Evidence Page

RAG Security Evidence Pack

A scan-friendly evidence page for rag-security-platform focused on practical RAG security design: trust boundaries, retrieval validation, guardrails, tool authorization, runtime monitoring, and auditability.

Website homepage

Executive Summary

rag-security-platform is presented as a practical security reference for Retrieval-Augmented Generation (RAG) systems. It focuses on design and review artifacts that help teams reason about risk before production release.

Problem Addressed

Many RAG implementations prioritize speed and feature delivery but leave security boundaries under-defined. That gap increases exposure to prompt injection, weak retrieval integrity, unsafe tool usage, and difficult post-incident analysis.

What the Project Contributes

A clear trust-boundary view across ingestion, retrieval, generation, and tool paths.
A control model for retrieval checks, policy guardrails, and output validation.
A reviewer-oriented evidence format linking controls to public artifacts.

Threat Model

The threat model centers on realistic attack paths in RAG workflows: hostile instructions in prompts/documents, retrieval poisoning, unauthorized tool actions, sensitive output leakage, and weak logging for investigations.

Security Gaps Identified

Unclear boundaries between untrusted input and privileged actions.
Insufficient validation of retrieved context prior to model generation.
Inconsistent policy enforcement before tool execution.
Limited structured logging for traceability and audit review.

Controls Introduced

Trust-boundary mapping at each pipeline stage.
Retrieval validation and context screening controls.
Policy guardrails for prompt handling, model output, and tool authorization.
Runtime monitoring and audit-focused event logging.

Validation Approach

Validation is framed as repeatable checks against documented threats and controls, with emphasis on traceable artifacts and conservative claims rather than performance marketing.

Expected Risk Reduction

Expected risk reduction is directional: fewer unsafe context transitions, tighter control of tool actions, and stronger audit readiness. No adoption, customer, or impact metrics are asserted on this page.

Artifacts Produced

RAG threat model
Security architecture pattern
Control matrix
Validation checklist
Launch-readiness evidence template
Observability/dashboard concept
Implementation starter references

Public Evidence

Public evidence is provided through repositories and website materials that describe the security model, controls, and readiness framing.

Controls Matrix

Risk Area	Example Threat	Control in rag-security-platform	Evidence
Prompt Injection	Malicious instructions in prompts or retrieved documents attempt to override intended behavior.	Input/context guardrails and policy checks before generation and before tool actions.	Public project and site materials emphasizing guardrails and secure RAG patterns.
Retrieval Poisoning	Low-integrity or compromised sources contaminate retrieved context.	Retrieval validation and context-screening controls before assembly.	Published focus on retrieval security and trust-boundary design.
Unsafe Tool Use	Model-triggered tool/API calls execute without sufficient authorization checks.	Explicit policy guardrails and authorization gates for tool execution.	Public positioning around tool authorization and agent safety.
Data Leakage	Sensitive content is exposed in model responses or tool output chains.	Output validation and policy filters before response release.	Security-readiness resources that cover validation and output safeguards.
Weak Auditability	Events cannot be reconstructed reliably due to missing or inconsistent traces.	Structured runtime monitoring and audit-oriented logging points.	Public content emphasizing runtime monitoring and auditability.

Prompt Injection

Example Threat: Malicious instructions in prompts or retrieved documents attempt to override intended behavior.
Control in rag-security-platform: Input/context guardrails and policy checks before generation and before tool actions.
Evidence: Public project and site materials emphasizing guardrails and secure RAG patterns.

Retrieval Poisoning

Example Threat: Low-integrity or compromised sources contaminate retrieved context.
Control in rag-security-platform: Retrieval validation and context-screening controls before assembly.
Evidence: Published focus on retrieval security and trust-boundary design.

Unsafe Tool Use

Example Threat: Model-triggered tool/API calls execute without sufficient authorization checks.
Control in rag-security-platform: Explicit policy guardrails and authorization gates for tool execution.
Evidence: Public positioning around tool authorization and agent safety.

Data Leakage

Example Threat: Sensitive content is exposed in model responses or tool output chains.
Control in rag-security-platform: Output validation and policy filters before response release.
Evidence: Security-readiness resources that cover validation and output safeguards.

Weak Auditability

Example Threat: Events cannot be reconstructed reliably due to missing or inconsistent traces.
Control in rag-security-platform: Structured runtime monitoring and audit-oriented logging points.
Evidence: Public content emphasizing runtime monitoring and auditability.

How it works

Trust boundaryControl pointValidation pointEvidence point

Document Ingestion

Trust boundary

Untrusted files/content enter the pipeline and require source and integrity checks.

Retrieval Layer

Control point

Retrieved candidates are filtered by trust, policy, and relevance constraints.

Context Assembly

Validation point

Context is checked before prompt construction to reduce malicious carry-through.

Model Generation

Control point

Prompt and response policies constrain unsafe or non-compliant behavior.

Tool Use

Trust boundary

External tools/APIs are isolated by authorization scope and execution policy.

Output Validation

Validation point

Responses are screened for policy, leakage, and structural validity before release.

Logging / Audit

Evidence point

Events and decisions are logged to support review, triage, and audit trails.

This architecture map shows where trust boundaries are crossed, where controls and validation are applied, and where audit evidence is captured across the RAG pipeline.

Who should use this

AI security practitioners
Cybersecurity professionals
AI platform engineers
Teams deploying RAG systems
Organizations evaluating trustworthy AI architectures

Measured outputs

Three public GitHub repositories are linked in this evidence pack: rag-security-platform, myStarterKit, and myStarterKit-maindashb.
The controls matrix on this page documents five risk areas for reviewer inspection.
The architecture section documents seven stages from Document Ingestion through Logging / Audit.
Public framework and observability references are provided as implementation guidance, not adoption claims.