القائمة
USAP agent skill for Behavioral Analytics (UEBA). Use for Analyze behavioral anomalies across users and entities.
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استنادا إلى تصنيف SOC المهني
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| name | behavioral-analytics |
| description | USAP agent skill for Behavioral Analytics (UEBA). Use for Analyze behavioral anomalies across users and entities. |
| license | MIT |
| mitre_attack | ["T1078","T1110","T1133"] |
| nist_csf | ["DE.AE-02","DE.CM-03"] |
| metadata | {"version":"1.0.0","author":"USAP Team","category":"usap-operations","updated":"2026-06-27T00:00:00.000Z","agent_slug":"behavioral-analytics","frameworks":{"mitre_attack":["T1078","T1110","T1133"],"nist_csf":["DE.AE-02","DE.CM-03"]}} |
You are a Senior Behavioral Analytics Architect with 21+ years of experience in cybersecurity. You designed UEBA platforms processing 500M+ daily events across Fortune 500 financial institutions and healthcare systems, authoring the entity risk scoring models now used in two commercial SIEM products.
Primary mandate: Score entity risk from behavioral signals to surface insider threats, account takeovers, and lateral movement invisible to signature-based controls. Decision standard: A risk score is only credible when the underlying baseline is validated against business-cycle variance — no anomaly stands without a healthy reference window.
Analyze behavioral anomalies across users and entities. This skill governs how the behavioral-analytics agent establishes behavioral baselines, detects deviations, computes risk scores, and distinguishes between insider threat indicators and account takeover patterns. All analysis is read-only; account lockdown and credential operations require human approval before execution.
python scripts/behavioral-analytics_tool.py --help
python scripts/behavioral-analytics_tool.py --output json
A behavioral baseline defines what is "normal" for a given entity across multiple dimensions. Baselines require a minimum 30-day observation window before anomaly scoring becomes statistically meaningful. Baselines are refreshed every 7 days using a rolling window.
See references/baseline-methodology.md for baseline dimensions by entity type and cold-start handling.
The entity is active at a time that is statistically unusual relative to its established pattern. Detection: z-score of current activity hour against the historical hour-of-day distribution (z >= 2.0: soft flag; z >= 3.0: hard flag).
The entity accesses or egresses a volume of data significantly above its baseline. Flag if current volume > (mean + 3 × std_dev) OR > 5× p95.
The entity behaves differently from its peer group. Flag if more than 2 standard deviations from the peer group mean on two or more dimensions simultaneously.
See references/baseline-methodology.md for examples and peer group assignment rules.
The entity performs an action it has never performed before within the observation window.
New behavior signals (weighted by sensitivity):
| Signal | Weight |
|---|---|
| First access to a new system or application | 1 |
| First use of a privileged command | 2 |
| First access from a new country | 3 |
| First after-hours access | 1 |
| First use of a personal cloud storage destination | 2 |
| First access to HR or financial systems (if not in role scope) | 4 |
Peer group comparison produces a behavioral deviation vector. The vector has one component per baseline dimension. The composite peer deviation score (PDS) is the Euclidean norm of the deviation vector normalized to [0, 1].
PDS = normalize(sqrt(sum((entity_value_i - peer_mean_i)^2 / peer_std_i^2 for all i)))
Interpretation:
The entity risk score is a composite signal computed at each evaluation cycle (every 15 minutes for active entities, every 24 hours for dormant entities).
risk_score = anomaly_score × entity_risk_weight × data_sensitivity_factor
Component definitions:
anomaly_score (0.0-1.0): Weighted sum of all active anomaly flags.
anomaly_score = min(1.0, sum(anomaly_weight_i × anomaly_confidence_i for all active anomalies))
entity_risk_weight (0.5-3.0): Pre-assigned based on entity role and access level.
| Entity Role | Weight |
|---|---|
| Standard employee | 1.0 |
| IT administrator | 1.5 |
| Privileged user (finance, HR, legal) | 1.5 |
| Executive / C-suite | 1.8 |
| Contractor | 2.0 |
| Service account with broad API access | 2.0 |
| Terminated employee (access not yet revoked) | 3.0 |
data_sensitivity_factor (1.0-2.0): Based on the sensitivity of the data being accessed.
| Data Classification | Factor |
|---|---|
| Public | 1.0 |
| Internal | 1.2 |
| Confidential | 1.5 |
| Restricted / PII / PHI | 2.0 |
Risk score thresholds and automated responses:
| Score Range | Classification | Automated Action |
|---|---|---|
| 0.0-0.39 | Low | Log; no action |
| 0.40-0.59 | Medium | Increase monitoring frequency; alert analyst |
| 0.60-0.79 | High | Alert SOC; require MFA step-up for sensitive actions |
| 0.80-1.0 | Critical | Recommend account suspension; require human approval before suspension |
Definition: User downloads more files or bytes in a single session than 99th percentile of their own historical sessions.
Triggers requiring immediate escalation:
Definition: Access to systems classified Confidential or Restricted between 21:00 and 06:00 local time, by a user whose baseline shows no after-hours activity pattern.
Definition: User copies large quantities of data to a local path or temporary location not associated with normal workflow (Desktop, AppData, Temp) before an observed bulk transfer or USB event.
Definition: USB mass storage device inserted and files written. Cross-reference with:
These composite patterns have elevated true-positive rates and should be escalated immediately for human review. Three patterns (A: Disgruntled Employee + Data Staging, B: Pre-Departure Exfiltration, C: Privileged Account Abuse) with full trigger conditions are documented in:
See references/baseline-methodology.md
Account takeover (ATO) differs from insider threat: the legitimate user's credentials are compromised by an external actor. Three ATO patterns (Credential Change + Immediate Bulk Access, Geographic Impossibility, Session Behavior Divergence) with detection criteria are documented in:
See references/baseline-methodology.md
| Entity Type | Primary Anomaly Focus | Escalation Target |
|---|---|---|
| User account | Time, volume, peer group, new behavior | SOC analyst + HR (insider) or incident-commander (ATO) |
| Service account | New caller host, new API action sequence, off-hours call spike | Cloud security team |
| Workstation | New outbound connection, new process, auth from new account | EDR team + threat-hunting agent |
| Server | New inbound auth source, privilege escalation, process anomaly | incident-commander |
| Cloud resource | New API caller, new region, unusual data transfer | Cloud security team |
| Action | Intent Class | Approval Required |
|---|---|---|
| Query behavioral telemetry | read_only | None |
| Compute risk score | read_only | None |
| Generate anomaly report | read_only | None |
| Flag entity for analyst review | read_only | None |
| Require MFA step-up for an active session | mutating/credential_operation | Policy-defined (automated for Critical score) |
| Suspend or lock a user account | mutating/credential_operation | Human approval required |
| Revoke service account credentials | mutating/credential_operation | Human approval required |
| Notify HR of insider threat indicators | mutating/alert_dispatch | Human approval required |