AutoDoc Enterprise Briefing
AutoDoc Trust, Security & AI Usage Overview for Enterprise Teams
AutoDoc is built for sensitive documentation workflows where governance, auditability, and defensible outputs matter.
Enterprise Trust Flow
Identity & Access
Role-based access
Secure Credentials
Secrets vault
Tenant Isolation
Separate tenant boundary
Controlled Processing
Auditable jobs
Outputs & Review
Evidence packs + narrative drafts (human approved)
The Trust Model
Clear ownership and control
Customers own data and outputs.
Isolation by tenant
No cross-customer data mixing.
Auditability by default
Job records plus access trails.
How Access Works
- Credentials are stored in a secure secrets vault (not in code, not in logs).
- Processing jobs retrieve secrets only when needed and only with authorized permissions.
- Access is revocable, rotatable, and auditable.
- Secrets in request payloads
- Secrets in logs
- Uncontrolled credential reuse
Where Data Lives + How it's Isolated
Data location
Data and outputs reside in a controlled environment with defined residency and governance.
Tenant isolation
Each customer is provisioned as a separate tenant with tenant-specific storage boundaries and tenant-scoped permissions. Jobs and outputs are tagged and tracked per tenant.
Encryption
Encryption in transit and at rest is standard.
Audit Logging + Deletion
Audit trails
Job records capture who initiated a run, when it ran, what inputs it used, and where outputs were written. Operational logs support governance and incident response.
Deletion
Deletion can be performed by tenant and by dataset/output category. Retention is aligned to customer governance requirements.
AI-Assisted Evidence Drafting
No training on customer data
Evidence Layer
Drafting Layer
AI Usage
How AI is used
AI drafts narratives and structures information from retrieved evidence. Outputs are designed to be reviewed and approved by human experts.
What AI is not used for
AI does not "invent" evidence. AI does not make final filing decisions.
Model training
AutoDoc does not train models on customer data. Inputs are used only to generate customer outputs.
Vendor Quality
Completeness
Evidence coverage can be explained.
Accuracy
Evidence-grounded drafts plus human review.
Reproducibility
Consistent reruns and comparisons through job records.
Traceability
Narratives link back to source artifacts (tickets/commits/time entries).
What We Provide
- Evidence packs and structured documentation to support SR&ED preparation.
- Narrative drafts grounded in source evidence.
- Clear audit trail and evidence traceability.
Quality Pillars
Completeness
What was processed, when
Accuracy
Grounded + reviewed
Reproducibility
Reruns explainable
Traceability
Output -> evidence links