In my previous articles, I looked at MFA fatigue, ways to lower the risk of unauthorized access, and how to understand who has access to what. In this article, I wanted to look at the next step: making an Identity and Access Management program smarter through automation and better use of data.
As organizations add more applications, devices, and users, manual processes become harder to maintain. They are slower, easier to get wrong, and difficult to scale consistently.
Automation can help remove some of that manual work. Intelligence and better data can help us make more informed access decisions and identify problems earlier.
Action Items for Implementing Automation and Intelligence
Automate Account Provisioning and Deprovisioning
One of the first areas I would automate is account provisioning and deprovisioning.
When someone joins the organization, they should receive the access they need without relying on a long chain of manual requests. When they leave or change roles, that access should be removed or updated just as reliably.
Automating this process reduces the chance of accounts being forgotten and helps limit privilege creep over time.
The important part is not simply creating accounts faster. The automation also needs to handle changes correctly. A user moving between teams, becoming a manager, going on leave, or leaving the organization should trigger the appropriate access changes throughout the systems connected to the IAM program.
Integrate IAM With HR Systems
HR systems are often the best source for employment status and other information about a person's relationship with the organization.
Connecting IAM processes to that source makes it possible to drive the account lifecycle from changes that are already happening in HR.
For example:
- A new employee record can trigger account creation and initial access.
- A department or role change can trigger updates to group membership and application access.
- A termination can trigger account suspension and deprovisioning.
This reduces the number of manual handoffs between teams and gives the IAM program a more reliable source for lifecycle events.
The quality of the automation still depends on the quality of the source data. If job roles, managers, departments, or employment status are inconsistent, those problems will eventually show up in the access decisions built on top of them.
Implement Risk-Based Authentication
Not every authentication attempt carries the same level of risk.
A user signing in from a known managed device during their normal working pattern is different from the same account suddenly signing in from an unfamiliar device or location.
Risk-based authentication lets us consider more of that context before deciding what authentication is required.
Some of the signals I would look at include:
- User location
- Device state
- Sign-in behaviour
- Application sensitivity
- Authentication method
The goal is not to make every unusual event an automatic denial. It is to use the additional context to decide when stronger authentication, another verification step, or a block makes sense.
Used carefully, this can improve security without creating unnecessary friction for normal user activity.
Use Machine Learning and AI
Machine learning and AI can help analyze user behaviour and identify patterns that would be difficult to review manually at scale.
For example, they can help surface unusual authentication activity, unexpected changes in access patterns, or other behaviour that may deserve investigation.
I would treat those signals as additional context rather than a final access decision on their own.
An unusual event does not automatically mean an account has been compromised, and normal behaviour for one user may look completely different for another. The value comes from using the signal to focus attention where it is most useful.
As these tools improve, I expect them to become increasingly useful for helping IAM teams understand larger and more complex environments.
Monitor and Analyze IAM Metrics
Automation gives us another advantage: better data about how the IAM program is actually operating.
I would track metrics such as:
- Authentication attempts and failures
- Access requests
- Provisioning and deprovisioning activity
- Policy violations
- Manual exceptions
- Accounts that fail lifecycle automation
Looking at these over time can help identify where a process is unreliable or where users are consistently running into the same problem.
It also gives the IAM team a way to show whether controls are actually improving instead of relying only on whether a process exists on paper.
The exact metrics will depend on the organization, but the important part is using the data to ask better questions about how the program is working.
Review and Update IAM Processes Regularly
Automation should not mean a process is finished forever.
Organizations change. Applications are added, teams reorganize, roles change, and new ways of working appear. An automated process that matched the business a year ago may no longer match it today.
I would regularly review both the access rules and the processes behind them.
That includes checking whether:
- Application access still matches current job responsibilities
- Provisioning and deprovisioning cover all known entry points
- Exceptions are still required
- Risk signals are producing useful outcomes
- Manual work has started appearing around the automated process
Those manual workarounds are often a useful signal. They may mean the automation no longer reflects how people actually work.
Final Thoughts
Automation and intelligence can make an IAM program faster and more consistent, but they are not a replacement for understanding the underlying access model.
I still need to know who the users are, which resources they should be able to access, and what should happen when their relationship with the organization changes.
Once those foundations are understood, automation can remove repetitive work and reduce human error. Better data and intelligent signals can then help us find problems earlier and make more informed decisions.
The next articles in this series will go deeper into some of the technologies and approaches that can help build those capabilities into an IAM program.