LLM / RAG Risk

LLM and RAG Risk Evidence for Regulated AI Systems

Map large language model and retrieval-augmented generation risks to controls, owners, evidence records, access boundaries, human review, logging, monitoring, and audit-ready documentation.

View LLM / RAG Matrix Evidence Library

LLM / RAG Review View

Control Mapping

Prompt User input and system instruction

Prompt may include restricted data or unsupported instructions. Access, classification, filtering, and logging controls must be mapped.

Review Input controls

Retrieval Knowledge source lookup

RAG may retrieve stale, restricted, incomplete, or irrelevant content. Source governance and retrieval testing evidence are needed.

Gap Source control

Output Generated answer or action

Output may fabricate, omit context, or create unreviewed advice. Human review, output testing, and use-boundary evidence are needed.

Open Evidence missing

Evidence Audit-ready record

Prompt logs, retrieval tests, access reviews, output reviews, and incident records. LLM/RAG evidence should connect to the AI Evidence Register.

Mapped Register ready

The LLM / RAG risk question

LLM risk is not just hallucination risk.

LLM and RAG systems create evidence problems across inputs, retrieval sources, generated outputs, user permissions, logging, human review, monitoring, vendor dependencies, and downstream business action. The question is not only whether the answer is fluent. The question is whether the organization can show how the system was bounded, reviewed, monitored, and controlled.

What data can users submit?

What sources can the system retrieve?

What output requires review?

Who owns prompt and retrieval controls?

What logs are retained?

What evidence proves the control works?

LLM / RAG risk matrix

LLM / RAG Risk Evidence Matrix

This matrix separates common LLM and RAG risk areas from the controls and evidence needed for review. The goal is practical assurance: access boundaries, retrieval governance, output review, monitoring, incident response, and retained evidence.

Risk Area	What Can Go Wrong	Control Implication	Evidence Needed
Input Prompt and Data Exposure	Users submit confidential, regulated, personal, privileged, or unsupported information into prompts or uploaded context.	Define input rules, classification boundaries, data loss controls, prompt filtering, user training, and logging expectations.	Prompt policy, data classification rule, user guidance, logging record, DLP control evidence, training record.
Retrieval RAG Source Governance	The system retrieves stale, restricted, incomplete, unauthorized, low-quality, or irrelevant content from internal or external sources.	Govern retrieval sources, source freshness, access permissions, indexing rules, content quality, and source exclusion criteria.	Source inventory, data lineage record, access review, retrieval test, freshness check, indexing approval, exception log.
Output Hallucination and Unsupported Advice	The system generates plausible but incorrect, incomplete, misleading, unsupported, or overconfident outputs.	Require output testing, use boundaries, confidence warnings, citation checks, human review, and prohibited-use controls.	Evaluation summary, output review record, test cases, issue log, human oversight note, user-facing limitation notice.
Access Permission and Segregation Failure	Users receive outputs based on content or permissions they should not access, especially through retrieval or tool integrations.	Apply role-based access, source-level permissions, tenant separation, least privilege, and output boundary testing.	Access review, permission test, RBAC mapping, tenant isolation evidence, retrieval boundary test, security review.
Action Tool and Workflow Misuse	LLM agents or embedded tools trigger workflow actions, summaries, recommendations, tickets, emails, or decisions without adequate review.	Define action boundaries, approval gates, tool permissions, human-in-the-loop requirements, and rollback procedures.	Tool permission map, approval log, action audit trail, workflow control, override record, rollback evidence.
Monitoring Drift, Abuse, and Incident Gaps	Prompt abuse, output degradation, retrieval drift, model changes, unsafe content, or incidents are not detected or retained.	Monitor prompts, outputs, incidents, user behavior, retrieval quality, model changes, and recurring failure patterns.	Monitoring dashboard, incident record, model-change note, abuse report, issue log, trend review, remediation record.

Evidence request areas

What LLM / RAG review should request.

LLM and RAG assurance should request evidence about inputs, retrieval sources, output review, access boundaries, monitoring, incident response, vendor terms, and human oversight.

Input and Access Evidence

Prompt-use policy and user guidance.
Data classification and restricted-data rules.
Role-based access and permission review.
Prompt logging and retention expectations.

Retrieval and Output Evidence

RAG source inventory and source approval record.
Retrieval testing and source freshness checks.
Output evaluation and issue tracking.
Human review and escalation evidence.

Operating Evidence

Model, prompt, and retrieval change records.
Incident response and unsafe-output records.
Monitoring dashboard or review notes.
Vendor terms, limitations, and audit evidence.

Control mapping logic

Map the LLM/RAG evidence chain.

Use Case Identify the business process, user group, decision context, output type, and whether the system supports or influences a regulated workflow.

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Input Boundary Define what users may submit, what data is prohibited, what uploads are allowed, and how restricted content is controlled.

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Retrieval Boundary Map approved sources, indexing rules, freshness requirements, permission boundaries, and source-quality expectations.

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Output Boundary Define where outputs can be used, what requires review, what must not be automated, and how unsupported outputs are handled.

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Control Owner Assign accountability for prompt rules, retrieval controls, access review, monitoring, escalation, incident response, and evidence updates.

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Evidence Record Retain prompt logs, retrieval tests, output reviews, access reviews, incident records, model-change notes, and human oversight evidence.

Practical artifact

LLM / RAG Control Matrix

A structured artifact for mapping LLM and RAG risks to controls, owners, evidence items, review status, human oversight, vendor dependencies, and audit-readiness.

View Evidence Library Back to AI Risk Domains

LLM use-case inventory
Prompt and input policy
RAG source inventory
Access and permission review
Retrieval test evidence
Output evaluation records
Human review checklist
Incident and unsafe-output log
Model or prompt change notes
Vendor limitation statement

Common LLM / RAG gaps

Where LLM/RAG risk becomes an audit-readiness problem.

These gaps make it difficult to show that an LLM or RAG system is bounded, monitored, reviewed, and connected to evidence.

No Approved Source Inventory

The system retrieves from knowledge sources, but the organization cannot show which sources are approved, current, restricted, or excluded.

Prompt Data Exposure

Users can submit sensitive, confidential, regulated, or privileged information without clear rules, monitoring, or retention controls.

Unsupported Output Use

Generated outputs are used in workflows without enough review, source checking, limitation disclosure, or escalation.

Weak Retrieval Testing

The RAG layer is not tested for stale content, missing context, restricted sources, irrelevant results, or permission boundary failures.

No Human Review Trigger

The organization has not defined when outputs require human review, escalation, override, or rejection before downstream action.

Logs Are Not Reviewable

Prompt, retrieval, output, access, incident, and change records are scattered or not retained in a form that supports governance review.

GridLock GRC

LLM/RAG review as structured evidence.

In GridLock GRC, an LLM/RAG review should connect the AI system, use case, prompt boundary, retrieval source, risk, control, owner, evidence item, human review event, incident record, exception, and remediation action.

LLM Use Case → Prompt Boundary → Retrieval Source → Risk → Control → Evidence → Review Status

This converts LLM/RAG governance from scattered guidance into a traceable evidence chain.

GridLock GRC is not production software, a certified compliance platform, legal guidance, formal audit guidance, or a model-validation system. It is a public proof-of-work project for AI assurance evidence mapping.

View GridLock GRC

Related artifacts

Build LLM/RAG risk into the evidence library.

LLM / RAG Risk should connect directly to the Evidence Library, AI Risk Domains page, Vendor AI Risk page, Human Oversight page, and GridLock GRC prototype.

AI Evidence Register

Central register for systems, controls, owners, prompt/retrieval evidence, review status, exceptions, and remediation.

Open Evidence Library

Vendor AI Risk

LLM/RAG systems often depend on vendor models, APIs, platforms, embeddings, retrieval tools, or hosted AI features.

Open Vendor AI Risk

Human Oversight

High-impact LLM outputs may require review, escalation, rationale, override records, and retained human oversight evidence.

Open Human Oversight

Use notice

Independent research and portfolio artifacts.

InfoSecured.ai publishes independent AI assurance research, templates, and public proof-of-work artifacts for education, review, adaptation, and validation by qualified internal teams.

Materials are not legal advice, audit advice, certification advice, regulatory advice, model-validation advice, or a substitute for organization-specific professional review.

Read Editorial Standards

Make LLM and RAG risk reviewable.

Map prompts, retrieval sources, outputs, access controls, vendor dependencies, human review, incidents, monitoring, and evidence records.

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