Building an Application Security Assistant with GenAI

In today's rapidly evolving security landscape, developers need accessible, accurate, and timely guidance on secure programming practices. Traditional documentation, while comprehensive, often falls short in providing just-in-time assistance during the development process. By combining Retrieval‑Augmented Generation (RAG), LangChain, and FAISS, you can stand up an interactive chatbot that lets developers ask natural‑language questions—like “How do I prevent XSS?”—and receive precise, policy‑driven answers grounded in your secure coding guides and company policies.

The Challenge: Security Knowledge at Scale

Security policies and best practices are typically scattered across numerous documents, standards, and guidelines. Developers frequently face questions like:

• "How do I properly validate user input to prevent XSS?"

• "What's our company policy on authentication requirements?"

• "How long do we have to remediate low-severity findings?"

Without immediate answers, developers either interrupt their workflow to hunt for information or, worse, implement potentially insecure solutions.

• https://github.com/pubal/AISec/blob/main/chatbot.py

Introducing RAG-Based Security Assistants

This proof-of-concept demonstrates how to build an interactive chatbot that leverages company-specific security documentation to provide precise, contextual guidance to developers.

What is RAG and Why It Matters for Security

Retrieval-Augmented Generation (RAG) combines the power of large language models with the reliability of retrieval from verified knowledge bases. For security applications, this approach is crucial because:

1. It grounds responses in approved company documentation

2. It reduces hallucination by referencing specific policies

3. It provides consistent, authoritative answers

4. It can be updated with new security guidance without retraining

The Technology Stack: LangChain and FAISS

Our solution leverages several key technologies:

LangChain

LangChain provides the orchestration layer that connects document processing, retrieval, and response generation. In our implementation:

• Document loaders (PyPDFLoader, TextLoader): process PDFs and Markdown files

• Text splitter (RecursiveCharacterTextSplitter): break documents into semantic chunks

• Embeddings (OpenAIEmbeddings) ensure security-focused responses

• Chains (RetrievalQA or the Functional API pipeline) manages the information flow

FAISS Vector Database

Facebook AI Similarity Search (FAISS) powers our vector database, enabling:

• Efficient semantic search across thousands of document chunks

• Fast retrieval of contextually relevant information

• Persistence of embeddings for quick startup

• Scalability to handle large security knowledge bases

The Prompt Engineering Behind Effective Security Guidance

The prompt is the cornerstone of this security assistant. It shapes how the model interprets context and formulates responses.

Our Enhanced Security Expert Prompt

You are an OWASP-savvy application security expert and trusted advisor to developers within Company Inc. Your purpose is to help developers write secure code by providing precise, actionable guidance based on company security policies, OWASP best practices, and secure coding standards.

CONTEXT:

{context}

QUESTION:

{question}

INSTRUCTIONS:

1. Answer directly and concisely, prioritizing practical solutions over theory.

2. Include code examples where appropriate to demonstrate secure implementation.

3. Reference specific company policies or standards when they apply.

4. Provide clear steps for remediation if addressing a vulnerability.

5. Always mention security implications and potential risks.

6. If multiple approaches exist, recommend the most secure option first.

7. When referring to documentation, include the specific section names.

8. For high-risk issues (authentication, authorization, encryption), emphasize careful implementation.

If the answer isn't in the context, say: "I don't have specific guidance on this topic in our company documentation. Please contact the Application Security team at AppSec@company.com for assistance."

ANSWER:

Prompt Engineering Techniques Employed

Building Your Own Security Assistant: A Step-by-Step Guide

If you want to replicate this proof-of-concept, here's what the code is doing:

1. Document Processing

• Loads PDFs and Markdown files from a specified directory

• Splits documents into chunks of 1000 characters with 200-character overlap

2. Vector Store Creation

• Creates embeddings for each document chunk using OpenAI's embedding model

• Stores these embeddings in a FAISS vector database

3. RAG Pipeline Construction

• Initializes a retriever to fetch the 4 most relevant chunks for each query

• Creates a custom prompt template that frames the context and instructions

• Constructs a functional chain that performs retrieval, prompting, and generation

4. Interactive Interface

• Provides a simple command-line interface for querying

• Presents answers in a consistent format

Enterprise Integration: Beyond the Proof of Concept

This proof-of-concept can evolve into a powerful enterprise solution through various integration paths:

Microsoft Teams Integration

Implementing the security assistant as a Teams bot can:

• Allow developers to ask questions within their workflow

• Support group chats where security guidance benefits the whole team

• Integrate with development channels for visibility

• Provide both public responses and private consultations

Slack Implementation

As a Slack app, the security assistant can:

• Support slash commands for quick security checks

• Create dedicated security channels where the bot is always available

• Allow threaded conversations for complex security questions

• Integrate with CI/CD notifications for contextual security advice

IDE Extensions

Taking the assistant directly to where code is written:

• VS Code or JetBrains plugins that provide real-time security guidance

• In-line suggestions for security improvements

• Pop-up advice when risky coding patterns are detected

• Quick access to relevant company security documentation

Expanded Knowledge Base

The system can be enhanced with:

• Integration with vulnerability management systems

• Access to SAST/DAST scan results and recommendations

• Real-time updates from security advisories and CVE databases

• Training on secure code examples specific to your technology stack

Conclusion

By combining the power of GenAI with company-specific security knowledge, organizations can create an assistant that democratizes security expertise. This approach empowers developers to write secure code without constantly consulting the security team, ultimately leading to more secure applications and reduced security debt.

The most powerful aspect of this solution is its ability to provide contextually relevant, company-approved security guidance exactly when and where developers need it. As security requirements evolve, simply update the knowledge base, and the assistant immediately provides the latest guidance—no retraining required.

For security teams looking to scale their impact across large development organizations, a RAG-based security assistant represents a practical, immediate step toward embedding security expertise into the daily development workflow.