Identity analytics explained

At its core, identity analytics uses machine learning to correlate threat signals across network, cloud, SaaS, and identity systems in order to prioritize identities based on observed risk.

In security operations, identity activity is spread across many systems, while static identity records rarely show how privilege is exercised in real time. Rather than relying on assigned roles alone, this approach emphasizes observed privilege and cross-environment correlation.

What is identity analytics?

Identity analytics assesses identity risk based on observed behavior rather than static attributes like assigned roles or directory entries. This distinction is important because, during real attacks, how access is actually used often differs from how it appears on paper. By treating identities as active entities, identity analytics evaluates risk across cloud, network, SaaS, and identity systems instead of within isolated silos.

Clear boundaries also matter. When identity analytics is confused with adjacent identity practices, teams may rely on incomplete signals or misinterpret identity activity. 

Identity analytics is often mistaken for:

• Identity governance and access reviews, which focus on entitlement assignment rather than real-time behavior
  
• Authentication monitoring, which emphasizes login success or failure without broader activity context
  
• Static directory analysis, which reflects assigned privilege rather than observed privilege
  
• Manual threat hunting, which typically cannot scale to continuous analysis of thousands of identities
  

When identity risk is assessed primarily through entitlements, reviews, or login outcomes, critical behavioral signals can be missed. 

See why Identity and Access Management alone can’t stop modern attacks.

Why directory-based identity views fail to represent observed privilege

Assigned roles and group membership describe intended access, not actual behavior. This gap matters because attackers can abuse legitimate credentials without triggering changes in directory data. That’s why it’s important to shift risk assessment toward observed privilege and correlated activity instead of static records.

Static records, isolated events, and directory-driven identity assessment commonly fails in the following ways:

• Treating assigned privilege as equivalent to effective privilege, even when behavior indicates broader access
  
• Evaluating identity events in isolation instead of correlating signals into an identity-centric narrative
  
• Using event volume as a proxy for severity, increasing alert fatigue and misprioritization
  
• Forcing analysts to pivot across tools and queries to reconstruct identity context manually
  

The key analytical layers behind identity risk prioritization

Combining observed privilege, cross-domain correlation, and urgency scoring produces an identity-centric view of risk. This structure matters because identity compromise often spans identity providers, cloud services, SaaS applications, and networks. Treating these environments separately fragments context and obscures progression.

To unify telemetry and improve prioritization, identity analytics relies on a defined set of analytical layers. Each layer addresses a specific limitation of traditional identity monitoring, and removing any one weakens risk interpretation.

Identity analytics is built on the following layers:

• Observed privilege, which tracks real-time behavior and access patterns rather than assigned roles.
  
• Cross-domain correlation, which stitches identity activity across network, cloud, SaaS, and identity systems
  
• Urgency scoring, which combines business importance with the speed and sophistication of observed attack tactics
  
• Entity-centric investigation context, which keeps analysis centered on identities and the resources they touch
  

Replacing static trust assumption with observed privilege

Real-time behavior provides a more accurate view of identity risk than static role definitions. This matters because directory attributes may remain unchanged even as privilege is actively abused. Tracking observed privilege supports a dynamic, Zero Trust-aligned perspective.

To surface meaningful deviations, identity analytics focuses on specific behavior-based indicators. These indicators matter because subtle shifts in access often precede higher-urgency attacker actions, and include:

• Subtle shifts in privilege use that move beyond expected bounds
  
• Identities operating as over-permissioned relative to defined access expectations
  
• Real-time access patterns that contradict assigned directory roles
  

Creating a single narrative through cross-domain correlation

Signals scattered across systems rarely appear conclusive on their own. Linking activity across network, cloud, SaaS, and identity systems reduces ambiguity about what an identity is doing, transforming partial indicators into coherent behavior patterns.

To do this, identity analytics solutions consistently link activity across several domains, like:

• Identity providers and directories
  
• Cloud control and management layers
  
• SaaS collaboration and productivity services
  
• Network environments and local hosts
  

How identity analytics maps identity behavior to attack progression

Viewing identity compromise as a progression can clarify response decisions. Early-stage misuse looks different from late-stage exploitation, and correlating behavior helps differentiate the two. This helps reduce uncertainty about severity and timing.

To assess progression, analysts examine recurring behaviors that signal advancement, from access to control, across environments:

• Scripting engine use to access identity providers and then move into cloud environments
  
• Token theft, enabling lateral movement across identities and resources
  
• Kerberoasting using local credentials to obtain higher-privilege access
  
• Privilege escalation and policy tampering in cloud environments
  

Bridging that gap requires identity-focused analytics that surface real attack behavior early and reduce the investigation burden on already constrained teams.

See how Vectra AI ITDR delivers 3× threat visibility with 50% less workload →

How to evaluate identity analytics approaches 

Evaluation depends on whether an approach truly reduces uncertainty and improves prioritization. Static records and isolated alerts do not meet this requirement. Effective identity analytics therefore, centers on behavior, correlation, and urgency.

An effective evaluation should focus on whether an approach actually reduces uncertainty and improves prioritization. Static records and isolated alerts fall short of this goal. Strong identity analytics centers on observed behavior, cross-environment correlation, and urgency.

Clear evaluation criteria help prevent misclassification and operational friction. They also determine whether identity analytics can replace manual event stitching with consistent, identity-centric insight.

Identity analytics approaches can be evaluated using the following criteria:

• Does the approach rely on observed privilege, rather than static directory roles, as the foundation for risk assessment?
  
• Does it correlate identity signals across network, cloud, SaaS, and identity systems, instead of analyzing each in isolation?
  
• Is prioritization driven by urgency and risk, rather than raw alert or event volume?
  

How the Vectra AI Platform applies identity analytics to prioritize identity risk across environments

The Vectra AI Platform applies identity analytics by correlating identity threat signals across environments to prioritize which identities are most urgent. This matters because identity compromise can span network, cloud, SaaS, and identity systems, and isolated alerts do not provide a unified view of observed privilege and risk progression.

In essence, the Vectra AI Platform frames the problem as identity-centric prioritization rather than event-centric monitoring.

Using identity analytics, the Vectra AI Platform provides visibility into:

• Which identities show correlated behavior across network, cloud, SaaS, and identity systems.
  
• Which identities demonstrate shifts in observed privilege associated with risk progression.
  
• Which identities are involved in behaviors such as lateral movement or privilege escalation.

See how the Vectra AI Platform prioritizes identity risk across network, cloud, and SaaS →