Definition
Detecting anomalies in user access patterns by comparing user access activity to behavioral profiles that categorize users by role such as job title, function, department.
How it works
Peer group analysis identifies functionally similar groups of actors (users or resources) based on categorizations such as job title, organizational hierarchy, or other attribute that indicates similarity of job function. Current user access activity is then compared to the appropriate peer group behavior profile to identify anomalies.
Considerations
Potential for false positives from anomalies that are not associated with malicious activity.
Implementation perspective
Job Function Access Pattern Analysis should be treated as a technical defensive capability rather than a product checkbox. In practice, teams should define the protected scope, the conditions under which the technique acts, and the observable evidence that demonstrates the intended behavior. For this technique, likely engineering context includes the relevant system, activity, and evidence sources.
Use the technique to identify suspicious, unauthorized, or abnormal activity through observable evidence and repeatable analysis.
Questions to ask
- Which events, states, or artifacts must be observed for the analysis to work?
- What analytic logic, threshold, comparison, or signature turns observations into a finding?
- How are expected false positives, blind spots, and environmental variations documented?
- Who receives the result, and what action is expected when the technique produces a finding?
Evidence and validation
- Telemetry and data-source configuration records
- Analytic logic, thresholds, signatures, and version history
- Test cases demonstrating expected positive and negative results
- Alert, triage, escalation, and tuning records
Common failure patterns
- Required telemetry is missing, delayed, or transformed in a way that invalidates the analysis.
- The technique produces alerts without an accountable triage and response process.
- Detection coverage is claimed from product deployment without testing the relevant analytic behavior.
This implementation perspective is original Bare Metal Cyber educational content. It does not replace the official D3FEND definition or establish that a specific product implements the technique.
Technique hierarchy
Top-level family
Parent techniques
Direct child techniques
None listed at this level.
Artifacts and ontology entities
These relationships describe how D3FEND connects a defensive technique to artifacts or other ontology entities. They describe graph semantics, not a product certification.
Explicit technique relationships
NIST SP 800-53 relationships
The relation label is preserved from the D3FEND ontology. It is not converted into a claim that the control automatically implements or validates this technique.
ATT&CK Enterprise mitigation relationships
These links come from the D3FEND ontology’s ATT&CK mitigation mapping. They help users navigate between the knowledge bases and do not guarantee mitigation effectiveness.
Authoritative sources
- Open this technique on the official D3FEND website ↗
- Open the official ontology resources ↗
- Anomaly Detection Using Adaptive Behavioral Profiles ↗Securonix Inc · Igor A. Baikalov; Tanuj Gulati; Sachin Nayyar; Anjaneya Shenoy; Ganpatrao H. Patwardhan · PatentReference
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