Encryption in the AI Identity Context
AI identity platforms handle data that sits at the intersection of personal information and biometric data — face geometry, voice characteristics, and behavioral patterns used to create digital replicas. This data demands the highest encryption standards because a breach would not just expose personal information — it could enable unauthorized creation of AI clones, identity theft at a fundamentally new level.
The encryption practices of AI platforms determine whether biometric data is adequately protected at every stage: upload, processing, storage, and delivery.
Encryption Standards Comparison
| Feature | Synthesia | HeyGen | ElevenLabs | Resemble AI | Soul Machines | D-ID |
|---|---|---|---|---|---|---|
| Encryption at Rest | AES-256 | AES-256 | AES-256 | AES-256 | AES-256 | AES-256 |
| Encryption in Transit | TLS 1.3 | TLS 1.2+ | TLS 1.3 | TLS 1.3 | TLS 1.3 | TLS 1.2 |
| Key Management | AWS KMS | AWS KMS | AWS KMS | AWS KMS | Custom | Cloud KMS |
| Client-Side Encryption | No | No | No | No | Enterprise | No |
| Zero-Knowledge Arch. | No | No | No | No | Enterprise | No |
| Hardware Security Modules | Enterprise | No | No | No | Yes | No |
| Encryption Key Rotation | Automatic | Automatic | Automatic | Automatic | Custom | Automatic |
| Biometric Data Isolation | Yes | Partial | Yes | Yes | Yes | Partial |
| PCI DSS Compliance | N/A | N/A | N/A | N/A | N/A | N/A |
Encryption at Rest
AES-256 (Advanced Encryption Standard with 256-bit keys) is the universal standard for data at rest across all major AI platforms. This encryption level is considered computationally unbreakable with current and foreseeable technology. The key differentiator is not the algorithm but the key management infrastructure:
- AWS KMS (Key Management Service): Used by Synthesia, HeyGen, ElevenLabs, and Resemble AI. AWS KMS provides automatic key rotation, access logging, and hardware-backed key storage. This is a strong, well-audited key management solution.
- Custom key management: Soul Machines implements custom key management for enterprise customers, including customer-managed encryption keys (CMEK) — allowing the customer to control and audit all encryption operations on their data.
Encryption in Transit
Data moving between user devices and platform servers must be encrypted to prevent interception:
- TLS 1.3 (the latest standard) is used by Synthesia, ElevenLabs, Resemble AI, and Soul Machines. TLS 1.3 removes legacy cipher suites and reduces the handshake to one round-trip, improving both security and performance.
- TLS 1.2 remains used by HeyGen and D-ID. While TLS 1.2 is still considered secure when properly configured, TLS 1.3 provides stronger default security.
For video upload and download (the primary data transfer in AI video platforms), TLS encryption protects the biometric data — face footage, voice recordings — from interception during transmission.
Biometric Data Isolation
Beyond encryption, how platforms isolate biometric data from other customer data matters:
- Dedicated storage: Synthesia, ElevenLabs, Resemble AI, and Soul Machines store biometric data (face models, voice models) in isolated storage systems separate from general application data.
- Access controls: Biometric data should be accessible only to the specific processing services that need it, not broadly accessible within the platform’s infrastructure.
- Deletion guarantees: When a user revokes consent or deletes their avatar, the biometric data must be purged from all storage locations, including backups.
The Zero-Knowledge Ideal
The gold standard for biometric data protection is a zero-knowledge architecture — where the platform can process biometric data to create and operate AI avatars without ever having access to the unencrypted biometric data itself. This is technically challenging for AI model training, which typically requires access to raw data.
Soul Machines offers enterprise-grade architectures that move toward zero-knowledge for specific deployment scenarios. No platform has fully achieved zero-knowledge architecture for the complete avatar creation-to-deployment pipeline.
For more on the concept, see our zero-knowledge architecture glossary entry.
Practical Security Assessment
When evaluating platform security, look beyond encryption algorithms:
- Penetration testing: Has the platform undergone third-party security audits? Synthesia and ElevenLabs publish summaries of their security assessments.
- Bug bounty programs: Does the platform incentivize security researchers to find and report vulnerabilities? Active bug bounty programs indicate security maturity.
- Incident history: Has the platform experienced data breaches? If so, how were they handled and communicated?
- Insurance: Does the platform carry cyber insurance? Enterprise agreements should require evidence of adequate coverage.
Recommendations
All major platforms meet baseline encryption requirements (AES-256 at rest, TLS in transit). For organizations handling highly sensitive biometric data (celebrity AI twins, medical applications, government use), Soul Machines’ custom key management and hardware security modules provide the strongest protection. For standard enterprise use, Synthesia’s combination of AES-256, TLS 1.3, and SOC 2 compliance represents a well-validated security posture.
Platform Comparison: Best Picks by Use Case
For maximum biometric data protection with customer-managed encryption keys and hardware security modules, Soul Machines provides the most configurable enterprise security architecture. For well-validated standard encryption backed by SOC 2 and ISO 27001 certifications, Synthesia offers the strongest combination of AES-256, TLS 1.3, and documented security practices. For voice data protection with dedicated biometric isolation and published security assessments, ElevenLabs and Resemble AI maintain strong encryption standards specifically optimized for audio biometric data.
Frequently Asked Questions
Is AES-256 encryption sufficient to protect biometric data on AI platforms? Yes — AES-256 is the industry standard for data at rest and is considered computationally unbreakable with current technology. All major AI platforms use AES-256. The more important differentiators are key management practices (who controls the encryption keys and how they are rotated), biometric data isolation (whether face and voice data are stored separately from general application data), and deletion guarantees (whether encrypted data is fully purged from all storage systems including backups upon revocation).
What should I look for when evaluating AI platform security for enterprise deployment? Beyond encryption standards, evaluate four additional criteria: third-party security audits (has the platform undergone independent penetration testing), compliance certifications (SOC 2 Type II, ISO 27001), data residency options (where biometric data is physically stored), and incident history (how previous security events were handled and communicated). Synthesia and ElevenLabs publish summaries of their security assessments, providing the most transparency among AI video and voice platforms.
See our enterprise security feature analysis for additional evaluation criteria.