AI Assurance Evidence

Build Audit-Ready Evidence for Regulated AI Systems

InfoSecured.ai publishes research-backed AI assurance articles, templates, and public proof-of-work artifacts for control mapping, vendor AI risk, human oversight, LLM governance, financial-services AI risk, and compliance automation.

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Built for AI GRC oversight teams.

Evidence Library

Templates for AI evidence registers, vendor AI review, human oversight, LLM/RAG controls, and crosswalks.

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Financial AI Risk

Research notes on banking, AML, fraud, vendor AI risk assessment, model governance support, and audit-ready evidence.

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Research Notes

Science-backed articles translating AI governance research into practical controls and evidence questions.

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GridLock GRC Prototype

A public portfolio project exploring structured AI assurance evidence mapping.

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We Turn AI Governance Research Into Reviewable Evidence

AI governance becomes useful when risks, controls, owners, review points, and evidence records can be traced. InfoSecured.ai focuses on the operational layer of AI assurance: what must be documented, what must be reviewed, and what evidence should exist before AI use is challenged.

The goal is practical proof-of-work: research-backed articles, templates, control matrices, and the GridLock GRC prototype.

AI Assurance Tracks

Research-backed article tracks and public artifacts for AI assurance, risk, auditability, and governance workflows.

AML AI Oversight​

Track alert review, escalation criteria, analyst rationale, overrides, false-positives, and audit-log evidence.

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Vendor AI Evidence

Request and organize vendor model documentation, monitoring evidence, audit rights, and incident terms.

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Model Risk Support

Map traditional model-risk practices to AI-specific evidence gaps involving drift, explainability, and more.

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Why Evidence-First AI Assurance Matters

Regulated organizations need more than AI principles. They need traceable records showing what system was used, which control applied, who reviewed it, what evidence existed, and what changed over time.

Map Controls to Evidence-

Connect AI obligations, controls, owners, evidence artifacts, and review status in one traceable structure.

Human Oversight Records

Document reviewer authority, decision rationale, escalation triggers, overrides, and exceptions.

Vendor Evidence Discipline

Track vendor documentation, shared responsibilities, limitations, audit rights, and monitoring expectations.

Audit-Ready Records

Structure evidence so risk, compliance, audit, and governance teams can review decisions without starting from scratch.

Research Notes

Practical notes on AI assurance evidence, AML oversight, vendor AI risk, model-risk support, and compliance automation limits.

Build Audit-Ready Financial AI Assurance Evidence

Turn AI governance obligations into mapped controls, evidence records, vendor documentation, human oversight logs, model-risk artifacts, and audit-ready outputs.

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Built as Public Proof-of-Work

InfoSecured.ai and GridLock GRC are independent research and artifact projects focused on operational AI assurance evidence.

AI Evidence Register

A structured template for mapping AI use cases, risks, controls, owners, evidence artifacts, human review points, and audit status.

Vendor AI Risk Questionnaire

A review template for third-party AI systems, embedded AI tools, model APIs, and AI-enabled SaaS.

GridLock GRC Prototype

A portfolio project exploring framework-agnostic AI assurance evidence mapping through structured files and demo workflows.

Subscribe to the AI Assurance Brief

Research-backed notes on AI assurance evidence,
vendor AI risk, human oversight, LLM/RAG
governance, and compliance automation.

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