AI-Driven Identity Governance Framework (AIGF)
Abstract
The rapid adoption of cloud computing, artificial intelligence, and distributed enterprise applications has increased the complexity of managing digital identities and access privileges. Traditional Identity and Access Management (IAM) approaches often rely on predefined rules and manual reviews, which can limit their ability to detect emerging risks and adapt to dynamic business requirements. The AI-Driven Identity Governance Framework (AIGF) proposes an intelligent, risk-based approach to identity lifecycle management, access certification, segregation of duties, and privileged access governance.
By integrating machine learning, behavioral analytics, and automated decision-making, AIGF aims to identify anomalous access patterns, recommend appropriate access permissions, detect excessive privileges, and support timely access reviews. The framework emphasizes continuous monitoring, explainable AI, data privacy, and human oversight to ensure that automated decisions remain transparent, accountable, and aligned with organizational security policies.
By combining AI capabilities with established Identity Governance and Administration (IGA) principles, AIGF seeks to improve operational efficiency, strengthen regulatory compliance, reduce identity-related security risks, and support proactive access governance across complex enterprise environments. The proposed framework provides a foundation for organizations to transition from reactive, rule-based identity governance toward adaptive, predictive, and continuously optimized identity security.
- Published in
- Proceedings of the Raptors Conference, Vol. 1 (2026)
- Conference
- Raptors Conference 2026, 21 October 2026, online
- Accepted
- 2026-10-16
- DOI
- https://doi.org/10.68495/rc.2026.C067
- Presentation
- Slides (PDF)
- Licence
- © 2026Neha Asthana. Published under Creative Commons Attribution 4.0 International (CC BY 4.0).
- Publisher
- Hackathon Raptors, Community Interest Company 15557917, London, United Kingdom
- Permanent address
- https://proceedings.raptors.dev/asthana-ai-driven-identity-governance
Open access. Every entry in the Proceedings is free to read, with no charge to authors and no charge to readers. Slides are served from this site, so they stay available at the paper's permanent address.
Cite this paper
Neha Asthana (2026). AI-Driven Identity Governance Framework (AIGF). Proceedings of the Raptors Conference, Vol. 1 (2026). Hackathon Raptors, London. https://doi.org/10.68495/rc.2026.C067
@inproceedings{raptors2026-c067,
title = {{AI-Driven Identity Governance Framework (AIGF)}},
author = {Asthana, Neha},
booktitle = {Proceedings of the Raptors Conference},
volume = {1},
year = {2026},
publisher = {Hackathon Raptors},
address = {London, United Kingdom},
doi = {10.68495/rc.2026.C067},
url = {https://doi.org/10.68495/rc.2026.C067},
abstract = {The rapid adoption of cloud computing, artificial intelligence, and distributed enterprise applications has increased the complexity of managing digital identities and access privileges. Traditional Identity and Access Management (IAM) approaches often rely on predefined rules and manual reviews, which can limit their ability to detect emerging risks and adapt to dynamic business requirements. The AI-Driven Identity Governance Framework (AIGF) proposes an intelligent, risk-based approach to identity lifecycle management, access certification, segregation of duties, and privileged access governance. By integrating machine learning, behavioral analytics, and automated decision-making, AIGF aims to identify anomalous access patterns, recommend appropriate access permissions, detect excessive privileges, and support timely access reviews. The framework emphasizes continuous monitoring, explainable AI, data privacy, and human oversight to ensure that automated decisions remain transparent, accountable, and aligned with organizational security policies. By combining AI capabilities with established Identity Governance and Administration (IGA) principles, AIGF seeks to improve operational efficiency, strengthen regulatory compliance, reduce identity-related security risks, and support proactive access governance across complex enterprise environments. The proposed framework provides a foundation for organizations to transition from reactive, rule-based identity governance toward adaptive, predictive, and continuously optimized identity security.}
}
TY - CONF TI - AI-Driven Identity Governance Framework (AIGF) AU - Asthana, Neha T2 - Proceedings of the Raptors Conference VL - 1 PY - 2026 DA - 2026/10/16 PB - Hackathon Raptors CY - London, United Kingdom DO - 10.68495/rc.2026.C067 UR - https://doi.org/10.68495/rc.2026.C067 AB - The rapid adoption of cloud computing, artificial intelligence, and distributed enterprise applications has increased the complexity of managing digital identities and access privileges. Traditional Identity and Access Management (IAM) approaches often rely on predefined rules and manual reviews, which can limit their ability to detect emerging risks and adapt to dynamic business requirements. The AI-Driven Identity Governance Framework (AIGF) proposes an intelligent, risk-based approach to identity lifecycle management, access certification, segregation of duties, and privileged access governance. By integrating machine learning, behavioral analytics, and automated decision-making, AIGF aims to identify anomalous access patterns, recommend appropriate access permissions, detect excessive privileges, and support timely access reviews. The framework emphasizes continuous monitoring, explainable AI, data privacy, and human oversight to ensure that automated decisions remain transparent, accountable, and aligned with organizational security policies. By combining AI capabilities with established Identity Governance and Administration (IGA) principles, AIGF seeks to improve operational efficiency, strengthen regulatory compliance, reduce identity-related security risks, and support proactive access governance across complex enterprise environments. The proposed framework provides a foundation for organizations to transition from reactive, rule-based identity governance toward adaptive, predictive, and continuously optimized identity security. LA - en ER -