AI UEBA, Behavioural Analytics for Users and Entities
Spharaka AI UEBA continuously baselines user and entity behaviour, detects anomalies, and surfaces insider threats with AI-driven behavioural analytics, fully integrated into the AI SOC platform.
Continuous Behavioural Baselining
Spharaka AI UEBA uses agentic AI and SAGE™, Spharaka's proprietary AI model for autonomous cyber defence, to learn normal behaviour for every user, machine, service account, and workload.
- User behaviour analytics across cloud, SaaS, and on-premise identity
- Entity behaviour analytics for endpoints, servers, workloads, and OT
- Insider threat detection with peer-group and self-baseline comparisons
- Identity risk scoring with continuous adaptive trust signals
- Behavioural anomaly detection with low-noise prioritization
- Privileged user monitoring with session-aware analytics
Detect Threats Other Tools Miss
AI UEBA detects credential theft, lateral movement, privilege abuse, data exfiltration, and account takeover that signature and rule-based tools miss.
- Account takeover and credential abuse detection
- Lateral movement and privilege escalation behaviours
- Data exfiltration and unusual data access patterns
- Service account and machine identity anomalies
- Native fusion with AI SIEM and AI SOAR signals
How a baseline is built and what it is compared against
Behavioural analytics fails in one of two ways: a baseline too loose to catch anything, or too tight to survive a normal Monday. These six steps are where that balance is actually set.
Establish identity
Before behaviour can be attributed it has to belong to someone. A directory user, a VPN session, a host owner, a cloud principal and a service account credential are resolved into one entity, because an attacker moving between those representations is exactly the case this exists to catch.
Learn the self-baseline
For each identity and each entity: which systems, at what hours, from where, using which protocols, at what volume. This is the comparison that matters most, because the question is not whether an action is permitted but whether this actor does this.
Learn the peer baseline
The same profile for the group the identity belongs to. Peer comparison is what catches the account that was compromised before the baseline was learned, and what stops a genuine change of role reading as an intrusion for the following month.
Score the deviation
A departure is scored on how far it sits from both baselines, what the identity is entitled to, what the asset is worth and what else is happening around it. Most deviations are uninteresting on their own, which is why the score feeds correlation rather than an alert queue.
Feed the case
Behavioural signal is an input to investigation, not a separate product with its own console. A UEBA anomaly, a correlated event and a threat intelligence match on the same entity are three inputs to one judgement, made once.
Adapt
Baselines move as the environment moves: new role, new team, new application, new working pattern. A baseline that does not adapt becomes a false-positive generator within a quarter, which is the usual reason a UEBA deployment quietly stops being read.
What behaviour it can see
Coverage across identity surfaces decides how much of an entity's behaviour is actually observable. Gaps here are where insider activity lives.
Identity and access
- Directory and identity provider authentication
- Privileged access management sessions
- VPN and remote access records
- MFA challenges, approvals and denials
- Service account and machine credential use
Entity activity
- Endpoint and server process and file activity
- File share and document repository access
- Database query and export patterns
- Cloud API calls by principal
- SaaS application usage and data export
- OT workstation and controller interaction through Signal
What it detects
- Account takeover and credential abuse
- Insider data staging and exfiltration
- Privilege escalation and entitlement drift
- Lateral movement between systems
- Dormant and orphaned account reactivation
- Service account misuse, which has no human pattern to compare
Service accounts deserve separate mention: they have no peer group and no working hours, so they are baselined on volume, sequence and destination instead, and they are disproportionately what an attacker reaches for.
Behavioural analytics against rule-based detection
Rules and baselines answer different questions. The reason UEBA exists is that four of the six most common threat classes cannot be expressed as a rule without unacceptable noise.
| Aspect | Rule-based detection | Spharaka |
|---|---|---|
| The question asked | Is this action permitted, and does it match a pattern someone described as bad? | Does this actor do this? A permitted action by the wrong actor is still the wrong action. |
| Stolen credentials | Produces a successful login from a valid account. There is no failure to match and no indicator to hit. | Scored against that identity's own hours, geography, device and access pattern, where the mismatch is the signal. |
| Insider activity | Procedurally authorised, so a rule that catches it also catches everyone doing their job. | Compared to the person's own history and their peers', which is the only place the difference shows. |
| Tuning | Thresholds set by hand, drifting as the estate changes, accumulating debt nobody has time to pay down. | Baselines adapt to the environment, so a change of role stops being a month of false positives. |
| New employees | Indistinguishable from anyone else until someone writes a rule about them. | Covered by peer comparison from day one, then by their own baseline as it accumulates. |
| What the output is | An alert, of which the great majority are legitimate behaviour that resembled the rule. | A scored signal that joins other evidence, so a lone weak anomaly does not become someone's afternoon. |
A resignation that turned into a data case
Nothing in this sequence is unauthorised. The employee had every entitlement they used, which is what makes insider activity the hardest of the six threat classes.
A senior salesperson begins accessing customer records at roughly four times their usual daily volume. Every record is inside their territory and their entitlement.
Self-baseline flags the volume. Peer baseline shows the rest of the team unchanged, so this is not a quarter-end effect across the group. On its own, still a weak signal.
Access shifts to 21:00 and later, which is outside this person's two-year pattern but well inside company norms overall. A rule using company hours would see nothing.
Exports to a personal cloud storage domain that the acceptable use policy permits. The proxy allows it, the category is approved, and no data loss rule matches the file types.
Volume deviation, hour deviation and destination are joined against one entity over eleven days. The sequence, rather than any single element, is what the case is built on.
Because the activity is authorised and the consequence is an HR matter rather than a security containment, AirWatch policy stages everything: no autonomous action, a case assembled with the evidence and routed to the named approver in legal.
The point of this example is the restraint. An autonomous platform that disabled a salesperson's account on a volume anomaly would cause more damage than it prevented, and the policy envelope is what makes that distinction rather than the detection quality.
What to test in a UEBA evaluation
Behavioural products demonstrate beautifully and disappoint quietly. These four tests surface the disappointment early.
How long until the baseline is usable?
Ask specifically, and ask what happens in the meantime. A platform that needs ninety days before it says anything useful has told you when the value actually starts, and peer comparison is how a good one covers the gap.
Change someone's job mid-trial
Move a real user to a different team and watch. A baseline that cannot absorb a legitimate role change will generate a month of noise every time anyone is promoted, and that is what makes teams stop reading it.
How does it handle service accounts?
They have no hours, no geography and no peers, and they are what an attacker reaches for. A platform that only baselines humans has left the most-abused identity class uncovered.
Show me a low score it was right about
Ask for a case where a weak behavioural signal only mattered once joined to something else. That is the whole argument for UEBA sitting inside the platform rather than beside it, and a vendor who cannot produce one is running it beside.
Frequently asked questions
What is AI UEBA?
AI UEBA, AI User and Entity Behaviour Analytics, applies agentic AI and machine learning to baseline normal behaviour and detect anomalies tied to insider threats, identity risk, and advanced attacks.
How is AI UEBA different from rule-based DLP or IAM tools?
Rule-based DLP and IAM enforce policy. AI UEBA detects intent-driven behavioural anomalies that bypass policy, including insider threats, account takeover, and stealthy lateral movement.
Is AI UEBA included in Spharaka's AI SOC platform?
Yes. AI UEBA is natively unified with AI SIEM and AI SOAR in the Spharaka AI SOC platform, sharing context for autonomous detection and response.
Explore related capabilities
AI SOC Platform
Unified, autonomous Security Operations Center.
AI SIEM
AI log analytics and correlation.
AI SOAR
Autonomous response and orchestration.
Autonomous Threat Hunting
Behaviour-based AI threat hunting.
Cyber Threat Intelligence
CTI and IOC enrichment.
SAGE™ AI Model
Spharaka's proprietary Cybersecurity SLM that powers this capability.
Related use cases and guides
An envelope describing one identity's normal, with several traces running inside it and one departing at the point it crosses.
Behavioural detection for identity-driven risk.
Capabilities
All eight capabilities, and where UEBA sits among them.
Evaluating Autonomous Defence
How to test behavioural detection in a proof of value.
See Spharaka AI UEBA in action
Discover how Spharaka's AI-native, autonomous cyber defence platform modernises your security operations.