Data-flow map
Sources, processing steps, model endpoints, storage locations, exports, and reviewer touchpoints for the selected workflow.
Security & Governance
Security is the architecture, not a feature.
We build AI systems for environments where client data leakage is unacceptable: on-premise, VPC, tenant-isolated, or client-approved deployment paths.
01
Private Deployment
We scope workflows for your VPC, on-premise hardware, or dedicated single-tenant cloud environments where appropriate. Client files are handled through approved model and storage paths.
02
Strict Access Controls (RBAC)
AI should only see what the user is authorized to see. Workflows are designed around row-level security, role-based access, least privilege, and reviewer permissions before answering sensitive queries.
03
Audit Trails
Data extraction, natural-language queries, reviewer edits, and approvals can be logged so compliance teams can inspect how important outputs were produced.
04
Data Discovery & Masking
We implement readiness workflows to discover, classify, and mask sensitive data before approved material reaches an AI model for processing.
Security review pack
Give InfoSec the artifacts they need before sensitive data moves.
A workflow pilot should be reviewable before it is connected to confidential files. We prepare the operating and security details around the specific workflow being tested.
Access model
Named user groups, roles, least-privilege rules, source permissions, reviewer permissions, and audit responsibilities.
Model and provider boundary
Approved model endpoints, data-sharing assumptions, retention settings, logging scope, and client-owned infrastructure options.
Pilot control checklist
NDA/DPA path, sample-data approach, deletion rules, approval checkpoints, incident contacts, and rollout decision criteria.
Procurement Readiness
Questions your InfoSec team will ask before a pilot.
Your team should not have to guess how an AI workflow touches confidential data. We clarify the deployment boundary, data flow, access model, retention, and reviewer controls before engineering work begins.
Where does client data run?
Deployment is scoped around the client's security boundary: client VPC, on-premise, or dedicated single-tenant cloud where appropriate. The final model, storage, and integration path is agreed before sensitive material is shared.
Do client files train public models?
No. Engagements are designed around approved model endpoints and private deployment patterns so client files, workpapers, data rooms, and ERP data are not used to train public models.
What can we review before a pilot?
We can provide the proposed data-flow map, model/provider list, access-control design, logging and retention approach, deployment responsibility matrix, and review checkpoints for the selected workflow.
How do you handle retention and logging?
Retention, export, audit logging, and deletion are defined per engagement. For sensitive workflows, we prefer client-owned storage, minimal retained payloads, and logs that preserve traceability without exposing more content than necessary.
Can this use our identity and access controls?
Where the environment allows it, workflows are designed around client IdP, role-based access, least privilege, and audit trails so users only see the sources and outputs they are authorized to review.
What happens before legal and InfoSec approval?
We can work from sanitized examples during discovery, execute an NDA before sensitive review, and support DPA or security addendum discussions as part of contracting.
Need to pass InfoSec review?
We can walk through the deployment model, data flow, access controls, retention, and artifacts your InfoSec team will need before a pilot.