Security
Built for Sensitive Revenue Data
Tars AI is designed to handle recordings, transcripts, account context, and competitive intelligence responsibly with a tenant-isolated architecture and practical controls that align with how enterprise revenue teams operate.
Core Security Principles
The system is built around isolation, least privilege, and defensible data flows.
Tenant isolation by design
Data is scoped per tenant across storage and application boundaries to prevent cross-customer access.
Least-privilege access
Access is designed to be role-based and constrained to what each user needs to do their job.
Encryption in transit + at rest
Sensitive data is protected during transmission and while stored, using standard encryption practices.
Auditability + traceability
Outputs are designed to be evidence-backed so teams can understand what the system used and why.
Privacy & AI Use
Useful automation without losing control over sensitive data and context.
- Tenant boundaries are enforced across storage and processing.
- Operational guardrails mirror enterprise workflows.
- Evidence-first outputs reduce ambiguity and improve trust.
- Deployment can be tailored for stricter policy requirements.
Customer data boundaries
Outputs are generated inside each customer's standalone workspace.
Practical controls
Retention, controlled sharing, and guardrails aligned to how teams operate.
Evidence-first behavior
Signals are traceable and structured — not vague summaries.
Deployment flexibility
Storage and deployment can be tailored to internal security policy.