerabilities, and training program effectiveness.
3. Countermeasure Development: Collaborate with cybersecurity experts and behavioural psychologists to create context-aware countermeasures targeting identified vulnerabilities.
4. Validation and Testing: Validate countermeasures through realistic cyberattack simulations and real-world scenarios to assess their efficacy in preventing and mitigating social engineering attacks.
5. Community Engagement: Implement and evaluate community-wide training and awareness programs in partnership with community organizations, schools, and businesses.
Project Delivery
1. Research findings: A thorough report on developing attack surfaces, human behaviour, and community training and awareness attempts to combat social engineering attacks.
2. Context-aware cybersecurity countermeasures: Documentation of the developed countermeasures and guidance for application and integration into current security practises.
3. Community-based initiatives: Specific suggestions for community organisations, educational institutions, and enterprises to strengthen cybersecurity training and awareness programs.
4. Academic contributions: Publishing research in respected publications and presenting at important conferences to advance the area.
5. Collaboration opportunities: Partner with cybersecurity professionals, community organisations, and educational institutions to encourage effective countermeasures and training.
Project 4: Securing Cloud-Based Manufacturing Resource Planning (MRP) System
Project Overview
The goal of this project is to provide a cloud-based MRP system for manufacturers, which will enable them to better manage their resources and operations. The system will offer features such as inventory management, production scheduling, procurement, and supply chain management. It will support multiple devices and operating systems and will be highly scalable and available.
Project Objectives
The objective of this project is to provide a user-friendly MRP system that helps manufacturers save time and effort, and enables them to manage their resources more efficiently and effectively. By providing highly customizable features and automated processes, the system will help manufacturers ensure compliance and reduce errors.
Project Approach
The system will be built using cloud technology, leveraging MIESoft's cloud platform as the infrastructure. We will employ agile development methodology, based on rapid iteration and feedback, working closely with the customer to ensure that the final system meets their requirements and expectations.
Project Delivery
We will deliver a complete system within the specified timeframe, providing training and support to ensure that the customer can fully utilize all the features of the system. We will also offer regular updates and maintenance services to ensure the system's high availability and stability.
We believe that this project will be a significant business area for MIESoft as a provider of cloud-based MRP solutions.