Financial AI security and compliance

Security architecture

The stack uses a layered security model to protect user assets and sensitive data.

API key handling

• Encrypted storage: Keys are encrypted and stored locally
• Least privilege: Exchange keys are scoped to trading/read—no withdrawals
• IP allowlists: Users should enable IP restrictions on the exchange side when available
• 2FA: Two-factor authentication is required for exchange accounts
• Rotation: Periodic key rotation and permission review

Data protection

• Local-first: Sensitive data stays local; no cloud sync of secrets by default
• Encryption: Configuration and tokens at rest are encrypted where applicable
• Anonymization: Public trade logs remove personal identifiers completely
• Access control: OS-level permissions restrict who can read local files

Regulatory alignment

Not investment advice

NoahAI Labs does not provide investment advice. Tools support user judgment; users remain responsible for decisions.

• Disclaimers: Non-advice language appears across relevant surfaces
• No guaranteed returns: We do not promise performance outcomes
• Risk disclosure: Clear risk communication where required
• User responsibility: Final accountability for trades remains with the user

Data protection practices

• Data minimization: Collect only what is needed to operate
• Anonymization: Published data is anonymized at pattern level
• Retention: Retention policies are explicit for local operational data
• Deletion: Support user-initiated deletion workflows where applicable

Auditability

Structured logging

Judgments and executions are recorded in standardized formats so reviews can be systematic.

• DecisionLog: Rationale and context for judgment calls
• ExecutionResult: Execution outcomes with timestamps
• RiskEvent: Risk events and responses
• XAITrace: Traceable rationale for AI-assisted decisions

Reproducibility

• Standardized schema: Logs follow a consistent schema
• Versioning: System and configuration versions are recorded
• Replay: Records support replay under stated assumptions
• Third-party review: Evidence can be audited by external reviewers

Public R&D and investor lens

Regulatory adaptability

The compliance posture is designed to adapt as requirements evolve.

• Modular controls: Security policies can be updated independently
• Standardized audit logs: Log formats can be adjusted to match program requirements
• Flexible access control: RBAC-style patterns as deployments mature
• Stronger protection: Data controls can be tightened for new rules

Enterprise readiness

The model is designed with enterprise adoption in mind.

• SSO: Integrations for corporate SSO where applicable
• RBAC: Role-based access patterns
• On-prem options: Deployable inside private networks for institutions
• SLA: Service-level agreements for enterprise deployments
• Audit logs: Internal controls and external audit support

Conclusion

Security and compliance in this stack combine layered security, regulatory alignment, and auditability to protect users and adapt to changing requirements.

For public R&D and investors, these properties support claims of regulatory adaptability, enterprise readiness, and trustworthiness.