Reference no: EM133800267

Data Security and Ethics

Assessment - Ethical Cybersecurity in Business

Assessment Description
Artificial Intelligence (Al) and Machine Learning (ML) are becoming increasingly prevalent in cybersecurity, offering innovative ways to detect threats, respond to incidents, and safeguard data. However, the deployment of Al in this domain raises important ethical concerns. This assessment challenges you to critically evaluate the ethical implications of using Al in cybersecurity, drawing on real-world examples and proposing guidelines for the ethical use of Al in this field.

Your Task

You are required to research and analyse the ethical implications of using Al in cybersecurity. Your analysis should consider the benefits and risks of Al technologies, focusing on how they affect privacy, fairness, accountability, and transparency. You are also required to assess the risk of using such technologies and formulate a risk management and mitigation analysis.

Complete Parts A and B below:

1. Part A (Group Case Study)
(Form groups of 4 or 5 members by Week 2
II. Each group will be assigned a company in class in Week 5
III. Each group is to write a 750-word case study and submit it as a Microsoft Word file via Turnitin by the end of the class

2. Part B (Individual Video)
(Create 4 slides of the areas you contributed to in the group work and reflect on your contributions
((Create a 5-minute video of yourself presenting these slides. In your video articulate your reflections on your contributions, as the majority of the marks will be allocated to this aspect.

III. Submit the video no more than 3 days after your in-class assessment via the Kaltura link

Assessment Instructions

PART A: Group Case Study
You have been hired as an ethical consultant team for a multinational tech company that specialises in Al-driven cybersecurity systems, such as Crowdstrike, IBM.
Each group will be assigned a company in class in Week 5 by your facilitator.
Your task is to prepare a comprehensive ethical analysis of the company's Al-driven cybersecurity systems, provide recommendations for addressing the concerns, and analyse risk management and mitigation strategies to ensure the responsible use of Al in its services.

1. Al in Cybersecurity (150 words)
• Provide an overview of how Al is used in cybersecurity by the company. (Hint: See readings for assessment 1 on MyKBS).
• Discuss both the benefits (e.g., faster detection, scalability) and risks (e.g., false positives, over-reliance on Al) associated with AI-driven cybersecurity systems in this real-world environment.

2. Ethical Analysis (200 words)
• Identify and critically analyse the ethical concerns of the use of Al for this purpose. You should address the following key issues:
o Data Privacy: Al systems require large amounts of data to function effectively, raising questions about the handling of sensitive information. Are there risks of privacy violations, and how can they be mitigated?
Bias in Threat Detection: Al algorithms can unintentionally inherit biases from training data, potentially leading to unfair or unequal threat detection. For example, could certain behaviours or user groups be disproportionately flagged as threats?

3. Cybersecurity in business (100 words)
• Explain why cybersecurity is important in business.
• Detail the ethical dilemmas one may encounter when attempting to balance privacy and security.

4. Cybersecurity Risk Assessment and Mitigation (300 words)
Assume there was a data breach in this company due to an intentional cyber-attack. Consider how your assigned company could assist with cybersecurity protocols by addressing the questions below.
• Define an attack surface and its role in cybersecurity risk assessment. Get Instant Help!
• What risk assessment and mitigation methods would normally be employed by humans and whv?
• Compare and contrast the speed, accuracy, and ethical implications of using Al vs human privacy and security team members during data breach scenarios. How could Al systems be

Compare and contrast the speed, accuracy, and ethical implications of using Al vs human privacy and security team members during data breach scenarios. How could Al systems be leveraged to respond to this data breach? Highlight the differences between human and Al capabilities in terms of response time, accuracy, and potential biases.

PART B: Individual video
Imagine you are required to reflect on your contributions as part of the team that analysed the company' Al-driven cybersecurity systems. Your reflections assist your team to improve their capabilities. As such you must address your contributions to this project, areas where you did well, areas where you needed to improve, and your overall understanding of the project.
1. Create 4 slides that cover
your contributions
areas where you did well
any challenges you had concerning working in a group.
areas where you needed to improve
your overall understanding of the project.
Create a 5-minute video of yourself presenting these slides.
In your video articulate your reflections on your contributions as the majority of the marks will be allocated to your reflections.
Submit the video no more than 3 days after your in-class assessment via the Kaltura link

Ethical Analysis

Identify and critically analyse the ethical concerns of the use of AI for this purpose. You should address the following key issues:

Data Privacy: Al systems require large amounts of data to function effectively, raising questions about the handling of sensitive information. Are there risks of privacy violations, and how can they be mitigated?

Bias in Threat Detection: Al algorithms can unintentionally inherit biases from training data, potentially leading to unfair or unequal threat detection. For example, could certain behaviours or user groups be disproportionately flagged as threats? Get Instant Help!

Cybersecurity in business

Explain why cybersecurity is important in business.