Policy, retention, and governance
Define what is allowed, how long artifacts are retained, and how exceptions are reviewed.
Compliance is usually bolted on
Most AI platforms treat governance as a checklist — separate from runtime behavior (if addressed at all).
Policy configurations drift from what actually executes, audit trails are incomplete, and retention rules are enforced manually.
How Threada handles it differently
Policy is resolved at runtime — every WorkItem checks retention class, approval requirements, and escalation thresholds before execution.
Governance controls are the same controls that route, approve, and execute work — not a separate layer to maintain.
Retention controls
- Set retention by data class and policy scope
- Apply archival and deletion workflows with audit records
- Keep retention aligned with business and regulatory requirements
Governance controls
- Policy overlays with clear scope and precedence
- Approval requirements for high-risk actions and workflows
- Review queues for low-confidence or policy-exception scenarios
What you get
- Every policy decision is traceable — who defined it, when it resolved, and what it allowed
- Retention and archival run automatically based on data classification
- Approval chains integrate with existing identity and role infrastructure
- Exception handling follows defined escalation paths instead of silent fallbacks
Policy is part of runtime behavior
In Threada, governance is not a separate checklist. Policy resolution determines routing, approvals, and execution outcomes in real time.
Governance FAQ
Does Threada replace our existing compliance tooling?
How are policies versioned?
Can we export audit records?
Govern AI work with confidence
Define policy, enforce retention, and maintain audit trails — all from one control plane.