Evidence defines how TealTiger proves that AI governance controls were actually enforced, not just documented.
The Evidence Governance Hub defines normative requirements for proof, capturing verifiable artifacts that demonstrate control execution and support audit review.
In agentic AI systems, governance credibility depends on runtime proof: what decisions were evaluated, what actions were allowed or blocked, and which controls were applied at execution time. This hub focuses on evidence as a first‑class system output, not an after‑the‑fact artifact.
What This Hub Covers
- What qualifies as audit‑grade governance evidence
- Deterministic, execution‑time decision records
- Tamper‑resistant evidence pipelines
- Evidence‑driven compliance and audit readiness
- Separation of governance evidence from operational telemetry
Why Evidence Matters
Without strong evidence:
- Governance claims cannot be verified
- Audits rely on explanations instead of proof
- Blocked actions leave no trace
- Compliance becomes manual and retrospective
- Trust in agentic systems erodes
TealTiger treats evidence as the source of truth for governance, risk, and compliance.
Core Articles
- Evidence Model for Agentic AI Governance
- The primary evidence model for capturing, protecting, and querying governance decisions in agentic AI systems.
Additional Articles
- What Counts as Evidence for AI Governance
- Building Tamper‑Resistant Evidence Pipelines
- Evidence‑Driven Compliance for AI Systems
These articles expand the evidence model by showing how runtime decision records, immutability, and continuous proof replace narrative explanations and manual audit preparation.
Related Insights
These Insights clarify how evidence is interpreted, operationalized, and reviewed across governance functions: