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AI use case · Fraud & security

Insider Threat & Access Anomaly Detection

Privileged access misuse by internal users — downloading subscriber PII, running unusual billing queries, accessing data outside their authorised scope — is difficult to detect without behavioural baselines.

UEBABehavioural MLAnomaly Detection
MediumBusiness priority
Fraud & securityDomain
4Main data sources

The problem

Privileged access misuse by internal users — downloading subscriber PII, running unusual billing queries, accessing data outside their authorised scope — is difficult to detect without behavioural baselines.

The AI approach

User and Entity Behaviour Analytics (UEBA) applied to access logs. ML baselines normal access patterns per user role. Deviations — unusual access times, bulk data exports, out-of-scope resource access — are scored and alerted in real time.

How it works

  1. 1
    Collect

    Gather access logs from BSS tools and data stores.

  2. 2
    Baseline

    Learn normal access per user and role.

  3. 3
    Detect

    Flag unusual access, bulk exports and privilege use.

  4. 4
    Respond

    Alert security and review access.

Data it uses

IAM Access LogsApplication Audit LogsDatabase Query LogsPII Access Records

How to measure value

Practical tips

Standards & references

Data governanceUse governed, consented data only
Responsible AIExplainable, monitored, human-in-the-loop

Related pages

This page describes generic industry practice and public standards. It is not based on, and does not describe, any particular vendor's product or operator's systems.