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.