Data is the highest-risk input to AI systems. In agentic environments, agents can read, transform, and emit data across boundaries that were never intended to be crossed.
TealTiger enforces data governance before model invocation — not after generation.
Why Data Governance Is Different for Agents
Traditional data governance focuses on access control and retention policies. Agentic systems introduce new risks:
The risk is not just who accesses data — it’s how data flows through the agent pipeline.
Governance Focus Areas
1. Source Control
Only approved data sources are accessible to agents. Unapproved sources are denied at the policy layer.
| Control | Description |
|---|---|
| Source allowlist | Only named sources can be queried |
| Environment scoping | Different sources for dev vs prod |
| Classification gates | Regulated data requires explicit approval |
2. Purpose Binding
Data access is limited to declared purposes. An agent retrieving customer data for “support” cannot use it for “analytics.”
3. Deterministic Redaction
Sensitive fields are removed or masked before data reaches the model:
- PII (emails, phone numbers, SSNs, credit cards)
- Secrets and credentials
- Regulated fields (PHI, financial data)
Redaction is not optional or best-effort. It is a policy-enforced transformation.
4. Egress Boundaries
Data leaving the system is inspected and controlled:
- Approved destinations only
- Sensitivity-aware blocking
- Aggregation requirements for exports
Enforcement Model
Policies are evaluated before data reaches the model — not after generation. This prevents data leakage, cross-domain contamination, and regulatory violations by design.
Practical Checklist
- Maintain an allowlist of approved data sources per agent/workflow
- Require declared purpose for every data access
- Apply deterministic redaction before model invocation
- Inspect outputs for sensitive data before egress
- Emit evidence for every data access decision
- Scope data permissions by environment and tenant
Related
- Security Governance — Identity and access enforcement
- Evidence & Audit — Proving data governance decisions
- Compliance Enablement — Regulatory alignment