37SECURITY ANALYTICS

Behavioral Anomaly Detection

Use AI to prioritize uncertainty; use deterministic policy to constrain consequences.

Priority: P2
Phase: Phase 3
Primary owner: Detection / Data Science
Status: Blueprint

Objective

Behavioral detection identifies suspicious deviations that static policy cannot anticipate. The design should combine understandable statistical baselines with optional machine-learning models and never let anomaly scoring alone execute irreversible remediation without policy.

What to build

Baseline agents by tools, resources, data volume, destinations, time, spend and delegation patterns.
Detect sudden privilege usage, bulk reads, novel destinations, unusual tool sequences and repeated denied actions.
Peer-group comparison for agents with similar business purpose.
Risk escalation when multiple weak signals occur together.

Implementation decisions

Maintain cold-start rules for new agents with insufficient history.
Expose contributing factors for every anomaly score.
Allow customer suppression/tuning and feedback loops.
Use anomaly results as inputs to policy, approval and investigation workflows.

Definition of done

Detection output is explainable enough for a SOC analyst to triage.
False positives can be tuned without retraining the entire system.
High-confidence anomalies create cases or increase runtime restrictions.

Success metrics

Alert precision
Analyst triage time
Confirmed incident rate
Behavior baselines with adequate history

DETECTION RULE

Use AI to prioritize uncertainty; use deterministic policy to constrain consequences.