While identity governance and administration (IGA) have always been essential, it has generally been a tedious, manual, and reactive function for most organizations. Access reviews would be performed once per quarter; provisioning requests would take days to get approved; and lines of evidence would come together through spreadsheets. This way of operating will no longer suffice.
The IT environment today consists of on-premises systems, cloud infrastructure, SaaS applications, and an increasing number of machine identities. To manage all of these different components ineffectively means putting your organization at risk, both from a security and operational perspective.
Therefore, through both AI and automation, the way IGA works has fundamentally altered the role of IGA from merely being a compliance checkpoint to being an active, intelligent layer of defense to the organization.
From Human Workflows to Artificial Intelligence Automation
Traditional IGA relied heavily on human judgment and manual effort. This included provisions of accounts, periodic access reviews, following up on approvals via an email chain, etc. The overall process was very time-consuming, extremely error-prone, and was unable to scale up to the demands of business today.
AI changes the picture by applying machine learning to decision-making process. For example, rather than asking a manager to review hundreds of access entitlements at once, a machine algorithm evaluates user activity, user activity in the context of their peer group, and access to company resources over prior time frames.
It then sends back recommendations for manager approval to accelerate the approval process for all approved access entitlements and flag any inconsistencies.
According to studies, AI-powered IGA systems result in an average of 52% decrease in time required for conducting access reviews, as well as 300% greater numbers of identified high-risk access issues such as segregation of duties violations and toxic access combinations.
With automation handling the execution, access workflows will happen automatically when an employee joins, changes positions, or leaves the organization. This will happen without any ticket requests and without any delays. Depending on predefined policies, access will be granted or revoked in real-time.
IGA Before vs. After AI and Automation
Areas Where AI is Making the Biggest Impact in IGA
Intelligent Access Reviews
Historically, managers completing access certifications (reviewing users’ entitlements) have found it difficult because there is too much information with too little context, and the pressure to approve items just to get through the queue.
AI mitigates this problem by only presenting high-risk items that truly require a human’s attention, while comparing users’ access with their peers, flagging outliers, and then recommending based on usage data. Reviewers only spend time on the decisions that actually count, versus approving hundreds of entitlements they would label as low risk.
Automated Provisioning and Deprovisioning
Every joiner, mover, or leaver event is a potential security gap if not handled correctly. Artificial Intelligence-driven Identity Governance Administration (IGA) Platforms are suited directly to HR systems. This is the only authoritative source of truth for identity governance; they can make the identity lifecycle happen in real time.
Tools like Hire2Retire take this further by bridging HR platforms with Active Directory, Entra ID, and other identity systems to automate the creation of user accounts, assign role-related access, and retire the access immediately upon a user leaving the organization. This eliminates the time between when an account is no longer needed and the time the orphan account and superfluous accesses are cleaned up.
Management of Machine Identity
It is a common issue for most Identity Governance and Administration (IGA) initiatives to stay up to date with the increased number of non-human identities within enterprise networks. Non-human identities include but are not limited to bots, artificial intelligence agents, service accounts, application programming interface (API) keys, and certificates. There is no governance structure in place for access to systems, data, and workflows performed by these types of entities.
Traditional IAM solutions were built for static, pre-determined identity lifecycles within predictable environments, and for managing human identities. AI agents differ from traditional IAM: they are short-lived, operate in many environments, and obtain permissions dynamically. As a result, AI-driven IGA solutions now apply similar discovery, classification, and governance to non-human accounts as to human ones.
Continuous Compliance and Audit Readiness
Regulatory requirements (GDPR, SOX, and HIPAA) require organizations to demonstrate who has access to what; the reason(s) they had access; and the time it takes to rescind access when circumstances change. Collecting documentation for audits manually creates an environment of time-intensive and error-prone activities.
Automation supports and maintains continuous compliance. Every action related to provisioning, modifying access, or making policy exceptions is automatically documented and maintained. Audit reports can be generated at any time. When regulatory agencies request identity-related audit documentation, it is ready, preventing any last-minute rush.
AI-Driven IGA and Regulatory Compliance
Anomaly Detection and Entitlement Sprawl
In terms of identity-related risk, entitlement sprawl is one of the riskiest forms, which occurs when users accumulate too many access rights and/or entitlements that are no longer needed. Accumulation of entitlement sprawl is typically accomplished through the performance of a role of a user changing, changes/approvals given during project assignment(s), and the rapid granting/performing of entitlements without cleaning them up.
ML algorithms monitor access behavior over time to check and surface entitlement sprawl before it becomes a threat. These algorithms identify accounts that have access rights that do not align with the user's current employment status or role.
They will also identify accounts that have been inactive for an extended period and develop reporting capabilities of unusual authentication patterns to identify potential credential compromise. These capabilities move the identity risk mitigation activities from a reactive to a proactive format.
What Modern IGA Actually Looks Like
An IGA is not simply a standalone product, but an architectural model, integrating numerous integrated components such as human resource information systems (HRIS), identity provisioning systems (IdP), cloud computing infrastructures, software-as-a-service (SaaS) applications, and internal/external security tools into a unified identity architecture and business process utilizing application programming interfaces (API). Here are the 5 pillars of a modern AI-driven IGA:
What IT Leaders Should Prioritize
If you are considering a new or modernised IGA program, the first thing you should focus on is automation maturity. Some questions to ask yourself: Is access provisioning still through a ticketing process? Does your access review process occur quarterly or in a scheduled manner? Are machine identities managed and governed?
Answering these types of questions is necessary for closing some of the gaps found in your current processes. Closing the gaps in your current processes with a tool will not be enough. You must also work toward connecting your identity data sources together, making HR the authoritative source for lifecycle events and providing quality, accurate data to your AI-based solutions. Garbage in and garbage out still applies here, as machine learning will only recommend based on quality data.
You can start with the use cases that have a significant impact and risk. Start with automated deprovisioning, role-based access control based on HR events, and continuously reviewing access to privileged accounts. You can build from the beginning with these types of use cases.
Final Thoughts
AI and automation are not only improving IGA but also making the traditional methods of IGA unviable. The sheer number of identities processed, the rapidity of workforce changes, and the complex and ever-evolving nature of the threats to the identity and access ecosystem have all exceeded what can be handled with manual processes.
Organizations using intelligent automation for their identity governance will be able to accelerate their operations’ pace and improve security while decreasing the time spent responding to audit requests. Without this type of intelligent automation, organizations will continue to rely on spreadsheets and a quarterly process of rubber-stamping, which exposes them to risk. While identity governance has always been important, the advent of artificial intelligence makes it effective.
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