RAG Integrity

RAG integrity refers to the trustworthiness and accuracy of retrieval-augmented generation systems, which enhance AI models by pulling relevant information from external knowledge sources before generating responses. Organizations must ensure that retrieved documents are current, uncontaminated, and come from authoritative sources, as faulty source material directly compromises the quality and reliability of AI outputs. For enterprise governance, RAG integrity is critical because it affects regulatory compliance, customer trust, and liability exposure when AI systems generate responses based on incorrect or manipulated source data.

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ResearchGlobal2026-09-02

Cyble's 2026 Report Codifies Untrusted-Input and Least-Privilege as Baseline Agent Controls

Cyble's AI Threat Landscape Report 2026 catalogues the attack surfaces surrounding agentic AI deployments, including prompt injection, RAG poisoning, vector store manipulation, and API credential theft. The report recommends that enterprises treat all content ingested by AI systems as untrusted, enforce least-privilege boundaries for every agent, and conduct adversarial red teaming as a standard operational control. Its findings represent a threat-intelligence codification of risks that regulators and auditors are increasingly treating as the baseline for reasonable AI security.

Cyble agent security red teaming least privilege RAG integrity prompt injection API credential management agentic AI