Investigate the efficacy of behavioral analytics in identifying and mitigating sophisticated threats in BI systems.

I have attached 2 word documents. One has the format – please use the same format and make sure to answer all the points and sub points mentioned in the prospectus (example). Make sure that the topic is the one mentioned in question title “Investigate the efficacy of behavioral analytics in identifying and mitigating sophisticated threats in BI systems.” and not the one in example. The other document is a write up made by me on various articles I found about it. You can use that and add more as you feel to create a good prospectus that is not plagiarized or AI generated. The format along with all the points to be answered is in the document named “Prospectus format” and the actual references and a write up on the actual topic is the document named “Current findings” 1
Impact of AI on Supply Chain Management (SCM)
Student’s Name
Institutional Affiliation
Course Name
Instructor
Date
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Impact of AI on Supply Chain Management (SCM)
Problem Statement
Supply management professionals now face opportunities and challenges due to the
rapid development of AI technology. While using AI has the potential to increase productivity
and cut costs, it also raises concerns about how it will affect human labor and the requirement
for new training and skills (Modgil et al., 2022). Businesses must comprehend how AI will
affect supply management to create effective adoption and integration strategies. In addition,
this study aims to examine how artificial intelligence (AI) will affect supply management.
The study evaluates how much AI has impacted supply management operations’
effectiveness, cost-effectiveness, and overall performance.
Purpose Statement
By gathering preliminary information from a survey of supply managers and related
departments, this study aims to analyze the effects of AI on supply management. The survey
results will pinpoint areas where Supply Management operations can be improved and create
performance-enhancing strategies.
Research Questions
1. Which AI technologies have Supply Management departments adopted?
2. What difficulties did supply managers encounter when implementing AI
technologies?
3. How much has AI increased supply management operations’ efficiency?
4. What financial benefits come with the application of AI technologies?
5. What tactics can be developed to use AI in supply management better?
Data Collection
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This study will collect data via online surveys of supply chain management
professionals in various companies. The online surveys will incorporate questions about how
companies utilize AI in supply chain management, the benefits and challenges of using AI,
skills, training, job roles, responsibilities, and ethical consequences. There will be a mix of
precise and closed-ended survey inquiries. While the closed-ended questions will allow the
researcher to make quantitative inferences from the survey results, the open-ended questions
will allow respondents to provide more in-depth information (Ali et al., 2022). Also, the
online surveys will be anonymous; the respondents will only need about 7 minutes to skim
and answer all the questions.
Data Analysis
One method used to analyze qualitative data is content analysis. A methodical
approach to data analysis that can be applied to surveys is content analysis. In order to find
patterns, themes, and relationships, the data must be coded and categorized. After coding the
data, the researcher can analyze the findings to make inferences. In order to gain insight into
the survey responses without having to read through each response, content analysis is a
valuable technique for analyzing survey data in a study of the effects of AI on supply chain
management. By classifying the responses, the researcher can quickly identify common
themes and patterns using AI in supply chain management. Such can help identify areas that
could use improvement or present opportunities that should be looked into further. Using
content analysis, the researcher can also find outliers—responses that differ noticeably from
the rest. These outliers can offer the researcher insightful analysis of the survey data to create
plans or modify the survey design. Additionally, content analysis can help to spot any biases
in the survey data that might be corrected by changing the survey instrument or conducting
additional research.
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Qualitative Research Tool
A study on the effects of AI on supply chain management can analyze survey data
using the Content Analysis Toolkit, a qualitative data analysis tool. The goal of this toolkit is
to aid the researcher in accurately and quickly analyzing and interpreting the qualitative data
gathered during the survey. The researcher can classify and code the survey responses using
the Content Analysis Toolkit. The researcher gives each response a predetermined category or
“code” to achieve this. Additionally, the toolkit has features that let the researcher spot trends
and quickly spot recurrent themes in qualitative data. The toolkit is particularly helpful for
research on how AI affects supply chain management because it enables the researcher to
spot areas for growth and areas for improvement in the application of AI right away. The
Content Analysis Toolkit, in general, is a useful qualitative research tool that can quickly
analyze survey data in a study of the effects of AI on supply chain management. The
qualitative data enables the researcher to quickly and accurately spot recurring themes,
patterns, outliers, and biased areas.
How to establish reliability and validity?
The survey questions will be pre-tested with a small sample of supply management
professionals to establish reliability and validity. The Cronbach’s alpha coefficient will
evaluate the survey’s reliability. The focus group will be recorded, transcribed, and reviewed
by several researchers to make sure that the themes and patterns found are consistent to
ensure accuracy.
Sample Survey Questions
1. For how long have you been working in Supply Chain Management?
2. What is your experience with AI technologies and their relations to Supply Chain
Management?
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3. Name the impacts of using AI in Supply Chain Management.
4. In what ways has AI affected the cost-effectiveness of Supply Chain Management
Operations in your workplace?
5. Are there some areas in your workplace that could be bolstered by using AI?
6. What are examples of strategies that could be used to optimize Supply Chain
Management with AI?
7. What are the various ways AI could be utilized to increase the efficiency of Supply
Chain Management?
8. What knowledge and expertise are needed for supply management professionals to
use AI effectively?
9. What challenges have you faced in your area of work while implementing AI in
Supply Chain Management?
10. Are you of the opinion that AI is necessary for supply managers to stay competitive?
11. How can AI be used to lessen the possibility of supply chain disruptions?
12. What other AI innovations in supply management would you like to see?
Differences in using a survey versus secondary data
Surveys are a popular tool for gathering data in social science research. They involve
obtaining information from a sample of participants by directly posing questions; this can be
done in person, over the phone, or online (Sullivan & Wamba, 2022). In contrast to surveys,
secondary data refer to information already gathered by others, such as census data, business
financial reports, and scholarly articles. Both internal and external sources, including
databases, governmental organizations, or scholarly journals, can be used to obtain secondary
data. The specific research question and the accessibility of relevant data sources in the
scenario, as mentioned above, will determine whether to use secondary data or a survey. A
survey might be the best choice, for instance, if the research question involves examining the
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views and experiences of supply chain managers regarding the implementation of AI.
Secondary data may be more appropriate if the research question is focused on identifying
trends or patterns in the industry’s adoption of AI.
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References
Ali, A., Udin, Z. B. M., & Abualrejal, H. M. E. (2022, October). The Impact of Artificial
Intelligence and Supply Chain Resilience on the Company’s Supply Chains
Performance: The Moderating Role of Supply Chain Dynamism. In International
Conference on Information Systems and Intelligent Applications: ICISIA 2022 (pp.
17-28). Cham: Springer International Publishing.
Modgil, S., Singh, R. K., & Hannibal, C. (2022). Artificial intelligence for supply chain
resilience: learning from Covid-19. The International Journal of Logistics
Management, 33(4), 1246–1268.
Sullivan, Y., & Wamba, S. (2022, January). Artificial intelligence, strong resilience to supply
chain disruptions, and firm performance. In Proceedings of the 55th Hawaii
International Conference on System Sciences.
Please use this topic and articles and add more for research.
Investigate the efficacy of behavioral analytics in identifying and mitigating sophisticated threats in
BI systems.
Articles:
ENHANCING CYBERSECURITY: PROTECTING DATA IN THE DIGITAL AGE. (2024). Innovations in Science
and Technologies, 1(1), 40-49. https://innoist.uz/index.php/ist/article/view/153
This paper highlights the vital role of cybersecurity in protecting data in today’s digital world. As
reliance on digital tech grows, the threat of cyber attacks also rises, challenging both organizations
and individuals. Enhanced cybersecurity is essential to guard against data breaches, unauthorized
access, and other risks. The paper examines the progression of cyber threats like malware, phishing,
and ransomware, outlining their repercussions such as financial harm, reputational damage, and
privacy violations. It also presents effective strategies for bolstering cybersecurity, including robust
encryption, multifactor authentication, regular security assessments, and employee training.
https://www.researchgate.net/profile/Vikram-Singh63/publication/345759768_Identification_of_Security_Threats_in_Business_Intelligence_Environment
/links/5face0ba45851507810d3e8f/Identification-of-Security-Threats-in-Business-IntelligenceEnvironment.pdf
Business intelligence (BI) systems provide planners and decision-makers with crucial internal and
competitive information to enhance business processes and inform strategic decisions. These
systems also foster an organizational culture of information security, which is increasingly important
due to the rise of online business platforms. As top management prioritizes information security,
companies in under-developed countries may still rely on outdated methods. This study examines BI
environments and their security features, discussing role-based security architecture and objectoriented security models. It also highlights security methods in intelligent BI technologies to
strengthen the overall security of the BI environment.
Mitigating Risk: Analysis of Security Information and Event Management
https://www.igiglobal.com/viewtitlesample.aspx?id=53869&ptid=47907&t=mitigating+risk%3a+analysis+of+security
+information+and+event+management
Business Intelligence (BI) tools aid strategic planning by analyzing sales, stock, and customer trends.
Recently, BI methods have been applied to Security Information and Event Management (SIEM),
providing a centralized hub for real-time and historical event data analysis to enhance security and
optimize IT resources.
Data privacy and security in business intelligence and analytics
https://upcommons.upc.edu/handle/2117/117033
The rise of web applications has led to Big Data, presenting valuable economic and scientific
opportunities but also challenges in storage and analysis. Big Data Analytics helps uncover patterns
and correlations, aiding decision-making in business intelligence. However, the extensive use of Big
Data raises security and privacy concerns. Major issues include overcollection in mobile apps, data
misuse, and multi-source data analysis, making traditional privacy methods inadequate.
Distinguishing sensitive information and preventing user reidentification pose challenges. This paper
explores solutions for securing Big Data and maintaining privacy, focusing on healthcare and web
analytics, and compares methods for preserving user data privacy in today’s applications.
A survey on technical threat intelligence in the age of sophisticated cyber attacks
https://doi.org/10.1016/j.cose.2017.09.001
Traditional security approaches struggle against evolving cyber threats, which are evasive and
complex. Organizations must gather real-time cyber threat information and transform it into threat
intelligence (TI) for effective prevention and disaster recovery. Despite increased adoption, there is
confusion about TI’s definition and application. Our paper classifies different types of threat
intelligence, focusing on Technical Threat Intelligence (TTI) issues, emerging trends, and standards.
We address the hesitation among organizations to share TI and propose sharing strategies based on
trust and anonymity to minimize business risks. We emphasize the importance of standardized
threat information for improved TTI quality and better automated analytics.
BUSINESS INTELLIGENCE: AN INTEGRATED APPROACH
Business intelligence (BI) systems integrate operational and historical data with analytical tools to
provide valuable insights for business planners and decision-makers. BI aims to enhance the quality
and timeliness of information, helping managers understand their firm’s position relative to
competitors. BI technologies aid in analyzing market trends, customer behavior, and company
capabilities to support strategic adjustments. Advances in data warehousing, data cleansing,
hardware, software, and web architecture contribute to a richer BI environment. This paper
proposes a framework for building an effective BI system.
The role of compatibility in predicting business intelligence and analytics use intentions
https://doi.org/10.1016/j.ijinfomgt.2018.08.017
Cybersecurity of Business Intelligence Analytics Based on the Processing of Large Sets of
Information with the Use of Sentiment Analysis and Big Data
http://dx.doi.org/10.35808/ersj/2631
The research aims to characterize cybersecurity solutions for Business Intelligence (BI) analytics
using sentiment analysis and Big Data. The study hypothesizes that current regulations and security
measures for BI analytics struggle to meet growing challenges due to legal and market demands,
impacting data protection. The research uses legal, comparative, structural, and functional analyses.
It finds that the increasing importance of the Internet, including IoT and IoE, poses greater
cybersecurity challenges due to the sensitivity of the data collected. Industry 4.0 trends require
businesses to be more customer-oriented and operate on larger scales. The study offers
recommendations to improve BI cybersecurity and presents a comprehensive analysis of
cybersecurity in BI analytics using Big Data and sentiment analysis.
Leveraging Business Intelligence to Enhance Cyber Security Innovation
This study investigates the role of business intelligence (BI) in advancing cybersecurity innovation
through a systematic literature review (SLR). It reveals that leveraging BI significantly enhances
cybersecurity, providing valuable insights for policymakers and top-level managers across
industries. The research contributes to existing literature by examining BI and cybersecurity through
an SLR. However, the study is limited to a qualitative approach, which may impact the
generalizability of its findings.
I have also attached a pdf of an article I found on google scholar as well.
These are all the potential articles I ill be looking at along with many more but these will be the buzz
words used to select the articles.
Thanks,