Security Architecture

FaceSign’s security architecture is built on a principle: tokenize first, analyze second. All biometric data passes through HSM tokenization at the point of ingestion, before it reaches the AI analysis layer. Raw video is never stored.

Six parallel AI models

Every verification session runs six AI models simultaneously. Each model analyzes a different dimension of the interaction, and their outputs are combined into a unified risk score.

Model	What it analyzes	What it catches
Interactive liveness	Facial motion, depth, temporal consistency	Deepfakes, photos, pre-recorded video, masks
Coercion detection	Vocal stress, gaze, micro-expressions, response timing	Duress, coaching, elder abuse, forced transactions
Environmental analysis	Background, lighting, audio fingerprint	Virtual backgrounds, fraud call centers, spoofed locations
Behavioral analysis	Interaction patterns, sentiment, deviation from baseline	Compromised accounts, behavioral anomalies
Predictive risk	Device fingerprint, IP, geolocation, time-of-day	Unusual devices, impossible travel, high-risk geolocations
Adaptive conversation	Real-time dialogue analysis, topic coherence	Scripted responses, inconsistent answers, evasion

The models run independently and cannot influence each other’s analysis. Each produces its own confidence score. The platform combines these into a final risk assessment that your application receives via webhook.

Data flow

The path from user camera to your webhook follows a strict sequence designed to minimize exposure of raw biometric data:

Stage	What happens	Raw data present?
1. Capture	User’s camera and microphone stream to the FaceSign widget over TLS 1.3	Yes (in transit only)
2. HSM tokenization	Video and audio frames enter the Hardware Security Module. Biometric features are extracted and tokenized. Raw frames are discarded.	Discarded after tokenization
3. AI analysis	Six models analyze the tokenized biometric features in parallel	No
4. Result delivery	Risk scores, transcript, and decision are delivered to your webhook	No
5. Retention	One-way biometric fingerprint stored for future recognition. All other session data follows your configured retention policy.	No

Raw video and audio are processed in memory during the session and discarded at the tokenization stage. FaceSign never writes raw biometric media to disk or object storage.

Encryption

Layer	Standard
In transit	TLS 1.3 for all connections between client, FaceSign servers, and your webhooks
At rest	AES-256 encryption for all stored data, including tokenized biometric fingerprints
Key management	Hardware Security Modules (HSMs) for tokenization keys and biometric feature extraction

Data roles

FaceSign operates as a data processor under GDPR and a service provider under CCPA. Your organization is the data controller. This means:

Responsibility	Who owns it
Deciding when to trigger a verification session	You (controller)
Processing biometric data during the session	FaceSign (processor)
Determining retention periods and deletion policies	You (controller)
Responding to data subject access requests	You (controller), with FaceSign support
Securing the processing infrastructure	FaceSign (processor)

A Data Processing Agreement (DPA) is available on request. FaceSign also provides DPIA (Data Protection Impact Assessment) support materials for organizations that require them.

What FaceSign does not store

Raw video or audio recordings
Unencrypted biometric data
Personally identifiable information beyond what you explicitly send in the session payload
Session media after the tokenization stage

What FaceSign does store

One-way biometric fingerprint (for future RECOGNITION node matching)
Session metadata (timestamps, risk scores, node outcomes)
AI-generated transcript of the conversation
Tokenized biometric features used for analysis

All stored data is encrypted with AES-256 and subject to your configured retention policy. See Biometric Data Handling for retention periods.

Next steps

Biometric Data Handling

Retention periods, deletion rights, and data subject access.

Deepfake Detection

How interactive liveness achieves a 99% or higher catch rate.

Compliance

GDPR, CCPA, SOC 2, and PSD3 regulatory alignment.

Getting started

Use cases

Guides

Security

Concepts & reference

Security Architecture

Six parallel AI models

Data flow

Encryption

Data roles

What FaceSign does not store

What FaceSign does store

Next steps

Biometric Data Handling

Deepfake Detection

Compliance

​Six parallel AI models

​Data flow

​Encryption

​Data roles

​What FaceSign does not store

​What FaceSign does store

​Next steps

Biometric Data Handling

Deepfake Detection

Compliance

Six parallel AI models

Data flow

Encryption

Data roles

What FaceSign does not store

What FaceSign does store

Next steps