Data Governance defines how TealTiger ensures agentic AI systems access, use, retain, and transmit data only for explicitly approved purposes.
The Data Governance Hub defines normative controls for data access, minimization, boundary enforcement, and lifecycle governance in AI workflows.
Unlike traditional systems, agentic AI reasons over data dynamically at runtime—retrieving context, persisting memory, invoking tools, and producing outputs that may cross trust boundaries. This hub focuses on deterministic, runtime‑enforced data governance, not post‑hoc review.
What This Hub Covers
- Purpose‑bound data access for agentic workflows
- Prevention of cross‑context and cross‑task data leakage
- Inference‑time data enforcement
- Memory isolation and retention boundaries
- Audit‑grade evidence for data usage decisions
Why Data Governance Matters
Without governance at runtime:
- Data escapes its original purpose
- Sensitive context leaks across tasks or users
- Memory becomes an ungoverned data store
- Compliance controls apply only on paper
- Audit evidence is incomplete or inferred
TealTiger treats data governance as an execution‑time control, not a static data management policy.
Core Articles
- Data Governance for Agentic AI
- The primary data governance model for enforcing purpose limitation, context boundaries, and evidence during execution.
Additional Articles
- Purpose‑Bound Data Access for Agentic AI
- Preventing Cross‑Context Data Leakage in Agentic Systems
- Data Governance at Inference Time
These articles expand the data governance model by showing how purpose binding, context isolation, and inference‑time enforcement prevent accidental data misuse and leakage in agentic systems.
Related Insights
These Insights show how data governance effectiveness is assessed through evidence-backed outcomes: