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Digital Marketing / Marketing Analytics / Data-Driven Marketin
Data-Driven Marketing Analytics: Google Analytics 4 and Google Merchandise Store Campaign Analysis
This Data Driven Marketing assessment requires students to develop practical digital analytics skills and apply them to the evaluation of a real-world marketing campaign. The assessment combines professional training through LinkedIn Learning with a recorded executive-level presentation based on data from the Google Analytics 4 Demo Account for the Google Merchandise Store. The aim is to analyse campaign performance, interpret relevant marketing KPIs and present actionable recommendations that senior executives and managers can use for decision-making. CW 1_ LinkedIn Learning and Rec… The first task requires completion of two LinkedIn Learning courses: Google Analytics 4 (GA4) Essential Training and Advanced Google Analytics. Students must submit the certificates of completion as evidence of developing the foundational and advanced skills required for digital marketing analysis. CW 1_ LinkedIn Learning and Rec… The second task places the student in the role of a digital marketing executive for the Google Merchandise Store. Students select and analyse a digital marketing campaign using GA4 demo-store data and then create a 10-minute recorded presentation, with a tolerance of ±2 minutes, providing evidence-based recommendations for improving campaign performance. CW 1_ LinkedIn Learning and Rec… The main presentation is limited to 12 slides and should include an executive summary, key findings and insights, recommendations, actionable steps, and references or appendix material. An additional 4–6 appendix/reference slides may be used for supporting charts, graphs and data. Recommendations must be linked directly to analytical evidence and aligned with the campaign's overall marketing objectives. CW 1_ LinkedIn Learning and Rec… The analysis should examine relevant marketing performance indicators, including click-through rate, conversion rate, cost per acquisition, return on ad spend and user-engagement measures such as bounce rate, session duration, pages per session and retention patterns. CW 1_ LinkedIn Learning and Rec… Students are expected to move beyond descriptive reporting and develop strategic recommendations concerning budget reallocation, creative optimisation, audience targeting and funnel performance. The presentation should identify underperforming and high-performing channels, evaluate audience segments, examine points of user drop-off and propose practical interventions to improve conversion and campaign efficiency. CW 1_ LinkedIn Learning and Rec… The marking scheme gives 20 points for LinkedIn Learning certification, 35 points for executive communication and structure, 35 points for data analysis and insight generation, and 10 points for references and appendices. CW 1_ LinkedIn Learning and Rec… CW 1_ LinkedIn Learning and Rec… Overall, the assessment integrates GA4 skills, campaign analytics, KPI interpretation, data visualisation, strategic recommendation development and executive presentation skills within a practical digital-marketing context. The brief also requires Cite Them Right Harvard referencing. CW 1_ LinkedIn Learning and Rec…
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3,000 words
ParkaLot Enterprise Parking Garage Management System: Software Analysis, Design and Prototype Development
This enterprise software engineering project requires students to analyse, design and prototype a centralised parking garage management system for ParkaLot Group, a UK operator of multi-storey parking facilities. The existing organisation relies heavily on manual processes, local spreadsheets and simple barrier-based vehicle counts, resulting in limited real-time occupancy information, inconsistent reservation arrangements, decentralised billing and reduced ability to optimise parking capacity and revenue. COMP1471 CW 2526 (1) The proposed system supports ParkaLot’s wider digital transformation by integrating customer registration, reservations, parking-space allocation, occupancy monitoring, contracts and billing. Customers can check availability and reserve parking through an online portal, while frequent and corporate users can establish recurring arrangements or block reservations. License-plate recognition and individual parking-space sensors enable automated access control and real-time occupancy tracking. COMP1471 CW 2526 (1) Additional functionality includes centralised electronic billing, dynamic pricing, promotional schemes and predictive decision-making for controlled overbooking. Historical usage data may be analysed to estimate no-shows, early departures and overstays, while operational dashboards support staffing, pricing and capacity-management decisions across the garage network. COMP1471 CW 2526 (1) Development is undertaken in two phases. The first uses structured analysis and design, requiring an Entity Relationship Diagram, Data Flow Diagram including a context diagram, and implementation of a prototype database. The second expands the solution using object-oriented analysis and UML, with emphasis on adaptable and reusable software design. COMP1471 CW 2526 (1) The final report covers the software-engineering **5 Ps—Problem, Process, Project, Product and People—**alongside ERD and DFD models, UML use cases, at least three sequence diagrams, a detailed class diagram and application of design patterns such as GRASP. Students must also submit prototype evidence, source code, personal reflection, peer assessment and work-contribution documentation. COMP1471 CW 2526 (1) Overall, the assessment integrates requirements engineering, structured modelling, object-oriented design, database development, design patterns, software project management, implementation and acceptance testing within a realistic enterprise-system case study. Overview word count: approximately 360 words. AI-use note: the brief permits Levels 1–4 of generative-AI use, including research and exploration at Level 4, but all final submitted text, code, diagrams and designs must be the students’ own work. Level 4 use requires disclosure, an appendix of prompts/outputs and reflective commentary. COMP1471 CW 2526 (1)
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Software Engineering / Enterprise Systems Development
3,000 words
Enterprise Software Engineering Development: ParkaLot Parking Management System Analysis, Design and Prototype
This Enterprise Software Engineering Development assessment is based on the ParkaLot Group, a fictional operator of multi-storey parking garages across major UK cities. The organisation currently relies on fragmented manual processes, basic barrier-based vehicle counting, local spreadsheets, on-site payment and inconsistent customer access arrangements. Students act as software engineering consultants and are required to analyse these operational weaknesses and design a centralised enterprise parking management system capable of supporting reservations, customer accounts, vehicle identification, billing, real-time occupancy monitoring, dynamic pricing and management reporting. COMP1471 CW 2526 The proposed ParkaLot platform is intended to integrate customers, parking spaces, reservations and billing across the garage network. Planned functionality includes online registration and reservation, recurring and corporate parking arrangements, licence-plate recognition, automated space allocation, sensor-based occupancy tracking, centralised monthly billing, electronic payments, promotional pricing and predictive overbooking. The system must also provide dashboards and historical reporting to assist management with capacity planning, staffing, pricing and operational decision-making. COMP1471 CW 2526 The coursework is completed in two main development phases. Phase 1 – Structured Analysis and Design requires an Entity Relationship Diagram representing the logical data model, a Data Flow Diagram including a Level 0 context diagram, and implementation of a prototype database. Phase 2 – Object-Oriented Development extends the system using object-oriented analysis, design principles and UML, with substantial business and user-interface functionality implemented using suitable OO technologies. COMP1471 CW 2526 The final report contains analysis of the 5 Ps of software engineering: Problem, Process, Project, Product and People. Students discuss the business problem and commercial risks, justify the development methodology followed, define resources and budget, document project artefacts and requirements, and identify the main stakeholders involved in the project. The technical design section includes the ERD, DFD, UML use-case model, at least three sequence diagrams, a detailed class diagram and discussion of design patterns. COMP1471 CW 2526 Students must additionally submit a functioning prototype that reflects the design and participate in acceptance testing and a live demonstration. Individual students are questioned on both theoretical and technical aspects of the submitted system. The assessment also evaluates group contribution, peer and self-assessment, personal reflection, research quality, communication and professional teamwork. COMP1471 CW 2526 The weighting places substantial emphasis on technical design and implementation: the UML design is worth 24 marks, the software prototype 15 marks, design patterns 6 marks, and acceptance testing/demonstration 25 marks. This makes the coursework strongly focused on demonstrating the relationship between requirements analysis, software architecture, UML modelling, implementation quality and working enterprise-system functionality. COMP1471 CW 2526
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Environmental Sustainable Engineering
3,000 words
Environmental Sustainable Construction & Logistic Sources – EG7037
This coursework assesses Environmental Sustainable Engineering and Logistic Sources through a large-scale construction project scenario. The student is appointed as the project manager for a £320 million design-and-build project involving the development of a new shopping centre in central London. The project is planned over a 48-month period and is presented as a flagship initiative focused on environmentally friendly and sustainable construction practices, with government support for its sustainability commitments. The project manager is responsible for protecting the client's interests while ensuring that project standards, schedules, budgets, procurement requirements and quality expectations are achieved. The assignment is structured around environmental sustainability, construction logistics, procurement, renewable energy, health and safety, and sustainable design. Part A requires investigation of the environmental, economic, historical and cultural factors that could contribute to significant project costs and delays. It also requires discussion of four renewable or sustainable energy sources that could be used within the project, together with justification for each selected energy source. These activities require consideration of the project's location, sustainability objectives and practical construction requirements. Part B focuses on supply chain management and health and safety. Students must outline the supply chain management and procurement process for the material resources required for the construction project. This includes consideration of how appropriate materials and suppliers can be sourced and managed. The section also requires identification of key health and safety considerations during the production phase and discussion of the associated risks. Part C focuses on sustainable design systems. Students must discuss the need for implementing passive or active systems during the design stage and identify appropriate tools or techniques that can support their application. The potential environmental impacts of these systems must also be considered. The assignment therefore requires students to connect sustainable design decisions with environmental performance and the wider objectives of the construction project. The completed coursework should be approximately 3,000 words, excluding appendices and labelled diagrams or sketches. Students are expected to provide a well-researched and clearly structured written account using appropriate textbooks and other relevant sources. Images, photographs and diagrams should include captions and be cross-referenced within the text. The submission should include a conclusion and a properly cited bibliography or reference list. References must follow the Cite Them Right requirements. The coursework is submitted electronically through the designated Turnitin link.
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Engineering and Environment Advanced Practice London Campus Research Project
3,000 words
LD7152 – AP Research Project
The LD7152 AP Research Project is a postgraduate research and reflective assessment for students studying within the MSc Computing Framework or MSc International Project Management programme. The module focuses on developing students as independent researchers and requires them to reflect critically on their research strategy, activities, learning and personal development. The assessment is designed to encourage students to recognise their achievements, identify challenges encountered during the research process and evaluate how their research experience has contributed to their academic and professional development. The assessment consists of two components: a 3,000-word Reflective Individual Report worth 80% and a 10-minute Poster Presentation worth 20%. Students are expected to work independently while receiving supervisory guidance. The reflective report requires students to demonstrate critical engagement with knowledge discovery and reflect on their educational development in relation to the challenges experienced during their research project. The report is structured around four main areas. The first section, Finding a Research Topic, is approximately 500 words and requires students to explain their chosen topic, why it was selected, the research tools and search techniques used, the Library collections explored, the research aims and objectives, and relevant discussions or recommendations from their supervisor. The second section, Professional Activities, is approximately 500 words and focuses on research development tasks, scheduling, the independent researcher role, challenges such as time management, and communication with the supervisor. The third section, Literature Review and Research Work, is approximately 1,500 words. It requires a critical review of relevant literature with appropriate citation, discussion of the research method and major research activities, and consideration of the feasibility, challenges and recommendations associated with the selected method. The fourth section, Reflection of Research Project, is approximately 500 words and focuses on achievements, contributions, strengths and weaknesses, continuous self-development and employability. Students are expected to reflect on areas including decision making, problem solving, communication and influencing, technical skills, collaboration, innovation and proactivity. The second assessment component is a 10-minute poster presentation. The poster should provide an overview of the research activities and communicate what the student learned during the research process. It should balance visual and textual information and include the research objectives and significance, a summary of the literature review and research method, key research findings, and conclusions and recommendations. The report must include a cover page, table of contents, page numbers and captions for figures and tables. The required formatting includes Times New Roman, 12-point font, numbered headings and approximately 1.2–1.3 line spacing. The assessment brief permits Harvard or APA referencing. The report is submitted electronically through Turnitin on Blackboard. The module is assessed on a Pass/Fail basis, with students required to achieve 50% or above to pass. The assessment also evaluates critical reflection, application of knowledge, independent learning, communication of complex ideas, personal development and reflection on technical leadership.
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Data Warehousing and Big Data
Data Warehousing and Big Data – Inventory Management Data Warehouse
This individual coursework for CS7079 Data Warehousing and Big Data requires students to design, implement and test a data warehouse based on a business case scenario and then export and migrate data to a Big Data platform for further processing. The assessment focuses on the inventory management business process of ABC Consumer Electronics Outlet Ltd, a multi-channel consumer electronics retailer operating from six stores around London and conducting online business across the UK and Europe. The company manages more than 10,000 products across approximately ten categories and more than 200 brands. The company already uses cloud-based and business applications including Vend, Linnworks and Xerox, which generate large volumes of transactional data. However, these datasets are stored separately by the individual applications, making it difficult for managers to produce integrated reports and perform analysis to support business decision-making. A business analyst has therefore recommended the development of a data warehouse capable of meeting the company's reporting and analytical requirements. For this coursework, the solution is focused specifically on the inventory management business process. The inventory management case involves three main business activities: sending purchase orders to suppliers when stock reaches minimum levels, receiving purchase orders and storing stock in appropriate locations, and controlling and maintaining stock levels including adding new products and adjusting existing stock. The required analysis includes daily stock levels for the previous month, weekly identification of products at minimum stock levels, analysis of stock levels by brand, product type and supplier, daily and weekly sent and received stock orders, and analysis of received stock orders by supplier and month. Students must analyse and design a dimensional data model that represents these business activities. This includes defining the grain of three central fact tables, identifying appropriate dimensions, defining dimension attributes and fact measures, and producing simple star schemas showing the relationships between fact tables and dimensions. The design must be justified according to the available data sources and the reporting and analysis requirements. The implementation stage requires students to create the relational database using Microsoft SQL Server Management Studio. Students must create the database, dimension tables and fact tables, establish appropriate primary-key and foreign-key constraints, and demonstrate implementation and testing through SQL commands and their results. Test data must then be populated into the data warehouse. The Big Data component requires migration of test data from the data warehouse to an Apache Hadoop platform using the Hortonworks Data Platform. Students must export the data warehouse data to an external data file, migrate the file into Apache HDFS, create a suitable data structure for loading the data into Hive, and demonstrate Apache Pig for manipulating the loaded data. Implementation and testing of the Big Data storage environment must also be demonstrated through commands and results. The coursework concludes with a personal reflective section in which students discuss what they have learned throughout the overall coursework and the challenges encountered during the process. The final submission is expected to be a well-written, structured and well-presented report combining data warehouse design, database implementation, testing, Big Data migration and reflective learning.
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Emerging Technology and Cloud Computing
5,000 words
Emerging Technology and Cloud Computing – SafeCloud Project
This MSc Management coursework for BPP University’s Emerging Technology and Cloud Computing module is a 5,000-word formal business report based on the SafeCloud project at AlwaysUp Ltd., a manufacturing company specialising in power-electric equipment for buildings and critical installations. The assignment examines how emerging technologies, Big Data and cloud computing can support AlwaysUp Ltd.’s international expansion, real-time equipment monitoring, personalised preventive maintenance, data-driven decision-making, and secure management of equipment and client information. The company is seeking to expand from the UK into European markets through distributor partnerships and ultimately achieve global reach. The report requires the identification and critical evaluation of two emerging technologies that can improve AlwaysUp Ltd.’s support and equipment-care services. Students must evaluate the benefits and limitations of each technology within a manufacturing context and use real-world examples to support the analysis. The assignment specifically assesses the ability to demonstrate a comprehensive understanding and critical evaluation of emerging technologies in business. The second task focuses on designing and evaluating a cloud-based Big Data architecture that integrates the two selected emerging technologies. The proposed architecture must include an architecture diagram and should be evaluated in terms of scalability, security, cost-effectiveness, real-time data processing and decision-making. The solution should address AlwaysUp Ltd.’s business requirements and enable the collection, storage and analysis of equipment-related data. The third task examines data protection, ethical considerations, project risks and resource requirements associated with implementing the proposed solution. This includes consideration of data protection and ethical issues arising from the selected technologies, implementation risks, and the human, technological and other resources required. The fourth task analyses the strengths and weaknesses of the combined emerging technologies and Big Data architecture and considers their application to AlwaysUp Ltd.’s business strategy. The report must conclude with a proposed route forward based on the findings. The required report structure consists of an introduction of approximately 500 words, four main tasks of approximately 1,000 words each, a conclusion of approximately 500 words, Harvard-style references and optional appendices.
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Understanding Patient Data
Data Presentation: NJ OSME Drug-Related Deaths in NJ Counties
This assignment focuses on data presentation and management using drug-related death data from New Jersey counties. The assignment is part of the Understanding Patient Data course and develops practical skills in inspecting, cleaning, organizing, analyzing, and presenting patient-related datasets using Microsoft Excel. Students are required to use the data provided in the file “5.3a Chart on Drug Deaths by NJ County (2015)” and input the county-level information into an Excel spreadsheet. The assignment requires students to inspect and clean the data where necessary, including removing, imputing, and explaining incomplete data entries. Students must also organize and sort the data and obtain descriptive statistics for heroin drug-related deaths and a second variable of their choice. The analysis includes creating descriptive statistics for four variables and comparing the descriptive statistics of heroin with a selected variable. Students must create a copy of the original dataset on a separate worksheet, sort total deaths from largest to smallest, and create a 2-D bar chart and scatterplot. The charts are then used to develop interpretive statements about heroin-related deaths based on the combined analysis of the visualizations. The assignment also requires students to use descriptive statistics to compare the central tendency of three specified variables: Cocaine, Fentanyl, and Oxycodone. A separate worksheet must contain a key or log explaining variable names, abbreviations, and terms used in the dataset. The assignment develops practical skills in Excel-based healthcare data analysis, descriptive statistics, data visualization, interpretation of patient data, and data management. The grading criteria include data input and cleaning, descriptive statistics, sorted data, bar chart creation, scatterplot presentation, and interpretive analysis. The completed assignment must be submitted electronically in Microsoft Excel (.xls or .xlsx) format.
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Cyber Security / Cloud Management
2,500 words
Cyber Security and Cloud Defence Strategy for ShieldSafe Analytics
This Level 7 Cyber Security for Business and Cloud Management portfolio examines the security challenges faced by ShieldSafe Analytics Ltd., a multinational health analytics organisation specialising in AI-enabled diagnostics and telehealth. The organisation processes high volumes of sensitive patient information, including biometric and genomic data, across hybrid-cloud environments and IoT-enabled healthcare infrastructure. Following a suspected data-exfiltration incident involving anomalous traffic from a diagnostic platform connected to third-party cloud APIs, students are required to evaluate the organisation's information environment and develop appropriate cyber-security and cloud-defence strategies. The first task focuses on information environments and the weaponisation of information. Students identify critical elements of ShieldSafe's information environment, evaluate vulnerabilities associated with the data-exfiltration incident and examine how patient data or analytical systems could be manipulated by malicious actors. Relevant real-world healthcare cyber incidents should be used to support the analysis. The second task examines offensive and defensive Information Operations. Students analyse techniques used by nation-state actors and cybercriminal organisations, including healthcare ransomware incidents such as WannaCry, and compare offensive and defensive approaches. The analysis considers how ShieldSafe can balance these approaches while protecting sensitive data and preserving trust in AI-enabled diagnostic systems. The third task applies Information Operations within legal and ethical boundaries and requires development of a secure cloud migration strategy for ShieldSafe's legacy Electronic Health Record system. The supporting student guide specifically permits students to demonstrate an implementation using Amazon AWS, including IAM users and roles, VPC configuration, security groups, web servers, EC2 instances and AWS migration services. The final task requires a comprehensive cyber-defence strategy, including implementation of Zero Trust Architecture across cloud platforms and analysis of vulnerabilities affecting cyber-physical healthcare systems such as wearable medical devices and diagnostic equipment. Students must propose controls against both remote and local attacks. Overall, the portfolio integrates information operations, healthcare cybersecurity, hybrid-cloud protection, secure migration, Zero Trust, cyber-physical security and strategic cyber defence. The work is produced as a portfolio report using PebblePad and must use Harvard referencing throughout, with appropriate citation of academic sources, images, definitions and external arguments.
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Machine Learning and Deep Learning
2,000 words
Development and Evaluation of Deep Learning Models for Healthcare Classification
This individual technical assessment focuses on the design, development, analysis and evaluation of a deep learning solution for a healthcare-related classification problem. Students select one of two provided scenarios: Polycystic Ovary Syndrome (PCOS) detection using ultrasound images or heartbeat classification using electrocardiogram (ECG) signals. The objective is to develop an appropriate deep learning approach and demonstrate critical understanding of the complete machine learning workflow, from initial data exploration through to model evaluation and reflection. Students may either design and train a deep learning model from scratch or customise and fine-tune an existing pre-trained architecture. The complete work is presented through a single Jupyter Notebook integrating Python code, technical discussion, results and visualisations. The notebook must clearly define the selected healthcare problem, explain its significance, justify methodological and architectural choices, and critically evaluate the resulting solution. The first stage involves exploratory data analysis and preprocessing, including investigation of class distributions, data imbalance and relevant patterns. Students prepare the data through techniques such as normalisation, augmentation, train-validation-test splitting and appropriate handling of class imbalance. This is followed by model design, training, validation and hyperparameter tuning, with the architecture selected according to the characteristics of the data and classification task. Model performance must then be evaluated using appropriate classification measures, including precision, recall, F1-score, ROC curves and area under the curve (AUC). The developed model should also be compared against suitable benchmark approaches, which may include traditional machine learning algorithms or alternative deep learning architectures. This comparison should identify the relative strengths and limitations of the proposed solution. The final component requires clear visual presentation and critical reflection on the complete modelling process, including limitations, challenges and opportunities for improvement. Importantly, grading prioritises methodological rigour, analytical depth and critical evaluation rather than simply achieving the highest predictive accuracy. Overview word count: approximately 330 words. AI restriction: this brief only permits automated AI tools for spelling and grammar checking. It explicitly prohibits tools such as ChatGPT, Gemini or Copilot from authoring assessment text or code; any permitted AI use must also be acknowledged.
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Business Intelligence / Data Analytics / Project Management
Business Intelligence and Data Analytics Dashboard for Project Progress Evaluation
This group assessment requires students to act as Project Analysts and critically evaluate the progress of a university project using business intelligence, data analytics and project management techniques. The scenario is based on Northumbria University London planning two major events: a Convocation and an associated Concert. Students must select one of these projects and assess its progress through a professionally structured presentation. The assessment requires students to begin by defining the main problem or opportunity associated with the selected project. They must then identify and analyse the project's key stakeholders and deliverables. A major component of the work involves evaluating project progress using suitable business intelligence and data analytics techniques and developing an appropriate dashboard, which may be created using Microsoft Excel or other suitable software. Students must not only present the dashboard but also critically analyse and justify its design, selected metrics, analytical methods and usefulness for project monitoring and decision-making. Because the projects are treated as already being in progress, students are permitted to create assumed or projected data to demonstrate their dashboards. The assessment does not primarily evaluate the accuracy of real project data; instead, it assesses students' ability to design, present and critically evaluate meaningful dashboards. Any assumed data should therefore remain realistic, internally consistent and relevant to the selected project. The presentation should conclude with evidence-based recommendations for the successful implementation and use of the proposed business intelligence or data analytics solution. Academic references and relevant examples must support the presentation, and a single reference list must be included. Assessment weighting places particular emphasis on application of BI tools and techniques (30%), followed by stakeholders and deliverables (20%) and justification of BI tools (20%). Project and opportunity analysis, conclusions and recommendations, and presentation/referencing are each worth 10%. The final assessment is a 15-minute group presentation followed by a 5-minute question-and-answer session. Every group member must contribute to the presentation. The PowerPoint submission is made electronically through Turnitin. Do not select Harvard automatically for this one. Unlike the previous Roehampton brief, this Northumbria brief requires academic references and a reference list but does not state a specific referencing style in the uploaded document.
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Cyber Security for Business and Cloud Management
This activity aims to assess your comprehension of the diverse concepts discussed in this module. You must use the frameworks and concepts covered in this module's delivery to respond to all the tasks below. Scenario ShieldSafe Analytics Ltd. is a fast-growing health analytics company specialising in AI-driven patient diagnostics and telehealth platforms. Operating across multiple countries, the company processes high volumes of real-time patient data, including biometric and genomic records. Due to the increased reliance on remote healthcare and IoT-enabled medical devices, their infrastructure has expanded into hybrid cloud environments. Recently, ShieldSafe experienced a suspected data exfiltration incident involving anomalous traffic from one of its diagnostic platforms integrated with third-party cloud APIs. As a result, executive leadership has raised concerns about the company’s vulnerability to adversarial information operations, particularly in relation to data manipulation, misinformation, and insider threats. As a Junior Cybersecurity Strategist, you’ve been recruited to support the lead cyber intelligence consultant in reviewing vulnerabilities within their information environment, exploring offensive and defensive Information Operations (IO) concepts, and crafting robust cyber defence mechanisms. The leadership also wants to migrate a legacy electronic health record (EHR) system used across its African operations to a more scalable and secure cloud infrastructure. However, concerns exist regarding cross-border data protection laws, insider threats, and the strategic use of information in potential cyber warfare scenarios.
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L7 Cyber Security for Business and Cloud Management
This activity aims to assess your comprehension of the diverse concepts discussed in this module. You must use the frameworks and concepts covered in this module's delivery to respond to all the tasks below. Scenario ShieldSafe Analytics Ltd. is a fast-growing health analytics company specialising in AI-driven patient diagnostics and telehealth platforms. Operating across multiple countries, the company processes high volumes of real-time patient data, including biometric and genomic records. Due to the increased reliance on remote healthcare and IoT-enabled medical devices, their infrastructure has expanded into hybrid cloud environments. Recently, ShieldSafe experienced a suspected data exfiltration incident involving anomalous traffic from one of its diagnostic platforms integrated with third-party cloud APIs. As a result, executive leadership has raised concerns about the company’s vulnerability to adversarial information operations, particularly in relation to data manipulation, misinformation, and insider threats. As a Junior Cybersecurity Strategist, you’ve been recruited to support the lead cyber intelligence consultant in reviewing vulnerabilities within their information environment, exploring offensive and defensive Information Operations (IO) concepts, and crafting robust cyber defence mechanisms. The leadership also wants to migrate a legacy electronic health record (EHR) system used across its African operations to a more scalable and secure cloud infrastructure. However, concerns exist regarding cross-border data protection laws, insider threats, and the strategic use of information in potential cyber warfare scenarios.
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