compliance_monitoring · workflow
Building Secure RAG Applications with Realm and ApertureDB
RAG-based AI chatbots lack native role-based access control, potentially exposing sensitive enterprise data — including personal information, company financials, and intellectual property — to unauthorized users, creating business and regulatory risk.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Crawl enterprise data sources
Realm's connectors crawl enterprise systems including SharePoint, OneDrive, and AWS S3 to discover users, groups, resources, and access policies.
Tools used
RealmApertureDBLLM
Outcome
ApertureData claims ApertureDB lowers setup time by 6-9 months, speeds up development by 10X, delivers savings upwards of $2M per team of 10, and performs 2-35X better than contemporary databases.
What failed first
Traditional RBAC systems struggle with scale and flexibility at enterprise level, and link-based document sharing creates gaps where chatbots serve restricted content to anyone who holds the link — a major cause of data leakage.
Results
Time saved6-9 months
Volume10X
Cost replacedupwards of $2M
Grounding & classification
Source type: technical build writeup
19 fields verified against source quotes.
enterprise searchragknowledge basefailure mode describedtools describedworkflow describedsoftwarecost reductioncycle time reductiontechnical build writeupcompliance monitoringrag answering