Governance

Four layers.
One governed lifecycle.

Not model monitoring. Not data cataloguing. Governance that spans from source data through model inference through agent action to business decision — every step traceable.

Tantor governance layers — Data, Model, Agent, Decision with end-to-end lineage

Why this matters.
Right now.

Without governed governance, these problems compound silently with every new data source and AI deployment.

Governance exists at one layer only.

Model monitoring tracks drift but cannot trace decisions through federated data. Oversight is present but incomplete.

Agents operate beyond governance.

AI agents make decisions and invoke other agents — often with no centralised visibility into what data they accessed.

Decisions cannot be reconstructed.

When regulators ask why a credit application was declined, most architectures cannot produce the full chain.

From input to
governed output.

1

Data

Access controls, PII masking, and lineage enforced at the federation query layer

2

Model

Bias, drift, and explainability monitored — connected to data lineage below

3

Agent

Permissions, guardrails, and human-in-the-loop gates applied to every agent action

4

Decision

Full reconstruction from source data through model inference to business outcome

What Governance
delivers.

📊

Data governance.

Access controls, PII protection, classification, and lineage from the moment data enters the platform.

🧬

Model governance.

Bias detection, drift tracking, fairness metrics, and explainability connected to the data layer beneath.

🤖

Agent governance.

What agents can do, what data they access, what approvals are required — governed by policy, not convention.

⚖️

Decision governance.

The complete audit trail connecting all layers — outcome fairness, full reconstruction, regulator-ready.

Why governance cannot wait.

₹250Cr
Max DPDP penalty per violation
May '27
DPDP full compliance deadline
₹22Cr
Average data breach cost — India 2025
2.27M
Cybersecurity incidents in India 2024

Governance from
the first query.

See how Tantor's four-layer governance makes every AI decision traceable, explainable, and regulator-ready.

Frequently asked
questions.

What is AI governance in Tantor?

Governance in Tantor spans four interconnected layers — data, model, agent, and decision — so every AI output is explainable, fair, and audit-ready. Unlike point tools that govern only one layer, Tantor governs the full lifecycle from source data through model inference and agent action to the final business decision.

Why isn't model monitoring enough for AI governance?

Model monitoring tracks drift and performance but cannot trace a decision back through federated data, agent actions, and policy checks. Complete AI governance connects all four layers, so when a regulator asks why a decision was made, you can reconstruct the entire chain — data lineage, model reasoning, guardrails, and human oversight.

How does governance make AI decisions audit-ready?

Tantor's governance framework records access controls, PII masking, quality attestations, guardrail events, and human-in-the-loop actions as part of one continuous audit trail. Every decision is reconstructable and exportable for regulatory examination, aligned to DPDP and RBI requirements.

Which regulations does Tantor governance align with?

Tantor's governance is built to align with the DPDP Act, RBI guidelines, and GDPR — by architecture rather than configuration. This makes it suitable for regulated industries where explainability and accountability are mandatory.