The Impact of AI on Identity and Access Management
Keywords:
AI in Identity and Access Management, Identity ManagementAbstract
The rise of artificial intelligence (AI) is reshaping identity and access management (IAM), enhancing security, scalability, and adaptability in ways previously thought unattainable. Traditional IAM systems, often reliant on static policies and role-based access, struggle to keep up with the dynamic threats and sophisticated user behaviors in today’s digital landscape. AI-driven IAM introduces intelligent automation, anomaly detection, and real-time response, enabling organizations to safeguard sensitive information more effectively while minimizing friction for legitimate users. By integrating machine learning algorithms, IAM systems can analyze vast data streams to detect patterns and anomalies, proactively adjusting permissions and identifying potential security risks with minimal human intervention. Furthermore, AI empowers predictive analytics in IAM, foreseeing and mitigating risks before they escalate into security incidents. Biometric authentication, behavioral analysis, and continuous monitoring have become more precise with AI, allowing organizations to implement adaptive authentication measures that align with user behaviors and risk levels. This adaptive approach to IAM not only improves security but also enhances user experience by reducing redundant checks and verifications. However, implementing AI within IAM is not without challenges, such as ensuring data privacy, ethical considerations, and preventing biases in algorithmic decision-making. As organizations continue to adopt AI-enhanced IAM solutions, they must navigate these complexities to balance security, user convenience, and ethical responsibility. This shift in IAM represents a critical evolution, where AI is not merely a tool but an integral component in building resilient and forward-looking access management systems for the modern digital enterprise.
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Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, 17, 1-9.
Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and practice in technology enhanced learning, 12(1), 22.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and machines, 28, 689-707.
Sandhu, R. S. (1993). Lattice-based access control models. Computer, 26(11), 9-19.
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., ... & Zhavoronkov, A. (2017). Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 9(5), 5665.
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., ... & Friedman, P. A. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet, 394(10201), 861-867.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International Journal of Educational Technology in Higher Education, 16(1), 1-27.
Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A survey on security and privacy issues in Internet-of-Things. IEEE Internet of things Journal, 4(5), 1250-1258.
Ashforth, B. E., & Johnson, S. A. (2014). Which hat to wear?: The relative salience of multiple identities in organizational contexts. In Social identity processes in organizational contexts (pp. 31-48). Psychology Press.
Samarati, P., & De Vimercati, S. C. (2000). Access control: Policies, models, and mechanisms. In International school on foundations of security analysis and design (pp. 137-196). Berlin, Heidelberg: Springer Berlin Heidelberg.
O’Leary, D. E., & O’Keefe, R. M. (1997). The impact of artificial intelligence in accounting work: Expert systems use in auditing and tax. Ai & Society, 11, 36-47.
Lawrence, T. (1991). Impacts of artificial intelligence on organizational decision making. Journal of Behavioral Decision Making, 4(3), 195-214.
Devedžić, V. (2004). Web intelligence and artificial intelligence in education. Journal of Educational Technology & Society, 7(4), 29-39.
Stephanopoulos, G. (1990). Artificial intelligence in process engineering—current state and future trends. Computers & Chemical Engineering, 14(11), 1259-1270.
Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
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).
Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
Naresh Dulam. DataOps: Streamlining Data Management for Big Data and Analytics . Distributed Learning and Broad Applications in Scientific Research, vol. 2, Oct. 2016, pp. 28-50
Muneer Ahmed Salamkar, and Karthik Allam. Architecting Data Pipelines: Best Practices for Designing Resilient, Scalable, and Efficient Data Pipelines. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
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
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
Naresh Dulam. Apache Spark: The Future Beyond MapReduce. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Dec. 2015, pp. 136-5
Naresh Dulam. NoSQL Vs SQL: Which Database Type Is Right for Big Data?. Distributed Learning and Broad Applications in Scientific Research, vol. 1, May 2015, pp. 115-3
Naresh Dulam. Data Lakes: Building Flexible Architectures for Big Data Storage. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Oct. 2015, pp. 95-114
Naresh Dulam. The Rise of Kubernetes: Managing Containers in Distributed Systems. Distributed Learning and Broad Applications in Scientific Research, vol. 1, July 2015, pp. 73-94
Naresh Dulam. Snowflake: A New Era of Cloud Data Warehousing. Distributed Learning and Broad Applications in Scientific Research, vol. 1, Apr. 2015, pp. 49-72
Sarbaree Mishra. A Distributed Training Approach to Scale Deep Learning to Massive Datasets. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
Sarbaree Mishra, et al. Training Models for the Enterprise - A Privacy Preserving Approach. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
Sarbaree Mishra. Distributed Data Warehouses - An Alternative Approach to Highly Performant Data Warehouses. Distributed Learning and Broad Applications in Scientific Research, vol. 5, May 2019
Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
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
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