Secure AI RAG System
Creole Studios built a production-ready Secure AI RAG System that allows enterprise users to safely query internal knowledge bases without exposing confidential data across roles. The system combines identity-aware document retrieval, prompt injection defense, automatic PII masking, audit logging, Docker-based deployment, and CI/CD security scanning to create a secure AI layer ready for compliance-driven environments. As more businesses adopt AI inside internal workflows, the need for secure automation, controlled deployment, and reliable DevOps practices is becoming critical. This is one example of how AI is transforming DevOps from simple deployment automation into secure, self-defending infrastructure.
Remote
5K – 10K USD
3 - 6 Months
Web
About the Project
Building a Self-Healing Security Ecosystem for Enterprise AI Applications.
Our team developed the Secure AI RAG System to address a critical gap in enterprise AI adoption: how do you let employees query a company knowledge base through an AI assistant without leaking confidential data across role boundaries?
The answer required more than a chatbot. It demanded a defense-in-depth architecture that enforces identity at every layer of the retrieval pipeline. For teams building similar secure deployment environments, structured DevOps consulting services can help align infrastructure, automation, security, and release workflows from the start.
The system combines a Flask REST API, a custom Secure RAG Engine, and three independent security modules, including PII detection, prompt-injection defense, and audit logging, into a single containerized service that is CI/CD-ready from day one.
Project Challenges
Building this autonomous, identity-aware environment required solving several unique challenges in the AI security lifecycle:
Unrestricted AI Data Access : Traditional RAG systems retrieve the most relevant documents regardless of who is asking. In an enterprise setting this means a junior employee could inadvertently receive salary data, acquisition plans, or security credentials simply by phrasing a query cleverly.
Prompt Injection & Jailbreak Attacks :Malicious users can craft inputs designed to override system instructions, extract the system prompt, or escalate their own privileges. Off-the-shelf LLM wrappers provide no defence against these vectors.
PII Leakage in AI Responses : Even when access control is correct, AI-generated answers can inadvertently surface PII (emails, phone numbers, SSNs, API keys) that was embedded in source documents. GDPR and CCPA require this to be masked before delivery.
Lack of Compliance Audit Trail : Regulated industries require an immutable record of every AI query, every document accessed, and every security event. Most AI frameworks offer no built-in audit logging, which makes DevSecOps compliance automation essential for audit-ready AI systems.
Developer Friction with Security Controls : Security layers that slow down development are routinely bypassed. The system needed to be transparent to developers while remaining robust against adversarial inputs.
How Did We Help?
We approached the development with a phased, security-first strategy to ensure each challenge was addressed systematically. This approach followed a practical DevOps implementation roadmap where security, automation, testing, and deployment were planned as connected delivery layers instead of isolated tasks.
Identity-Based Access
Secure RAG Retrieva
Prompt Injection Defense
PII Detection & Masking
Audit Logging System
Containerized Deployment
CI/CD Security Scanning
The outcome
The project emerged as a model for secure AI deployment, replacing a ‘build-first, secure-later’ culture with a ‘security-by-design’ ecosystem.
- Zero Unauthorised Data Leakage Role-based retrieval ensures that confidential and secret documents are mathematically inaccessible to lower-privilege users, regardless of query phrasing.
- 100% Prompt Injection Block Rate All six malicious query categories (jailbreak, role manipulation, system-prompt extraction, data dump, privilege escalation, instruction override) are detected and blocked before reaching the LLM context.
- Automatic PII Masking Seven PII types are detected and masked in real time, ensuring AI responses are GDPR/CCPA-compliant at the API boundary without developer intervention.
- Full Compliance Audit Trail Every query, permission denial, PII event, and injection attempt is logged with user attribution and severity, providing a ready-made audit trail for SOC 2 and ISO 27001 reviews.
- Zero-Friction Developer Experience Security controls are invisible to legitimate users. The Docker Compose setup means a new developer can run the full secure stack in under five minutes with a single command.
- CI/CD Security Gate Automated scanning with Bandit, Safety, and Trivy prevents vulnerable code or dependencies from reaching production, reducing Mean Time to Remediation from days to seconds.
Natalie Cooper
"The Secure RAG System transformed how we think about AI in the enterprise. We went from worrying about data leakage every time someone typed a query, to having a self-defending pipeline that enforces access control, masks sensitive data, and logs everything — automatically. It is the most pragmatic approach to AI security we have ever seen."