Technical assurance evidence for governed AI systems
EvidenceBound maps a narrow technical control layer to broader governance frameworks. The objective is to produce inspectable evidence for operator decisions, not to market a technical receipt as legal compliance.
Controls that can produce evidence.
EvidenceBound binds exact evidence, policy, deterministic checks, negative states, approvals and recovery lineage. These artifacts can support governance processes when an organization maps them to its actual role, risk classification, policies and assurance program.
GOVERN · MAP · MEASURE · MANAGE
The NIST AI RMF is voluntary and use-case agnostic. Its Core organizes risk management into four functions. EvidenceBound addresses only selected technical evidence needs inside those broader organizational activities.
| RMF function | EvidenceBound support | Boundary |
|---|---|---|
| GOVERN | Versioned policy identity, explicit approval boundaries, durable decision receipts, accountable operator state. | Does not create governance culture, assign legal accountability or define an organization's risk appetite. |
| MAP | Declared evidence sources, action scope, dependency lineage and blast-radius context. | Does not determine all affected stakeholders, domain harms or socio-technical context. |
| MEASURE | Repeatable deterministic verification, conformance tests, negative-path evidence and retained result identity. | Does not replace domain TEVV, model evaluation, red teaming or independent assessment. |
| MANAGE | Fail-closed outcomes, interrupt/kill boundaries, correction lineage, recovery receipts and explicit proceed/block decisions. | Does not choose organizational risk treatment or authorize deployment on behalf of accountable operators. |
Official source: NIST AI Risk Management Framework and NIST AI RMF Playbook.
Evidence support for an AI management system
ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining and continually improving an AI management system. EvidenceBound can supply technical records into such a management system; it is not the management system itself.
Potential evidence contribution
Policy versioning, traceability, deterministic test receipts, human approval evidence, incident/correction lineage, retained action identity and recovery state.
Outside EvidenceBound
Organizational objectives, management responsibility, competency, legal obligations, internal audit scope, certification, broader risk treatment and continual-improvement governance.
Official source: ISO/IEC 42001:2023 — Artificial intelligence management system.
Technical evidence that may support regulated workflows
For AI systems that fall within regulated categories, the EU AI Act can require risk-management, documentation, traceability/logging, human oversight, robustness, cybersecurity and other controls. EvidenceBound can support some technical evidence around those duties, but applicability is a legal and system-classification question.
| Area | Possible EvidenceBound artifact | Not established by EvidenceBound |
|---|---|---|
| Risk controls | Versioned executable policy, blocked states, scoped action boundaries. | Complete statutory risk-management system or legal sufficiency. |
| Logging / traceability | Content-addressed task, evidence, policy, result and recovery lineage. | All logging duties, retention periods or regulator-specific formats. |
| Human oversight | Explicit approval requirement, interruption/kill boundary, operator decision receipt. | Whether the assigned human oversight arrangement satisfies the law. |
| Robustness / cybersecurity | Adversarial conformance tests, fail-closed negative paths, integrity checks. | Security certification, penetration-test result or overall system robustness. |
Official sources: European Commission — AI Act and European Commission — Navigating the AI Act.