ITW601 Information Technology – Work Integrated Learning Report 2 Sample
Assignment Brief
In your group, further develop a comprehensive IT project action plan progress report of 4,000-words (+/- 10%), which covers risk management, time management and resource management, and includes the provision of a detailed analysis on the solution suggested in Assessment 1.
A project action plan will discuss how you might manage different risks faced during the execution of the project and provide a detailed analysis of the solution suggested in Assessment 1, including any difficulties faced and how these were overcome. Preparing a project action plan will prepare your group for the final part of your project.
Task Instructions
This assessment aims to provide an update on your group’s project (that is, a progress report) alongside the project action plan which will provide you with the future direction for the remainder of your group’s project. At this stage, your group should have a thorough understanding of the project in terms of its feasibility, timeline, cost and resources.
This assessment will require your group to respond to the how and why queries which surround your project.
Please ensure that you cover the following points in your report as this will prepare you for the final part of the development process:
• A set of tools you are planning to use for the expected outputs.
• How your group has estimated the cost, timeline and resources for the project.
• The complete management plan of the cost, timeline and resources for the project.
• The risk management plan for your project; that is, discuss the potential risks involved in your approach.
• Discuss any ethical implications of your project which may occur in a professional setting
Report Structure
The structure of your 4,000-word (+/- 10%) progress report on your project action plan should contain the following sections:
• Overview
o This section should provide an overview of the purpose, scope and objectives of the project.
• Assumptions and Constraints
o Discuss the assumptions on which the project is based on and any imposed constraints on which will need to be considered, such as schedule, reusability of any other tools, technology to be employed and so forth.
• Project Organisation
o Provide the organisational entities within the project – describe the project’s internal organisational structure and define roles and responsibilities of various group members.
o Identify all group members and their roles.
• Managerial Process Plan
o Discuss the project start-up plan, risk management plan and project closeout plan.
• Technical Process Plan
o Analyze the development process model, technical methods as well as any tools and techniques that will be used throughout project implementation.
o Analyze various approaches you have shortlisted for the execution of the project along with the pros and cons of all approaches. You should also discuss why you have selected a particular approach.
• Ethical Implications
o Discuss the ethical implications of the project.
• References
o Provide in-text citations and a reference list which includes industry specific and academic references.
Solution
Overview
The following project focuses on the establishment of a cyber-security platform driven by AI that helps in threat detection and response. The purpose of this project is to delve into the implementation of artificial intelligence in eradicating cyber threats. Cybercrimes have increased rapidly in the present era along with technological advances. This calls for immediate implementation of threat detection and response systems and the role of AI in handling cyber threats has demonstrated effective outcomes. The main advantage of using AI is its flexible nature that can analyse and mitigate new dangers improving “framework flexibility” (Yaseen, 2023). The project offers various scopes in the field of AI-driven cyber security frameworks as it delves into the effectiveness of AI in handling cyber threats. It also unravels the versatility of AI-powered frameworks in handling new dangers and learning from past experiences. Further, the balance of using AI to mitigate cyber threats along with the sustenance of digital platforms to mitigate cyber threats underscores the relevance of AI-driven cyber security platforms.
The main objectives of the project are to construct a web-based platform that uses AI for threat detection in real-time alongside an immediate response system. It also delves into the usage of machine-learning frameworks to analyse vast quantities of information and use that information to gather evidence against possible threats. Also, the construction of a user-friendly interface is crucial for security professionals to engage with AI to understand any impending cyber threats for quick response. The working of the cyber security platform needs to be constantly monitored and reviewed for its smooth operations. Implementation of feedback is crucial in the enhancement of the functioning of the platform’s threat detection and response capacities. The following project report will also focus on the assumptions and constraints of the project alongside project organisation discussion. Further, a managerial and technical process plan will also be implemented to enhance functioning of the proposed cyber security platform for university assignment help The ethical importance of the project will also be focussed in the following sections
Assumptions and Constraints
The project is based on the assumption that implementing artificial intelligence will help in the mitigation of cyber threats and will enhance the security of IoT (Internet of Things) systems. It also assumes that AI is a reliable tool for dealing with excess information and organising it for informed decision-making purposes. AI is a powerful tool for the automation of tasks which helps in the precise detection of threats and generates responses accordingly, leading to a more secure cyber security framework. Organisations such as the National Institute of Standards and Technologies are also shifting towards the usage of AI-powered threat detection frameworks to identify and reduce threats in the future (Kaur, Gabrijelcic, and Klobucar, 2023). However, there are constraints related to scheduling such as identification of the most important limitations in the implementation of the cyber security framework and working on strategies to overcome it.
Also, examining the impact of addressing the limitation on the overall security system is essential.
Schedule constraints such as “ineffective change management” where the impact of system susceptibilities is overlooked can bring down organisational efficiency. There are various AI tools that are reused to identify threats and strengthen cyber security. The reusability of AI tools poses various constraints. The application of using AI technology requires specialised knowledge and training without which users cannot use it to its full potential. Vectra AI is one such AI tool that has the capability of monitoring threats in real time and allows organisations quick detection of cyber threats and resolution (geeksforgeeks.org, 2024). Although this tool is highly efficient and uses machine learning for threat detection, its reuse is limited as it lacks proper guidelines for its implementation which is problematic for users. Another reusability constraint is “scalability” which means the expansion of AI-powered frameworks with the growth of organisations to assess and mitigate emerging threats.
Various technologies are employed in the construction of a cyber-security platform which comes with its own set of constraints. Technology such as “predictive analytics” is incorporated to gather insights from past data to predict future threat outcomes. There may be data-related inaccuracies such as missing or incomplete data which will result in inaccurate predictions. Other technology such as “machine learning” helps in the assessment of real-time user demeanour and web traffic to determine threats. Using machine learning may face vital constraints such as data quality issues and lack of training data.
Project Organisation
The internal organisational structure for this project focussing on an AI-powered cyber security platform is influenced by organisational structure which deals with technological challenges. Organisational structure must be formed where technical persons are present who analyse problems and extract solutions as well. “Organizational learning” impacts the performance of the organisation and deals with the transfer of knowledge among various levels of working individuals and the organisation itself (Oh and Han, 2020). The internal organisation structure also consists of senior-level managers and auditors who analyse risk factors for the efficient working of the organisation.
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Figure 1: Internal organisation structure for risk management
(Source: divurgent.com, 2024)
Based on the above figure, there are three levels of risk management. Level one managers oversee daily functioning of the organisation and manage risks. They also oversee managerial duties to ensure they comply with organisational policies. Level two senior managers identify emerging risks and help in development of “risk management” strategies. Level three includes the governing body or auditors who review the working of first and second-level managers and analyse third-party risks.
Completion of this project involves diverse group members who are:
• Machine learning engineers
• Hardware engineers
• Testing personnel
• Project manager
• IoT security analyst
Each of these team members listed above plays an important role in the completion of the project which is the construction of an AI-operated cyber security model. Machine learning engineers apply their skills to create predictive models for estimating cyber threats. They work closely with project managers to develop efficient machine-learning systems based on project requirements. The presence of an able hardware system to support the computational power is required for AI and ML operations (Talib et al., 2021). Hardware engineers take care of the development of efficient hardware that makes full use of resources while reducing power requirements. Hardware engineers for developing an efficient “cyber security threat detection platform” design a secure hardware system and embed authentication protocols. The testing personnel are assigned the task of reviewing and monitoring the functioning of the designed cyber security platform. This project deals with AI which works on a set of training data. Testing personnel make sure that this data is accurate and reliable.
The role of project managers is extremely crucial for the completion of projects. They not only assign tasks and responsibilities but also monitor progress and coordinate with team members for project completion (Salvador et al., 2021). Hence, for this project purpose which is an “AI-driven cyber security platform”, project managers work in collaboration with the other group members to ensure they carry out their responsibilities accurately and on time. They help in the enhancement of project performance. IoT security analysts make sure that the system structure is updated to minimise the risk of cyber attacks.
Managerial Process Plan
1. Project Start-Up Plan
Project start up planning is an initiation phase of project where milestone of project along with risk and budget has been identified accordingly. The definition of start up is defined as the working on new ideas to generate results and revenues .This start up is taking some of the components like end point protection, threat detection and response, advance threat detection and cloud security . Technical Feasibility Analysis helps to assess the feasibility and implementation issues concerning real-time analytics and machine learning as well as the consolidation of the proposed UI with existing SIEM systems. Team Construction and Resource Distribution focused on the Core Development Team, finally, relevant people who should be enlisted are machine learning engineers, software developers, UI/UX designers, and cyber security specialists (del Rio, & Boie, 2021).
Task assignments and role Definitions focused on establishing accountability matrices for each team member as well as delineation of shared roles between development and security teams based on a project requirement (Frumence et al., 2021). Creating the foundation for the flow of institutional communication, the project management platform helps to create a project management tool for monitoring tasks, achievements, and completion of progress. Regular meetings and update scheduling a weekly meet-up to give a progress check, address issues that may have arisen, or check on the current positions.
2. Risk Management Plan
Risk management plan is defined as the mitigation strategy of risk which can hinder the success of the project. There are a few risk management approaches like threat and vulnerability detection, risk based vulnerability management, defence in depth and continuous monitoring. Proposed project is showing threats regarding Malware attack, Distributed denial of service attack, phishing attack, zero day exploitation which can fell the traffic into jeopardy. There are the some additional risk factor in the scope of project management like timeline progress issue, incompatibility with security network, communication issue which can fall risk the entire project management as well.
Risk Identification and Classification
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Table 1: Risk Factor Evaluation Table
(Source: Own-Constructed)
Known threats include a lack of progress on the timeline, integration incompatibility with other security frameworks, and a lack of communication between the development and security teams. Include the following proper data privacy addressing, the biases of the threat detection algorithms and the asymmetry of the data collection and processing. Risk Assessment and Impact Analysis, in each risk, a likelihood and an impact are given ranging from 1-5 a 5 means a high likelihood or impact (Gray et al., 2024). Percentages computed from these scores determine which risks deserve the most attention in terms of risk reduction. Establishing how each realized risk could impact the project duration, cost, or product so that better decisions in allocation and timing can be made.
Mitigation Strategies
Threat mitigation strategy is capable for eradicating threats of the project management and in the aspects of technical implementation. This study recommends designing redundancy into the model training datasets to reduce the number of false positives in the model. Also, create backup procedures to keep the platform running without interruption in case of technical problems. Implement an iterative structure for cultivating a development model that can change at will after beginning the pilot tests. If a problem occurs during the course of a project, additional staff and other resources are also hired from other service providers in order to ensure that the scheduled timeline is met. Prevention of bias in the algorithm will involve conducting annual audit checks on the algorithms and making bias tests as well. Furthermore, the data stored from the users will be sanitized, and encrypted to meet data privacy law policies (Spellman, Cost, & Villano, 2021). CRMR, as well as the utilization of project management software that will help in the ongoing monitoring of risk indicators such as developmental updates and compliance reports. The project team will prepare the bi-weekly risk assessment report that will inform the stakeholders of emerging risks and risk management progress on the project as follows.
3. Project Closeout Plan
Project closeout plan is final stage after designing of project. This is containing with testing, validation and the implementation of all stages . The closeout plan is validating which is cross checking the allocation of resources and expenditure of all the stated project as well. The closeout planning will be checking the role of AI and machine learning in threat detection, number of mitigated threats along with the amount of overallocated and under allocated resources while conducting project management as well.
Project Assessment and Last Examination
Perform an intensive stress test on all the functionality and concentration on threat detection in real-time, response configurations, and advanced analytics. Validation shall also encompass issues to do with the ability of the system to perform its tasks, how well it can handle loads, and its compatibility with other integrated security instruments. Many of the above activities can involve security professionals from the related organizations to receive the final feedback on UAT and make the appropriate changes if needed (Damij, & Damij, 2021). Essential performance indicators should be assessed, including the time taken to address incidents, the number of false positives, and general user interactions in relation to the defined project aims and goals.
Documentation and Knowledge transfer
Write large IEEE documentation including Overview of System Architecture, User manuals, Algorithm and Flow explanation, and Code repositories. Holding informative training activities for the internal IT and cyber security that are in charge of managing and upgrading the platform to guarantee deep knowledge about the major elements and measures of this system will never be a bad practice (Garnett, & Baker, 2021). Explain how the platform will be managed and maintained – this includes but is not limited to system updates, data backup, and retraining schedules of the models.
Last Gathering Stakeholder Review or Endorsement
In the process of project completion, provide a detailed overview of the output in the form of the final presentation to stakeholders and demonstrate the potential users of the platform and its capabilities, the presented interface, and performance indicators. Single out the approval of the stakeholders and complete the development process officially to pass the project to the maintenance personnel. Use a project questionnaire administered to stakeholders to capture lessons learned and best practices on the project.
Technical Process Plan
The work that has been proposed shall integrate a number of technical approaches and machine learning techniques to support the development of the AI-based cyber security Platform for Threat Detection and Response. This plan gives an initial impression of this technical process: development process model, machine learning algorithms, tools and techniques, and execution methods.
Development Process Model
This research will incorporate the Agile Development Model in order to maintain a development and delivery cycle, as well as flexibility and adjustments owing to customers’ feedback. This approach is suitable for this project because it can always evolve the Agile model to suit the current cyber security environment. In the Agile model, each phase of the project (also known as a ‘sprint’) will contain goals that need to be met such as developing the model, designing the interface, and testing the model. Its major advantages include shorter development cycles, an improved product creation process where possible modifications are done based on user feedback, and the creation of a consummated product that effectively responds to user demands (Pesut et al., 2022). Machine learning applications in threat detection and subsequent response. However, for this platform, it will employ both the supervised and unsupervised learning models to identify and provide a response to threats.
The following machine-learning models have been selected for implementation:
Random Forest
Random forest is an ensemble method used for both of classification and regression method by combining different decision trees. Random forest is the combination of multiple decision trees for making one decision. Precise, strong tolerance to large sets, and not sensitive to overlearning. Computer resource needs may be especially true for large databases.
Decision Tree
The decision Tree model has been chosen as a solution for the classification of threats since it works on the process of segmenting data into features of various importance. Low complexity, data does not need much preparation and can be used for simple types of pattern recognition. Highly susceptible to overtraining and is not very effective in identifying sophisticated threats without calibration.
Support Vector Machine (SVM)
SVM is the most suitable for threat detection in cyber security because it provides a clear boundary between data assigning it to different classes; moreover, this classification allows to reveal of abnormal patterns of network traffic (Cornish et al., 2023). Good for when the number of features is high; stable when there is a clear gap in terms of distance. Also, can be used in an effective and efficient manner only with smaller datasets, and can be quite confusing during interpretation when working with feature interactions.
K-Means Clustering
An example of unusual pattern detection is the anomaly detection K-Means algorithm will be used for clustering the data which will be used to detect the data points that represent the potential threats. Effective when used on big data, useful in recognizing unknown or zero-day threats. Requires prior defined clusters; has low accuracy to intricate data unless integrated with other classifiers.
Neural Networks
In more complex and variable threat conditions, an artificial neural network will act as an additional layer of pattern recognition for large systems. Multiple-level learning adaptability; good at identifying multifaceted structures. Testing on the real images takes time to make the computations, and it needs lots of training data.
Technical Methods and Tools
Development Environment in Jupyter Notebook has been selected to develop models and analyze data because of how convenient it is, it supports Python and it has a good graphing system. Jupyter can also help share the code and results with the rest of the team so that this study can monitor the progress being made. The primary programming language for this project is because it has rich libraries and frameworks to support Machine Learning (Song et al., 2022).
Pandas for data manipulation and NumPy for handling data. Scikit-Learn for data, preprocessing, normalization, feature scaling, and Cross-validation for splitting data for training and testing the model. Additionally, popular libraries including Matplotlib, and Seaborn for data visualization will come in handy in analyzing trends of cyber security incidents to validate the models. Testing and Validation, Cross and K fold validation will be used in testing to reduce model over-fitting. In order to handle false positives and optimize response time the Confusion Matrix will be employed to assess the models’ efficiency.
Approach Analysis for Project Execution
Some of the options that were used in the decision-making process concerning how this project could be implemented include the Waterfall, Agile, and Hybrid development models. Both have their benefits and have their drawbacks detailed below.
Waterfall Model
As to the strength, it was possible Clear and easily understandable structure with distinct stages that helped to be as detailed as possible and keep everything systematic and step-by-step. Concerning the disadvantages It is less flexible; it is not suitable where there are recurring feedback and changes in the requirements of the project (Brilhante, & Skinner, 2022). Due to the fact that threats in information security are highly dynamic, the Waterfall model does not have the flexibility that allows developing the patterns and feedback on its platform in a short time.
Agile Model
When it comes to the prospects it is very flexible, updated frequently, and brings continuous feedback and is best suitable for projects where requirements may vary based on user feedback and threat development. On the flip side, the development of a dedicated team collaboration may require certain modifications of the set timeline with regard to the feedback session at the end of every sprint. Agile was selected for this project since new threats are often discovered, and there is a dire need for iterative development, especially to redesign detection or response mechanisms following feedback.
Scrum (Agile) and Waterfall Model Hybrid
As defined by Erick Bergmann and Andy Hamilton, the Agile-Waterfall hybrid typically allows teams developing software to work within the Agile methodology, while hardware development teams and product managers stick to the Waterfall approachch (Kyne et al., 2021). As to the disadvantages, this often becomes difficult to manage for the simple reason that each stage uses a different methodology, and this may cause problems in terms of resources and time. The problem in coordinating a hybrid model outweighs the advantages of having one especially since Agile is sufficient for the purpose of flexibility in this project.
Ethical Implications
Proposed generation of an AI cyber security tool also entails a number of ethical issues including those to do with data privacy and protection, and whether or not the AI tool will incorporate a biased form of decision-making. They say it is possible to ensure that personal information, as well as any sensitive information, does not fall into the wrong hands. Every processed data must adhere to the laws, for instance, GDPR and HIPAA that require the platform to manage, process, and store all data collected effectively and safely (O’Connell et al., 2021). Practices to ensure that the information of the users is obtained and handled in an ethical manner must be in place to minimize the risk of any unauthorized access in the usage of information gathered through the services are offering; there should be checks now and then to ascertain whether proper measures are being followed with regard to the policies that have been put in place on the handling of user’s data. The first of these is the ethics of how the algorithms used for threat identification pre-supposed by the machine learning models are free of biases. Bias can emerge unconsciously from training data or the methodologies selected and it can cause unfair treatment or reveal disparities in identifying specific types of behaviors or users. In this regard, the platform will integrate bias detection solutions and have periodic checks to eliminate the occurrence of biases and fairly treat users of the platform.
Other measures that should be taken include the need to be clear in the use of data and the detection of threats. Anyone accessing the system should be aware that the data they input is used within the system for creating threat intelligence or response actions. Accessibility is beneficial for users and meets the criteria of ethical behavior due to the concept of informed consent. Third, threat intelligence also creates new questions about the ownership and control of threat intelligence data when it is shared with other organizations (Pesut et al., 2022). While collective intelligence is very useful in enhancing threat detection capacities, this can only be solicited if the data to be shared must first undergo a process that would remove the identities of different organizations while the data that circulate between the participating organizations are not detrimental to the security of any single organization.
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