Negotiating Data Privacy Policies Using Powerful IAM Policies

Authors

  • Sairamesh Konidala Vice President at JPMorgan & Chase, USA Author
  • Jeevan Manda Project Manager at Metanoia Solutions Inc, USA Author
  • Kishore Gade Vice President, Lead Software Engineer at JP Morgan Chase, USA Author

Keywords:

Identity and Access Management (IAM), data privacy, compliance, GDPR

Abstract

Modern digital environments place strict demands on businesses to safeguards the personal
information on data privacy laws like GDPR, CCPA & HIPAA. Data privacy rules are
becoming increasingly complicated; hence companies face two challenges maintaining
compliances & protecting access to the private data. Strong IAM policies are not just a
strategic necessity but also an operational one given the significant financial and reputational
risks linked with data breaches and regulatory infractions. Emphasizing best practices that
help businesses to control data access while maintaining productivity and user experience,
this article presents a pragmatic approach for matching IAM with privacy requirements.
Combining IAM ideas with real-world case studies shows how businesses may use these
tools to create a strong privacy framework that adapts to changing regulatory surroundings,
therefore supporting sustainable growth and improving customer confidence.

References

1. Spiekermann, S., Grossklags, J., & Berendt, B. (2001, October). E-privacy in 2nd

generation E-commerce: privacy preferences versus actual behavior. In Proceedings of the

3rd ACM conference on Electronic Commerce (pp. 38-47).

2. Cooley, R., Mobasher, B., & Srivastava, J. (1999). Data preparation for mining world wide

web browsing patterns. Knowledge and information systems, 1, 5-32.

3. Teraguchi, N. C. R. L. Y., & Mitchell, J. C. (2004). Client-side defense against web-based

identity theft. Computer Science Department, Stanford University. Available: http://crypto.

stanford. edu/SpoofGuard/webspoof. Pdf.

4. Kulyukin, V., Gharpure, C., Nicholson, J., & Pavithran, S. (2004, September). RFID in

robot-assisted indoor navigation for the visually impaired. In 2004 IEEE/RSJ International

Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566) (Vol. 2,

pp. 1979-1984). IEEE.

5. Fleming, L., & Sorenson, O. (2003). Navigating the technology landscape of innovation.

MIT Sloan Management Review, 44(2), 15.

6. Kang, J. (1997). Information privacy in cyberspace transactions. Stan. L. Rev., 50, 1193.

7. Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online

shoppers using in-store navigational clickstream. Journal of consumer psychology, 13(1-2),

29-39..

8. Shapiro, C. (2000). Navigating the patent thicket: Cross licenses, patent pools, and

standard setting. Innovation policy and the economy, 1, 119-150.

9. Parimi, S. S. (2018). Optimizing Financial Reporting and Compliance in SAP with Machine

Learning Techniques. Available at SSRN 4934911.

10. Syed, F. M., & ES, F. K. (2020). IAM and Privileged Access Management (PAM) in

Healthcare Security Operations. Revista de Inteligencia Artificial en Medicina, 11(1), 257-

278.

African Journal of Artificial Intelligence and Sustainable Development

By African Science Group, South Africa 16

African Journal of Artificial Intelligence and Sustainable Development

Volume 1 Issue 1

Semi Annual Edition | Jan - June, 2021

This work is licensed under CC BY-NC-SA 4.0.

11. Sebastian, I. M., Ross, J. W., Beath, C., Mocker, M., Moloney, K. G., & Fonstad, N. O.

(2020). How big old companies navigate digital transformation. In Strategic information

management (pp. 133-150). Routledge.

12. Cohen, J. E. (2012). What privacy is for. Harv. L. Rev., 126, 1904.

13. Taleb, N. N. (2010). The Black Swan:: The Impact of the Highly Improbable: With a new

section:" On Robustness and Fragility" (Vol. 2). Random house trade paperbacks.

14. Ghiglia, D. C., & Romero, L. A. (1994). Robust two-dimensional weighted and

unweighted phase unwrapping that uses fast transforms and iterative methods. JOSA A,

11(1), 107-117.

15. Karygiannis, T., & Owens, L. (2002). Wireless Network Security:. US Department of

Commerce, Technology Administration, National Institute of Standards and Technology.

16. Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data

Management. Innovative Computer Sciences Journal, 6(1).

17. Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ

Computing Journal, 1(1).

18. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud

Success. Innovative Computer Sciences Journal, 6(1).

19. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of

SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).

20. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL

Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).

21. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as

an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative

Computer Sciences Journal, 6(1).

22. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.

African Journal of Artificial Intelligence and Sustainable Development

By African Science Group, South Africa 17

African Journal of Artificial Intelligence and Sustainable Development

Volume 1 Issue 1

Semi Annual Edition | Jan - June, 2021

This work is licensed under CC BY-NC-SA 4.0.

23. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE

RELIABILITY WITH ACID TRANSACTIONS.

24. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive

Strategies for Secure Online Account Opening.

25. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking

App Design and Functionality to Boost User Engagement and Satisfaction.

26. Thumburu, S. K. R. (2020). Integrating SAP with EDI: Strategies and Insights. MZ

Computing Journal, 1(1).

27. Thumburu, S. K. R. (2020). Interfacing Legacy Systems with Modern EDI Solutions:

Strategies and Techniques. MZ Computing Journal, 1(1).

28. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative

Computer Sciences Journal, 4(1).

29. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End

Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative

Computer Sciences Journal, 5(1).

30. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best

Practices. Innovative Computer Sciences Journal, 5(1).

31. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed

Learning and Broad Applications in Scientific Research, vol. 6, July 2020

32. Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks

Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in

Scientific Research, vol. 5, July 2019

African Journal of Artificial Intelligence and Sustainable Development

By African Science Group, South Africa 18

African Journal of Artificial Intelligence and Sustainable Development

Volume 1 Issue 1

Semi Annual Edition | Jan - June, 2021

This work is licensed under CC BY-NC-SA 4.0.

33. Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses:

Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful

Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5,

Sept. 2019

34. Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing

Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and

Broad Applications in Scientific Research, vol. 5, Dec. 2019

35. Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of

Technologies, With Insights on Selecting the Right Approach for Specific Use Cases.

Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020

36. Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring

Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources.

Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020

37. Naresh Dulam. Machine Learning on Kubernetes: Scaling AI Workloads . Distributed

Learning and Broad Applications in Scientific Research, vol. 2, Sept. 2016, pp. 50-70

38. Naresh Dulam. Data Lakes Vs Data Warehouses: What’s Right for Your Business?.

Distributed Learning and Broad Applications in Scientific Research, vol. 2, Nov. 2016, pp.

71-94

39. Naresh Dulam, et al. Kubernetes Gains Traction: Orchestrating Data Workloads.

Distributed Learning and Broad Applications in Scientific Research, vol. 3, May 2017, pp. 69-

93

40. Naresh Dulam, et al. Apache Arrow: Optimizing Data Interchange in Big Data Systems.

Distributed Learning and Broad Applications in Scientific Research, vol. 3, Oct. 2017, pp. 93-

114

41. Naresh Dulam, and Venkataramana Gosukonda. Event-Driven Architectures With

Apache Kafka and Kubernetes. Distributed Learning and Broad Applications in Scientific

Research, vol. 3, Oct. 2017, pp. 115-36

African Journal of Artificial Intelligence and Sustainable Development

By African Science Group, South Africa 19

African Journal of Artificial Intelligence and Sustainable Development

Volume 1 Issue 1

Semi Annual Edition | Jan - June, 2021

This work is licensed under CC BY-NC-SA 4.0.

42. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation

Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June

2019

43. Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative

Adversarial Network Training. Distributed Learning and Broad Applications in Scientific

Research, vol. 5, Sept. 2019

44. Sarbaree Mishra. “Moving Data Warehousing and Analytics to the Cloud to Improve

Scalability, Performance and Cost-Efficiency”. Distributed Learning and Broad Applications

in Scientific Research, vol. 6, Feb. 2020

45. Sarbaree Mishra, et al. “Training AI Models on Sensitive Data - the Federated Learning

Approach”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr.

2020

46. Sarbaree Mishra. “Automating the Data Integration and ETL Pipelines through Machine

Learning to Handle Massive Datasets in the Enterprise”. Distributed Learning and Broad

Applications in Scientific Research, vol. 6, June 2020

Downloads

Published

03-05-2021

How to Cite

[1]
Sairamesh Konidala, Jeevan Manda, and Kishore Gade, “Negotiating Data Privacy Policies Using Powerful IAM Policies”, African J. of Artificial Int. and Sust. Dev., vol. 1, no. 1, pp. 373–392, May 2021, Accessed: Apr. 29, 2025. [Online]. Available: https://ajaisd.org/index.php/publication/article/view/45