Move beyond static passwords and one-time MFA. CyberVault’s behavioral telemetry engine continuously evaluates user identity, device posture, and contextual risk to enforce dynamic, adaptive access controls in real-time.
Traditional authentication is point-in-time. Our behavioral telemetry system operates as a continuous feedback loop, constantly verifying identity and adjusting privilege levels without disrupting workflow.
Lightweight, privacy-first SDKs collect micro-behavioral signals across endpoints, browsers, and mobile apps. Data is hashed and anonymized before leaving the device.
Signals are cross-referenced with network topology, geolocation, device posture, time-of-day patterns, and organizational baseline profiles.
Our ensemble models (LSTM, Isolation Forest, Gradient Boosting) calculate a real-time trust score (0-1000) reflecting identity confidence and session risk.
Based on the score, the policy engine dynamically grants, restricts, or challenges access. High-risk anomalies trigger step-up authentication or session isolation.
False positives are fed back into the training pipeline. Models adapt to legitimate behavioral shifts (e.g., new devices, remote work transitions) without manual reconfiguration.
We analyze 140+ non-invasive behavioral indicators to build a continuous identity fingerprint. No biometric storage required.
Flight time, dwell time, rhythm consistency, backspace frequency, and typing cadence patterns.
Acceleration curves, click timing, drag paths, touch pressure, and swipe velocity profiles.
Tab switching frequency, application focus patterns, copy/paste ratios, and navigation depth.
OS version, patch level, encryption status, MDM compliance, hardware attestation, and root/jailbreak detection.
IP reputation, VPN/tunnel detection, Wi-Fi BSSID consistency, GPS drift, and impossible travel velocity.
Query frequency, data volume thresholds, privileged command usage, and cross-service call graphs.
Every session generates a dynamic risk meter that updates every 3-5 seconds. The engine balances false positive rates (<0.8%) with detection sensitivity, adapting to organizational threat models.
* Simulation: Score updates based on synthetic behavioral inputs. Production environments process 50k+ signals/sec.
| Trust Score Range | Automated Action |
|---|---|
| 0 – 250 | Session terminated. Account locked. SOC alert. |
| 251 – 500 | Step-up MFA required. Privilege reduced. Monitoring escalated. |
| 501 – 750 | Standard access granted. Enhanced logging enabled. |
| 751 – 1000 | Full privilege. Background monitoring. Cache optimization. |
Policies are fully customizable via our YAML-based policy-as-code engine. Supports OPA, XACML, and custom webhook integrations.
Native integration with Okta, Azure AD, Ping, Auth0, and custom SAML/OIDC providers. Seamless broker mode deployment.
Lightweight client SDKs for JavaScript, React, iOS, Android, and Electron. <2% CPU overhead, <50ms latency impact.
Real-time streaming to Splunk, Sentinel, QRadar, and Sumo Logic. Native SOAR playbooks for automated containment.
| Feature | Implementation |
|---|---|
| Data Minimization | On-device feature extraction. Raw behavioral data never leaves endpoint. |
| Encryption | AES-256-GCM in transit. Keys managed via HSM-backed KMS. |
| GDPR/CCPA | Right-to-erasure compliant. Pseudonymous identifiers with configurable retention. |
| Regulatory | SOC 2 Type II, ISO 27001, HIPAA BAA, FedRAMP Moderate ready. |
Deploy our behavioral telemetry engine in your environment within 48 hours. Our security architects will configure baseline models, integrate with your identity stack, and validate false-positive thresholds before go-live.