Why classical UEBA underdelivered
Legacy UEBA relied on statistical peer-group baselines. In real enterprises, peer groups are ambiguous, roles shift, and the baseline drifts. The output was either noise or silence.
What AI-native UEBA does differently
AuraXP™ agents combine per-identity behavioural context with organisational role, access, and prior incident history. Deviations are reasoned about, not just scored, and correlated with data movement, privilege change, and unusual system access.
- Identity-scoped behavioural context, not thin peer-group statistics.
- Cross-signal correlation, access, data movement, endpoint, communication.
- Reasoned alerts with evidence chains, not opaque anomaly scores.
- Human-in-the-loop for HR-adjacent action classes.
Boundary of appropriate autonomy
Insider-risk response often intersects HR, legal, and privacy considerations. Autonomous action is bounded accordingly, investigation is automated; consequential action requires the right human authority.
Frequently asked questions
Do you replace our existing UEBA product?
Not necessarily. Sphere™ can consume signals from existing UEBA or provide the analytics natively. The decision is a deployment question, not a technical constraint.
How is this different from DLP?
DLP focuses on data movement; UEBA focuses on behaviour. The two are complementary, Sphere™ reasons across both signal classes.