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ITW601 Information Technology – Work Integrated Learning Report 1 Sample

Assessment Task

As a group, write a 1,500-word (+/- 10%) project proposal to critically evaluate a potential project and devise an approach to deliver this project to market.

Please refer to the Task Instructions for details on how to complete this task.

A project proposal is a document that an IT engineer/manager, often working in a team, develops for business stakeholders in order to provide information about the proposed project and seek approval and funding.

A project proposal generally contains the title of the project, the methodology that will be adopted for the project and the expected outcomes of the proposed project. The project proposal should also discuss the benefits that the development of the proposed infrastructure/project will have for the business as well as describing the financials of the project and an estimated timeline for the successful completion of the project.

This assessment will help you to develop your problem solving, management and teamwork skills. You will need to demonstrate your ability to identify and communicate ideas to different stakeholders in order to gain their approval for your ICT-based project proposal.

Your group can come up with your own idea in IT/cybersecurity domain, but these are some suggestions which may assist you in formulating a project –

- Embedding COVID-19 infographics to an analytics website.

- Implementing a CI/CD pipeline for continuous delivery.

- Migrating in-house infrastructure to cloud platform (MS Azure/AWS).

- Setting up data pipelines for an AI-based project in cloud.

Proposal Structure

Your 1,500-word (+/- 10%) project proposal should contain the following:

1. Title of the proposed project

2. Description of the project (300 words)

- Describe the business stakeholders for the project

- Describe the project you are planning to undertake

- Start with a high-level overview of the project, then explain it in more detail

3. Proposed solution (500 words)

- Describe how your proposed solution is effective by comparing it with some other alternative approaches

- Describe the importance of your proposed solution in terms of business value.

4. Project plan (500 words)

- Discuss here the resources, cost and timelines of your project

Resources from Module 4 will help you write this section.

5. Conclusion (200 words)

- Provide a succinct summation of what has been addressed in your report.

You will need to develop and support your project proposal with industry specific and academic references and include a reference list in APA style.

Solution

Proposal

Description of the project

Stakeholders of the project

To ensure alignment with the company’s IT and cybersecurity strategy and manage the project effectively in an AI driven cybersecurity platform, the Chief Information Officer (CIO) or Chief Technology Officer (CTO) could oversee the adoption of the platform. Stakeholders are responsible for ensuring that the platform adheres to regulatory frameworks and data protection laws such as GDPR and HIPAA (Franke et al., 2024). Additionally, the IT operating team and risk management team could handle the integration of the new platform with existing infrastructure by ensuring seamless operations and minimal disruptions. Business unit leaders and employees can also enhance security measures on the platform's usability by providing feedback.

Planning of the project

In the initial phase, the project goal is to engage with stakeholders to determine technical and business requirements, such as system capabilities, compliance needs, integration points, and user preferences. The platform design phase includes network setup real-time analytics, and security features to complete the machine learning models and data analytics tools (Rehman et al., 2024). In the development phase, the integration of AI and machine learning models helps to detect threats with real-time monitoring of network traffic, user behaviour, and system logs. During the pilot testing, the project will gather user feedback on the functionality, usability, and efficiency of the platform. Based on pilot feedback the project will ensure data privacy protocols by including GDPR and HIPAA. Lastly, to improve threat detection and response capabilities, the project will continuously monitor the platform’s performance to ensure long-term effectiveness.

Overview of the project

The project of AI-driven cybersecurity focuses on building a robust, scalable, and user-friendly system that integrates AI capabilities for threat detection and response (Huyen et al., 2024). The project aims to develop an AI-driven cybersecurity platform by leveraging advanced machine learning algorithms to enhance real-time threat detection that improves the overall security posture of organizations. The project will also follow a structured development process to ensure the platform's scalability and effectiveness in evolving cyber landscapes. A user-friendly dashboard will automate incident response which allows security professionals to interact with the system using natural language commands.
Proposed solution

AI-based cybersecurity is one of the efficient, innovative and intelligent identification processes of cybersecurity threats. Effective utilisation of the threats can improve the overall protection mechanism. By using the machine learning approach, the platform can work with a large amount of data from distinct sources and define the possible security threats in the shortest time. It also offers predictions, intuitive design, the ability to become an integrated part of the existing tools, as well as mechanisms that enhance the refinement of detection and response in real-time for the assignment helpline.

Comparison with Alternative Approaches

One of the main alternatives to AI-driven platforms is rule-based security systems. The rule-based structure searches threats through pattern matching, and their configurations are set manually to target known weaknesses (Khraisat & Alazab, 2021). While these systems can effectively mitigate known threats, they perform worse when the threat involves unknown attacks. Furthermore, Rule Based Systems (RBS) are generally prone to higher False Positive (FP) rates which inundate security analysts thus hindering their response times (Fokas & Brovko, 2020). On the other hand, the proposed AI system can learn automatically from the threat data. This process helps to detect new patterns of attack, can eliminate the false positives and adapt to the changing environment. Without frequent updates, the AI-based system can able to get an improved outcome.

Another is Security Information and Event Management (SIEM) solutions, where disparate entities’ information is processed for continuous threat analysis (Dias & Correia, 2020). Despite its incredible ability of data aggregation, SIEM platforms rely on human intervention when detecting and responding to an attack, and this takes time. It features a data aggregation process like SIEM, but it goes a layer higher in automating the response to an incident without involving human intervention. A big plus for the AI platform is that all actions are performed automatically and the decision-making process is done much faster than with manual methods.

Business Value of the Proposed Solution

Great business value is achieved by this innovative system. Identification of capabilities and handling cyber threats can be done more effectively through this process. One organisation can use this system to get more stabilised and secure cyber protection. This improvement in the security posture minimises the chance of a data leak, which is financially and reputationally devastating (Bandari, 2023). The platform’s capability for predicting certain risks and adaptive learning mechanisms also protect organisations from evolving threats, giving organisations a preemptive cyberattack plan that reduces the number of possible weak points.

One of the essential business values is the automation functions of the platform. Incident management can be automated. The automated system ensures that the work of security specialists is not overloaded. This means that fewer man-hours are required in incident management (Fokas & Brovko, 2020). Therefore, assurance of savings in costs associated with operations is obtained. Also, cutting across response time of incidents enables organisations to handle or prevent impacts of cyber threats with minimal downtime and loss of business value.

The integration capability means that businesses can truly get the most out of earlier investments in cybersecurity tools. Instead of existing systems being replaced, they are enriched by an advanced AI-based layer that can integrate into the current framework of any organisation, so there is no need for them to redesign their security systems.

Project plan

 

Table 1: Project Plan
(Source: Self-created)

Analysis

The project plan for the AI-driven cybersecurity platform basically involves the total six important phases, each of them with specific resources, proper timelines, and expected costs (Reddy et al. 2020). In phase 1, all industrial experts and stakeholders consulted to gather the platform requirements to ensure the legal compliance with basic regulations such as HIPAA and GDPR, and involving a main cybersecurity expert team and other legal professionals ($100,000, 1 month). A legal team specializing in the main facts of data protection laws (e.g., GDPR, HIPAA) and it is an essential factor to review compliance, and addressing potential privacy issues and other regulatory requirements, and decreasing risks of legal breaches and fines.

Cybersecurity experts that would be hired to gather the detailed platform requirements, to ensure the platform meets organizational needs (Onih et al. 2024). In phase 2, the machine learning model development focuses on improving the advanced threat detection algorithms to user behavior analysis, use network data, and the system log (Goswami et al. 2024). Data scientists and machine learning engineers specifically build the real-time analytics models with access to large threat detection algorithms, user behavior analysis, and system logs.

Machine learning and data science engineers build the real-time analytics models, with access to the large threat datasets ($700,000 in the expected 3 months). In phase 3, platform development & integration, front-end developers, along with the cybersecurity experts will be properly hired to create the platform infrastructure to build APIs, and integration with associated tools such as design and SIEM, and design of user-friendly interfaces and other response workflows around $1,000,000 5 months. Pilot testing involves assessing the platform's optimization with the full development team. In this phase utilizes the cloud storage and other servers to ensure a smooth deployment (Lanka et al. 2024). In the phase of ongoing support & improvements, the platform navigates the continuous monitoring, bug fixing, and feature updating.

Conclusion

The AI-driven cybersecurity platform project offers a comprehensive approach to improving the posture of an organization. By leveraging advanced machine learning models with real-time threat detection the project automates incident response. The study elaborates the phases clearly to ensure scalability, adaptability, and user-friendly structure. The ability to analyze vast datasets, predict risks, and respond in real-time sets provides Security Information and Event Management (SIEM) solutions. However, rule-based systems struggle with high false-positive rates that delay responses. Also, Effective data aggregation of SIEM platforms heavily relies on manual intervention for decision-making which is also time-consuming. In a business perspective, the platform delivers significant value by leveraging threat detection and incident management. It also reduces reliance on human intervention that minimizes operational costs. The project complies with legal and regulatory frameworks for continuous improvements through feedback loops and adaptive learning mechanisms to respond to future cyber threats.

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