Risk Assurance defines how TealTiger ensures agentic AI systems remain within approved risk boundaries throughout their lifecycle.
The Risk Governance Hub defines normative controls for continuous risk identification, assessment, and translation into enforceable governance actions
Unlike traditional systems, agentic AI can change behavior at runtime—expanding scope, increasing autonomy, and accumulating exposure without code changes. This hub focuses on continuous, execution‑time risk governance, not static risk assessments.
What This Hub Covers
- Continuous risk assessment during execution
- Detection and containment of risk drift in long‑lived agents
- Runtime linkage between risk signals and enforcement
- Risk‑driven restriction, approval, or blocking of actions
- Audit‑grade evidence for risk decisions
Why Risk Assurance Matters
Without runtime risk assurance:
- Risk accumulates silently over time
- Controls drift from original assumptions
- Incidents are detected only after impact
- Risk reviews become retrospective explanations
- Audit confidence erodes
TealTiger treats risk as a dynamic system property, continuously evaluated and enforced during execution.
Core Articles
- Risk Assurance for Agentic AI
- The primary risk assurance model for detecting, evaluating, and enforcing risk boundaries in agentic AI systems.
Additional Articles
- Continuous Risk Assessment for Agentic AI Systems
- Risk Drift in Long‑Lived AI Agents
- Linking Risk Signals to Governance Enforcement
These articles expand the risk assurance model by showing how continuous assessment, drift detection, and deterministic enforcement prevent silent risk escalation in agentic systems.
## Related Insights
These Insights explain how risk indicators are translated into enforceable governance actions:
- From Risk to Runtime: How Governance Enforcement Actually Works
- [AI Risk Signals from Runtime Telemetry](/governance/insights/