Monitoring up the Stack: User Activity Monitoring
The previous Monitoring up the Stack post examined Identity Monitoring, which is a set of processes to monitor events around provisioning and managing accounts. The Identity Monitor is typically blind to one very important aspect of accounts: how they are used at runtime. So you know who the user is, but not what they are doing. User Activity Monitoring addresses this gap through reporting not on how the accounts were created and updated in the directory, but by examining user actions on systems and applications, and linking them to assigned roles. Implementing User Activity Monitoring User Activity Monitors can be deployed to monitor access patterns and system usage. The collected data regarding how the system is being used, and by who, is then sent to the SIEM/Log Management system. This gives the SIEM/Log Management system data that is particularly helpful for attribution purposes. Implementing User Activity Monitoring rests on four key decisions. First, what constitutes a user? Next, what activities are worth monitoring? Third, what does typical activity look like, and how do we define policies to scope acceptable use? And finally, where and how should the monitor be deployed? The question about what constitutes a user seems simple, and on one level it is. Most likely a user is an account in the corporate or customer directory, such as Active Directory or LDAP. But sometimes there are accounts for non-human system users, such as service accounts and machine accounts. In many systems service accounts, machine accounts, and other forms of automated batch processing can do just as much damage as any other account/function. After all, these features were programmed and configured by humans, and are subject to misuse like any other accounts, so likely are worth monitoring as well. Drilling down further into users, how are they identified? To start with, there is probably a username. But remember the data that the User Activity Monitor sends to the SIEM/Log Management system is will be used after the fact. What user data will help a security analyst understand the user’s actions and whether they were malicious or harmful? Several data elements are useful for building a meaningful user record: Username: The basic identifier for a user in the system, including the namespace or other protocol-specific data. Identity Provider: The name of the directory or database that authenticated the user. Group/Role Membership: Any group or role information assigned to the user account, or other data used for authorization purposes. Attributes: Was the user account assigned any privileges or capabilities? Are there time of day or location attributes that are important for verifying user authenticity? Authentication Information: If available, information around how the user was authenticated can be helpful. Was the user dialed in from a remote location? Did they log in from the office? When did they log in? And so on. A log entry that reads user=rajpatel; is far less useful than one that contains “user=rajpatel; identityprovider=ExternalCORPLDAP; Group=Admin; Authenticated=OTP”. The more detailed the information around the user and their credential, the more precsion the analyst has to work with. Usually this data is easy to get at runtime – it is available in security tokens such as SAML and Kerberos – but the monitor must be configured to collect it. Now that we see how to identify a user, what activities are of interest to the SIEM/Log Management system? The types of activities mentioned in other Monitoring up the Stack posts can all be enriched through the user data model described above; in addition there are some user-specific events worth tracking, including: User Session Activities: events that create, use, and terminate sessions; such as login and logout events. Security Token Activities: events that issue, validate, exchange and terminate security tokens. System Activities: events based around system exceptions, startups, shutdowns, and availability issues. Platform Activities: events from specific ports or interfaces, such as USB drive access. Inter-Application Activities: events performed by more than one application on behalf of the user, all linked to the same business function. Now that we know what kind of events that we are looking for, what do we want to do with these events? If we are monitoring we need to specify policies to define appropriate use, and what should be done when an event – or in some cases a series of events – occurs. Policy set up and administration is a giant hurdle with SIEM systems today, and adding user activity monitoring – or any other form of monitoring – will require the same time to set up and adjust over time. Based on an event type listed above, you select the behavior type you want to monitor and define what users can & cannot do. User monitoring systems, at minimum, offer attribute-based analysis. More advanced systems offer heuristics and behavioral analysis; these provide flexibility in how users are monitored, and reduce false positives as the analysis adapts to user actions over time. The final step is deployment of the User Activity Monitor; and the logical place to start is the Identity repository because repositories can write auditable log events when they issue, validate, and terminate sessions and security tokens; thus the Identity repository can report to the SIEM/Log Management system on what users were issued what sessions and tokens. This location can be made more valuable by adding User Activity Monitors closer to the monitored resources, such as Web Application Firewalls and Web Access Managers. These systems can enhance visibility beyond simply what tokens and sessions were issued (from the Identity repository), adding information on how were they used and what the user accessed. Correlation: Putting the Data to Work With monitors situated to report on User Activity, the next step is to use the data. The data and event models described above provide an enriched model that enables the analyst to trace events back upstream. For example, the analyst can set up rules that identify known good and bad behavior patterns to reflect authorized usage and potentially malicious patterns. Authorized usage patterns generally reflect the use case flows that users follow. In most cases these do
