User and Entity Behavior Analytics (UEBA)

UEBA continuously learns the normal query patterns for every authenticated entity—both human users and machine service accounts—interacting with your inference endpoints.

• Temporal Analysis: Models the typical request frequency, volume, and timing (e.g., a service account that suddenly queries at 3 AM).
• Structural Profiling: Learns the statistical distribution of input shapes, token lengths, and embedding structures for each client.
• Drift Detection: Flags statistically significant deviations from the established baseline without relying on static, signature-based rules.

UEBA is a primary defense against black-box model stealing attacks where adversaries issue thousands of carefully crafted queries to replicate your proprietary model's functionality.

• Sequential Query Analysis: Identifies systematic, grid-like probing patterns in input spaces that are characteristic of extraction, not organic usage.
• Entropy Monitoring: Detects low-entropy, high-volume query streams where an attacker is methodically mapping decision boundaries.
• Cross-Session Correlation: Links seemingly independent sessions to a single extraction campaign by analyzing shared query structures and timing fingerprints.

Detects when a valid API key or JWT is being abused by an unauthorized actor, even if the authentication itself succeeds.

• Geo-Velocity Checks: Flags impossible travel scenarios where a single credential is used from geographically disparate locations within an impossible timeframe.
• Resource Access Shifts: Identifies when a service account historically accessing a single model endpoint suddenly enumerates all available models in the serving infrastructure.
• Privilege Escalation Patterns: Detects repeated attempts to access higher-tier model versions or administrative endpoints inconsistent with the entity's established role.

Monitors for anomalous data access patterns that suggest a trusted internal actor is abusing their legitimate inference privileges to exfiltrate sensitive outputs or training data.

• Output Volume Spikes: Detects when a user's response payload size or total data egress dramatically exceeds their rolling historical average.
• Unusual Aggregation Queries: Flags queries that appear designed to reconstruct training data distributions rather than perform legitimate single inferences.
• Off-Hours Activity: Correlates query activity with human resource records and normal shift patterns to identify suspicious after-hours data access.

Instead of comparing an entity only to its own history, UEBA compares behavior against a dynamically constructed peer group of similar entities.

• Role-Based Clustering: Groups service accounts by their assigned RBAC roles and flags one that deviates from the cluster's collective behavioral envelope.
• Application Fingerprinting: Identifies when a specific client application instance behaves differently from other instances of the same application, suggesting tampering or a compromised binary.
• Contextual Anomaly Scoring: Reduces false positives by suppressing alerts for behaviors that are anomalous for the individual but normal for the peer group.

UEBA engines aggregate multiple weak behavioral signals into a unified risk score that can dynamically trigger automated security responses.

• Bayesian Belief Networks: Combine disparate signals—like unusual location, atypical query structure, and high volume—into a single probabilistic risk assessment.
• Adaptive Rate Limiting: Integrates with the Policy Enforcement Point (PEP) to dynamically tighten rate limits for a high-risk session rather than blocking it outright.
• Step-Up Authentication: Triggers a demand for proof-of-possession token verification or re-authentication when a session's risk score crosses a defined threshold.

A defensive mechanism that analyzes sequential query patterns to identify black-box extraction attacks. UEBA establishes a baseline of normal API query behavior—such as typical request frequency, input diversity, and embedding drift—and triggers alerts when an entity exhibits systematic probing indicative of an attacker attempting to replicate a proprietary model's functionality.

• Detects high-volume, low-diversity query sequences
• Flags queries that systematically explore the model's decision boundary
• Correlates query patterns with credential usage to identify compromised keys

A defensive technique that restricts the number of API requests a client can make within a specific timeframe. While rate limiting enforces static thresholds, UEBA adds a dynamic layer by learning per-entity behavioral baselines. An entity that suddenly accelerates from 10 queries per minute to 900 may still be under a hard limit but represents a behavioral anomaly worthy of investigation.

• Prevents brute-force extraction and denial-of-service
• UEBA contextualizes rate violations with historical patterns
• Combined approach catches slow-and-low exfiltration attempts

A chronological, tamper-proof record of all access and query events stored in WORM-compliant storage. UEBA consumes these audit logs as its primary data source, applying machine learning to detect deviations from established norms. Without a comprehensive audit trail, behavioral baselines cannot be accurately constructed.

• Provides the ground truth for UEBA model training
• Enables forensic reconstruction of attack timelines
• Supports non-repudiation for compliance reporting under frameworks like SOC 2 and FedRAMP

A security model that eliminates implicit trust and requires continuous verification of every access request. UEBA operationalizes zero trust by providing the continuous behavioral signals needed to make real-time authorization decisions. If an authenticated entity suddenly exhibits anomalous query behavior, UEBA can trigger a step-up authentication challenge or session termination.

• Shifts from binary allow/deny to risk-based access
• Integrates with Policy Enforcement Points (PEPs) for adaptive enforcement
• Detects credential compromise even after successful authentication

A mechanism defined by RFC 7662 that allows a resource server to query the authorization server about a token's active state and metadata. UEBA enriches introspection data with behavioral risk scores, enabling the authorization server to make context-aware decisions. A valid token presented with an anomalous behavioral profile can be downgraded or revoked in real time.

• Combines static token validity with dynamic risk assessment
• Enables just-in-time revocation based on behavioral anomalies
• Supports OAuth 2.0 and SPIFFE-based serving environments

A component in a zero-trust architecture that intercepts requests to a protected inference endpoint and enforces access decisions. When integrated with UEBA, the PEP becomes behavior-aware, consulting a risk engine that evaluates the requesting entity's current query patterns against its historical baseline before permitting or denying access.

• Acts as the enforcement actuator for UEBA-derived signals
• Can inject latency-minimal checks via sidecar proxies
• Enables adaptive policies like 'allow but throttle' for suspicious entities