Zero-trust AI
ZTA-AI
Ask your company's data anything, without ever letting the language model see the data itself.
- Year
- 2025
- Role
- Systems & security
- Built with
- FastAPI, Postgres, Redis, RBAC / ABAC, Docker
- Source
- Repository
<0ms
p95 pipeline latency
0
raw data to the model
0
compliance frameworks
The threat model
Enterprises want to ask their internal data questions in plain language. What they cannot do is hand raw records, schemas, or credentials to a model that might memorize or hallucinate them. The usual RAG shortcut quietly stuffs all of that into the prompt and calls it a day.
The controls
- 01
Separated data access, business logic, and language generation completely. A deterministic compiler makes every decision; the model only renders an approved answer.
- 02
Modeled all data as immutable, versioned claims carrying provenance, sensitivity classification, and compliance tags.
- 03
Gated access with department-level RBAC plus attribute-based conditions on time, location, sensitivity, and anomaly signals.
- 04
Sandboxed the small language model with no database access, no tools, and no memory across requests, then validated every output back against its input claims to catch hallucination.
- 05
Logged every query, policy decision, and access event to an immutable audit trail, and mapped the whole system to BFSI, healthcare, and government compliance.
The guarantee
A deterministic, sub-second pipeline that answers in natural language while the model layer stays fully untrusted. The sensitive part of the system never sees the sensitive data.
Enforced by
Sandboxed SLM: no data, no tools, no memory
Claim-based immutable data model with provenance
RBAC + ABAC with department-level silos
Output validation against source claims
Immutable, queryable audit trail
Maps to RBI FREE-AI, DPDP, SEBI, GDPR, SOC 2, ISO 27001, HIPAA