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
Assess the security posture of retrieval pipelines, vector databases, document ingestion workflows, access control models, source governance and AI response grounding.
Assess vector database access, tenant isolation, embedding storage, metadata controls, encryption, administrative access, backup handling and logging.
Assess whether malicious, outdated, misleading or unapproved content could be embedded and retrieved by the RAG system.
Assess ingestion controls for file uploads, connectors, data classification, malware risk, content approval, duplicate handling and source validation.
Assess whether the system records who searched, what was retrieved, which sources were used, what answer was generated and whether restricted content was accessed.
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.
Review approved sources, source ownership, document classification, content lifecycle, freshness, retention, deletion and source trust rules.
Review whether retrieved content can manipulate model behavior, override system instructions, disclose restricted data or produce unsafe outputs.
Review whether personal data, sensitive data, credentials, secrets or regulated information is detected, minimized or redacted before embedding.
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
Users may retrieve chunks from documents they should not be allowed to access if the retrieval layer does not enforce permission boundaries.
Embeddings and retrieved chunks may expose personal data, confidential documents, credentials, secrets or regulated information.
Untrusted or manipulated content may be embedded into the knowledge base and later retrieved as trusted context.
Documents may contain instructions that manipulate the AI system, override controls or cause disclosure of restricted information.
RAG systems may use stale, duplicate, unapproved, unverified or low-quality sources, reducing answer accuracy and audit defensibility.
If retrieval events are not logged, organizations may not be able to investigate why an answer was produced or what source content was used.
Multi-user or multi-tenant systems may expose content across customers, teams, roles or regions if metadata filtering is weak.
RAG Security Assessment Approach
Identify RAG use cases, data sources, connectors, vector databases, document types, users, roles, AI workflows and business owners.
Classify sources by sensitivity, business value, regulatory impact, owner, approval status, retention requirement and retrieval risk.
Review retrieval permissions, metadata filters, role-based access, tenant isolation, source-level restrictions and authorization enforcement.
Review document ingestion, chunking, approval workflow, duplicate handling, source validation, malware handling and data minimization.
Review vector database access control, encryption, administrative access, backup controls, logging, monitoring and separation boundaries.
Evaluate whether retrieved content can influence the AI system in unsafe ways or cause sensitive data exposure.
Review retrieval logs, source traceability, answer auditability, user activity records and incident investigation readiness.
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
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.
RAG security matters because AI systems may retrieve sensitive, unauthorized, outdated, poisoned or misleading content if source governance and retrieval controls are weak.
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.
Chunk poisoning occurs when malicious, misleading, outdated or unapproved content is embedded into a RAG knowledge base and later retrieved as trusted context.
Prompt injection against RAG happens when retrieved content contains instructions that try to manipulate the AI system, override controls or expose restricted information.
Yes. RAG security includes review of vector database access control, metadata filtering, tenant isolation, encryption, administrative access, backup handling and logging.
PII redaction before embedding helps reduce the risk of personal data, secrets or regulated information being stored in embeddings or retrieved by unauthorized users.
Retrieval audit logs record user searches, retrieved chunks, source documents, generated answers and access decisions so organizations can investigate AI outputs and prove accountability.
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.
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.
