# RAG Security Assessment Services Canonical URL: https://srql.com/services/rag-security-assessment/ ## RAG Security Assessment Services Secure retrieval-augmented generation systems across source governance, retrieval permissions, vector databases, prompt injection risk and audit evidence. Retrieval-Augmented Generation systems are becoming part of enterprise search, AI copilots, compliance automation, security operations, customer support and internal knowledge workflows. These systems can improve access to information, but they also introduce new security risks when documents, embeddings, retrieval logic and user permissions are not governed properly. ServQual helps organizations assess RAG security across document ingestion, source governance, vector database controls, retrieval permission models, chunking risks, prompt injection exposure, sensitive data handling and retrieval audit logs. ## Why RAG Security Matters A RAG system connects AI outputs to enterprise knowledge sources. If the source data, retrieval pipeline or permission logic is weak, the AI system may expose sensitive information, retrieve the wrong context, rely on poisoned chunks or produce unsupported answers. RAG security is not only an AI issue. It affects data protection, access control, privacy, compliance evidence, knowledge governance, legal review, customer assurance and operational trust. A secure RAG architecture should control what data is ingested, how it is chunked, where embeddings are stored, who can retrieve information, how access is enforced and how retrieval events are audited. ## What ServQual Helps With ** ** RAG Security Assessment Assess the security posture of retrieval pipelines, vector databases, document ingestion workflows, access control models, source governance and AI response grounding. ** ** Vector Database Security Review Assess vector database access, tenant isolation, embedding storage, metadata controls, encryption, administrative access, backup handling and logging. ** ** Chunk Poisoning Risk Review Assess whether malicious, outdated, misleading or unapproved content could be embedded and retrieved by the RAG system. ** ** Secure Document Ingestion Review Assess ingestion controls for file uploads, connectors, data classification, malware risk, content approval, duplicate handling and source validation. ** ** Retrieval Audit Log Review Assess whether the system records who searched, what was retrieved, which sources were used, what answer was generated and whether restricted content was accessed. ** ** Retrieval Permission Model Review Review whether users can only retrieve the documents and chunks they are authorized to access. Validate permission inheritance, role-based access, tenant separation and source-level access control. ** ** Source Governance Review Review approved sources, source ownership, document classification, content lifecycle, freshness, retention, deletion and source trust rules. ** ** Prompt Injection Against RAG Review whether retrieved content can manipulate model behavior, override system instructions, disclose restricted data or produce unsafe outputs. ** ** PII Redaction Before Embedding Review whether personal data, sensitive data, credentials, secrets or regulated information is detected, minimized or redacted before embedding. ** ** RAG Governance Roadmap Create a prioritized remediation plan covering retrieval controls, data protection, source governance, logging, redaction, prompt injection resilience and operational evidence. ## Key RAG Security Risks We Assess ** ** Unauthorized Retrieval Users may retrieve chunks from documents they should not be allowed to access if the retrieval layer does not enforce permission boundaries. ** ** Sensitive Data Leakage Embeddings and retrieved chunks may expose personal data, confidential documents, credentials, secrets or regulated information. ** ** Chunk Poisoning Untrusted or manipulated content may be embedded into the knowledge base and later retrieved as trusted context. ** ** Prompt Injection Through Retrieved Content Documents may contain instructions that manipulate the AI system, override controls or cause disclosure of restricted information. ** ** Weak Source Governance RAG systems may use stale, duplicate, unapproved, unverified or low-quality sources, reducing answer accuracy and audit defensibility. ** ** Poor Auditability If retrieval events are not logged, organizations may not be able to investigate why an answer was produced or what source content was used. ** ** Tenant or Role Leakage Multi-user or multi-tenant systems may expose content across customers, teams, roles or regions if metadata filtering is weak. RAG Security Assessment Approach ** ** 1. Discover Identify RAG use cases, data sources, connectors, vector databases, document types, users, roles, AI workflows and business owners. ** ** 2. Classify Classify sources by sensitivity, business value, regulatory impact, owner, approval status, retention requirement and retrieval risk. ** ** 3. Assess Retrieval Controls Review retrieval permissions, metadata filters, role-based access, tenant isolation, source-level restrictions and authorization enforcement. ** ** 4. Assess Ingestion Controls Review document ingestion, chunking, approval workflow, duplicate handling, source validation, malware handling and data minimization. ** ** 5. Assess Vector Security Review vector database access control, encryption, administrative access, backup controls, logging, monitoring and separation boundaries. ** ** 6. Assess Prompt Injection Risk Evaluate whether retrieved content can influence the AI system in unsafe ways or cause sensitive data exposure. ** ** 7. Assess Logging and Evidence Review retrieval logs, source traceability, answer auditability, user activity records and incident investigation readiness. ** ** 8. Build Remediation Roadmap Prioritize improvements across permissions, source governance, redaction, logging, monitoring, vector security and RAG operating controls. ## Example Assessment Areas Retrieval permission model Vector database access control Retention and deletion handling Secure document ingestion Chunking and metadata design Chunk poisoning exposure Prompt injection against RAG PII redaction before embedding Sensitive data and secret handling Tenant and role isolation Retrieval audit logs Source citation and traceability Source governance and document approval Incident response for AI retrieval events Governance ownership and review workflow ## How SUSAN Supports RAG Governance SUSAN can support RAG governance by helping teams structure risks, controls, evidence and remediation actions within a broader governance, risk and compliance workflow. **With SUSAN, teams can:** Track RAG security risks and remediation actions Map RAG controls to governance and compliance requirements Support continuous assurance instead of one-time reviews Support leadership visibility through risk dashboards Connect AI security findings with privacy, cybersecurity and GRC workflows Maintain evidence for source governance and access control reviews ## Business Outcomes Reduced risk of sensitive data leakage Better vector database governance Lower risk of prompt injection through retrieved content Stronger retrieval access control Better evidence for compliance, legal, privacy and security reviews Improved source quality and trust Clearer ownership of RAG security controls Practical remediation roadmap for secure RAG adoption Better auditability of AI-generated answers ## Who Needs This Service? **This service is suitable for:** Organizations building AI copilots Enterprises using internal knowledge search with AI SaaS platforms embedding RAG features Security teams assessing AI retrieval risk Privacy teams reviewing personal data exposure GRC teams requiring source and evidence governance Legal and compliance teams concerned about answer traceability Cloud teams managing vector databases or AI pipelines Businesses using RAG for customer support, policy search or document automation ## FAQ ##### Most frequent questions and answers ** ** What is a RAG Security Assessment? A RAG Security Assessment reviews the security of retrieval-augmented generation systems, including document ingestion, source governance, vector databases, retrieval permissions, prompt injection risk, sensitive data handling and audit logs. ** ** Why does RAG security matter? RAG security matters because AI systems may retrieve sensitive, unauthorized, outdated, poisoned or misleading content if source governance and retrieval controls are weak. ** ** What is retrieval permission risk? Retrieval permission risk occurs when users can retrieve documents or chunks they are not authorized to access because the RAG system does not enforce source-level permissions correctly. ** ** What is chunk poisoning? Chunk poisoning occurs when malicious, misleading, outdated or unapproved content is embedded into a RAG knowledge base and later retrieved as trusted context. ** ** What is prompt injection against RAG? Prompt injection against RAG happens when retrieved content contains instructions that try to manipulate the AI system, override controls or expose restricted information. ** ** Does RAG security include vector database security? Yes. RAG security includes review of vector database access control, metadata filtering, tenant isolation, encryption, administrative access, backup handling and logging. ** ** Why is PII redaction before embedding important? PII redaction before embedding helps reduce the risk of personal data, secrets or regulated information being stored in embeddings or retrieved by unauthorized users. ** ** What are retrieval audit logs? Retrieval audit logs record user searches, retrieved chunks, source documents, generated answers and access decisions so organizations can investigate AI outputs and prove accountability. ** ** How is this different from AI Security Assessment? AI Security Assessment is broader and covers AI governance, inventory, vendor risk, data leakage and compliance readiness. RAG Security Assessment focuses specifically on retrieval pipelines, vector databases, source governance, chunk poisoning and retrieval audit logs. ** ** How is this different from LLM Security Testing? LLM Security Testing focuses on technical testing of model behavior, prompt injection, jailbreaks, guardrails and sensitive data leakage. RAG Security Assessment focuses on the retrieval layer and the data pipeline feeding the model. Start Your RAG Security Assessment Secure your retrieval pipeline before sensitive data, untrusted content or weak permissions become an AI security incident. ServQual helps organizations assess RAG security, strengthen source governance, reduce retrieval risk and build audit-ready AI retrieval controls. [Try SUSAN](http://scpauth.servqualit.com/) [Book a Discovery Workshop](https://srql.com/contact/)