Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Choosing the Right IAM Tool for Your Business Needs

PDF
  • Sairamesh Konidala,
  • Guruprasad Nookala
Sairamesh Konidala
Vice President at JPMorgan & Chase, USA
Guruprasad Nookala
Software Engineer III at JP Morgan Chase LTD, USA
Cover

Published 05-09-2022

Keywords

  • Identity and Access Management (IAM),
  • IAM tool selection
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Sairamesh Konidala and Guruprasad Nookala, “Choosing the Right IAM Tool for Your Business Needs”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 343–365, Sep. 2022, Accessed: Jan. 02, 2025. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/264

Abstract

Abstract:
Choosing the correct Identity and Access Management (IAM) tool is essential for businesses seeking to secure digital assets, ensure regulatory compliance, and streamline user access. As organizations grow, the complexity of managing identities and permissions also expands, making a robust IAM solution vital. This abstract explores the essential factors organizations should consider when selecting an IAM tool that aligns with their needs, including scalability, integration capabilities, user experience, and security features. It discusses how modern IAM solutions can offer features like role-based access control (RBAC), multi-factor authentication (MFA), and single sign-on (SSO), which not only enhance security but also improve operational efficiency. Additionally, the abstract examines the importance of adaptability in an IAM tool, as businesses must often integrate with various applications and systems within evolving digital environments. Selecting an IAM solution tailored to a company’s size, industry, and risk profile can significantly impact its security posture and user experience. This abstract aims to guide organizations in making an informed choice by addressing the diverse options and features available in IAM tools, focusing on how the proper selection can support regulatory compliance, reduce administrative burden, and foster a seamless access experience for employees. By prioritizing security, compliance, and ease of use, companies can choose an IAM tool that meets today’s requirements and is flexible enough to adapt to future demands.

PDF

Downloads

Download data is not yet available.

References

  1. Mohammed, K. H., Hassan, A., & Yusuf Mohammed, D. (2018). Identity and access management system: a web-based approach for an enterprise.
  2. Dyche, J. (2002). The CRM handbook: A business guide to customer relationship management. Addison-Wesley Professional.
  3. Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers (Vol. 1). John Wiley & Sons.
  4. Rigby, D., & Bilodeau, B. (2011). Management tools & trends 2013. London: Bain & Company.
  5. McAfee, A. (2009). Enterprise 2.0: New collaborative tools for your organization's toughest challenges. Harvard Business Press.
  6. Veil, S. R., Buehner, T., & Palenchar, M. J. (2011). A work‐in‐process literature review: Incorporating social media in risk and crisis communication. Journal of contingencies and crisis management, 19(2), 110-122.
  7. Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114-123.
  8. Fowler, M. (2010). Domain-specific languages. Pearson Education.
  9. Kahaner, L. (1997). Competitive intelligence: how to gather analyze and use information to move your business to the top. Simon and Schuster.
  10. Goldratt, E. M. (2017). Critical chain: A business novel. Routledge.
  11. Kegan, R., & Lahey, L. L. (2009). Immunity to change: How to overcome it and unlock the potential in yourself and your organization. Harvard Business Press.
  12. Flint, D. J., Woodruff, R. B., & Gardial, S. F. (2002). Exploring the phenomenon of customers' desired value change in a business-to-business context. Journal of marketing, 66(4), 102-117.
  13. Hubbard, D. W. (2014). How to measure anything: Finding the value of intangibles in business. John Wiley & Sons.
  14. Casadesus‐Masanell, R., & Zhu, F. (2013). Business model innovation and competitive imitation: The case of sponsor‐based business models. Strategic management journal, 34(4), 464-482.
  15. Royse, D. D. (1991). Research methods in social work (Vol. 1210). Chicago: Nelson-Hall Publishers.
  16. Gade, K. R. (2021). Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data. MZ Computing Journal, 2(1).
  17. Gade, K. R. (2021). Data-Driven Decision Making in a Complex World. Journal of Computational Innovation, 1(1).
  18. Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).
  19. Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
  20. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
  21. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
  22. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
  23. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
  24. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
  25. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  26. Thumburu, S. K. R. (2021). EDI Migration and Legacy System Modernization: A Roadmap. Innovative Engineering Sciences Journal, 1(1).
  27. Thumburu, S. K. R. (2021). Data Analysis Best Practices for EDI Migration Success. MZ Computing Journal, 2(1).
  28. Thumburu, S. K. R. (2020). Interfacing Legacy Systems with Modern EDI Solutions: Strategies and Techniques. MZ Computing Journal, 1(1).
  29. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
  30. 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).
  31. Babulal Shaik. Automating Compliance in Amazon EKS Clusters With Custom Policies . Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Jan. 2021, pp. 587-10
  32. Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
  33. Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77
  34. 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
  35. 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
  36. Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
  37. Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
  38. Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
  39. Naresh Dulam, et al. Snowflake Vs Redshift: Which Cloud Data Warehouse Is Right for You? . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Oct. 2018, pp. 221-40
  40. Naresh Dulam, et al. Apache Iceberg: A New Table Format for Managing Data Lakes . Distributed Learning and Broad Applications in Scientific Research, vol. 4, Sept. 2018
  41. Naresh Dulam, et al. Data Governance and Compliance in the Age of Big Data. Distributed Learning and Broad Applications in Scientific Research, vol. 4, Nov. 2018
  42. Naresh Dulam, et al. “Kubernetes Operators: Automating Database Management in Big Data Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
  43. Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
  44. Sarbaree Mishra. “The Age of Explainable AI: Improving Trust and Transparency in AI Models”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 212-35
  45. Sarbaree Mishra, et al. “A New Pattern for Managing Massive Datasets in the Enterprise through Data Fabric and Data Mesh”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Dec. 2021, pp. 236-59
  46. Sarbaree Mishra. “Leveraging Cloud Object Storage Mechanisms for Analyzing Massive Datasets”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, Jan. 2021, pp. 286-0
  47. Sarbaree Mishra, et al. “A Domain Driven Data Architecture For Improving Data Quality In Distributed Datasets”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Aug. 2021, pp. 510-31
  48. Sarbaree Mishra. “Improving the Data Warehousing Toolkit through Low-Code No-Code”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, Oct. 2021, pp. 115-37

Similar Articles

You may also start an advanced similarity search for this article.