AI Assurance Evidence
AI Assurance Evidence for Regulated AI Systems
InfoSecured.ai helps regulated organizations translate AI governance obligations into control mappings, evidence registers, vendor AI evidence requests, human oversight records, and audit-ready workflows.
Evidence Status
Verified, pending, missing, or expired evidence records.
Control Coverage
Controls mapped to risks, owners, evidence, and review status.
Human Review
Reviewer authority, rationale, escalation, and override records.
Vendor Evidence
Documentation, limitations, monitoring, and shared responsibility.
Evidence-first governance
What AI Assurance Means
AI assurance is the practical work of showing that AI systems are governed, controlled, reviewed, monitored, and evidenced.
It connects AI systems, risks, controls, owners, vendors, human review records, exceptions, runtime signals, and audit outputs into a structure that risk, compliance, audit, and governance teams can review.
InfoSecured.ai focuses on the implementation layer: evidence registers, control mappings, vendor evidence requests, oversight records, audit-log schemas, and carefully scoped automation patterns.
Assurance questions
What AI Assurance Evidence Should Prove
A useful AI assurance workflow should answer the questions that risk, audit, compliance, governance, vendor risk, and technology teams will ask during review.
System Scope
Which AI system is being reviewed, where it is used, who owns it, and what process does it support?
Control Coverage
Which controls apply, which risks do they address, and who is accountable for each control?
Evidence Sufficiency
What artifact proves the control exists, when was it reviewed, and whether it is current and complete?
Human Review Quality
Where human review occurs, what authority the reviewer has, and whether rationale is documented.
Vendor Accountability
What the vendor provides, what remains opaque, and where shared responsibilities are documented.
Audit Export Readiness
Whether evidence can be packaged for audit, risk, compliance, or governance review without manual reconstruction.
Core domains
AI Assurance Evidence Domains
InfoSecured.ai organizes AI assurance around practical evidence domains that apply across regulated organizations, with financial services and AML as the first deep use case.
Evidence Register
Track AI systems, risks, controls, owners, evidence artifacts, review status, and audit export readiness.
View TemplatesVendor AI Risk
Request vendor documentation, transparency limits, monitoring evidence, audit rights, and incident terms.
Review Vendor AI RiskHuman Oversight
Document reviewer authority, escalation triggers, override logic, decision rationale, and exceptions.
Explore OversightControl Mapping
Connect obligations to controls, evidence requirements, accountable owners, and review workflows.
Explore Control MappingLLM / RAG Governance
Track prompts, retrieval, outputs, access, logging, evaluation, human review, and incident evidence.
View Risk DomainsFinancial Services / AML
Apply evidence-first assurance to AML, fraud, sanctions, credit risk, model governance, and vendor AI tools.
View Use CasesFeatured resource
AI Assurance Evidence Starter Kit
A practical starter kit for documenting AI systems, controls, evidence artifacts, vendor responsibilities, human review records, model-change evidence, and audit-ready outputs.
Download the Starter Kit- AI Evidence Register
- AI System Inventory
- Control Mapping Matrix
- Vendor AI Evidence Request List
- Human Oversight Checklist
- Model Risk vs. AI Risk Crosswalk
- Audit Evidence Request List
- Governance Exception Register
Audience
Who This Is For
InfoSecured.ai is built for professionals who need practical evidence artifacts, not generic AI commentary.
AI Governance
Inventory, control mapping, ownership, and assurance workflows.
Compliance
Obligation coverage, evidence review, and documentation gaps.
Internal Audit
Audit trails, evidence requests, control testing, and issue records.
Technology Risk
System risk, control evidence, governance gates, and remediation tracking.
Vendor Risk
Third-party AI documentation, audit rights, limitations, and shared responsibility.
Model Governance
Model-change records, monitoring evidence, explainability limits, and approvals.
AML Oversight
Alert review, analyst rationale, escalation, override, and audit-log evidence.
Security / GRC
LLM/RAG controls, access, logging, monitoring, incidents, and governance evidence.
Public proof-of-work
GridLock GRC
GridLock GRC is a public prototype for framework-agnostic AI assurance evidence mapping. It models how AI systems, risks, controls, owners, evidence artifacts, human oversight records, vendor evidence, runtime signals, exceptions, governance decisions, and audit exports can connect inside a structured register.
GridLock is not an enterprise GRC platform, automated compliance engine, formal audit tool, or model-validation system. It is a public artifact project showing how AI assurance evidence can be structured.
View GridLock GRCResearch basis
Research-Based, Carefully Scoped
InfoSecured.ai uses research synthesis, public framework analysis, and artifact development to study how AI assurance evidence can be structured for regulated systems.
Templates and research notes are educational. They should be validated against organizational policies, legal obligations, audit standards, regulator expectations, and primary sources before operational use.
Read Editorial StandardsBuild Audit-Ready AI Assurance Evidence
Turn AI governance obligations into mapped controls, evidence records, vendor documentation, human oversight logs, and audit-ready outputs.