Secure by Design: How to Build GenAI Products Without Leaking Your Intellectual Property

As more companies look to create GenAI products and internal tools, the focus is shifting from prototypes to production. The transition raises urgent questions about scalability and performance as well as privacy, intellectual property, and infrastructure resilience.

Engineering teams working in regulated industries or with proprietary data know that using public LLM APIs comes with risk. Codebases, customer prompts, internal processes. If these leave your boundary, you lose control. And once data leaks, there is no way to take it back.

The good news is that building secure, reliable GenAI products is absolutely achievable. In this article, we break down the architecture, infrastructure choices, and process layers needed to ship GenAI products that meet enterprise security standards from day one.

We cover:

• The risks of unmanaged AI integration;
• Design principles for secure architecture for your GenAI products;
  How to host private LLMs;
• How to structure a RAG pipeline securely;
• Data handling and context filtering strategies;
• API security and logging;
• The trade-offs between full control and managed services.
  

Whether you’re building an internal LLM-powered assistant, a customer-facing chat tool, or a backend GenAI workflow, the general approach is the same: embed security into every layer of the stack.

Common Security Pitfalls in GenAI Products

The biggest risks in GenAI systems usually come from:

External LLM APIs With Sensitive Inputs

When teams use public APIs like OpenAI or Anthropic for production workloads, they often end up sending prompts that include:

• Source code;
• SQL queries or customer data;
• Proprietary business logic;
• Internal platform documentation.

Even with API terms claiming non-retention, the risk is organizational. You are trusting a third party with data you likely cannot audit or revoke.

Lack of Context Filtering

LLMs are only as safe as the prompts they are fed. Systems that blindly inject user data or internal documents into context windows open up the potential for accidental leaks, hallucinated responses based on sensitive terms, or prompt injection attacks.

Weak Access Controls and Logging

Without strong authentication, authorization, and logging, GenAI endpoints become a new attack surface. Since models are often treated as “black boxes,” it can be harder to detect misuse or abuse without comprehensive tracing.

Secure Architecture Principles for GenAI

You should have a series of principles to serve as a foundation for designing secure GenAI systems, regardless of the specific language model you choose.

Starting with keeping sensitive context in-house. Your infrastructure should ensure that no source code, internal documents, customer metadata, or system logs are ever sent to a third-party model provider unless explicitly intended. This applies to both runtime and training data. You can use RAG to avoid model fine-tuning on private data. Instead of fine-tuning a base LLM on proprietary documents or data, use a Retrieval-Augmented Generation (RAG) architecture. This separates your data from the model weights, keeping intellectual property out of the model layer while still enabling contextual answers.

Basically, as much as possible try to treat LLM access like any other high-privilege API. Every LLM call should be subject to rate limiting, user auth, logging, and optional redaction. These systems need to be governed the same way internal APIs or admin-level functions are.

Hosting Private LLMs: Tradeoffs and Recommendations

You have three main options for deploying secure GenAI services:

1. Self-Hosted Open Source Models

Models like LLaMA 3, Mistral, Mixtral, or Code Llama can be deployed on your own infrastructure. With tools like vLLM, Text Generation Inference, or llama.cpp, you can serve inference on consumer or enterprise-grade hardware.

Advantages:

• Total control over data and behavior
• Air-gapped deployment possible

Challenges:

• Requires GPU capacity and ML ops capability
• May lag behind GPT-4 in accuracy or multilingual support
  

Good For:

• Internal developer tools
• Domain-specific copilots
  

2. Managed Private LLMs (Cloud-Vended)

Services like Azure OpenAI, AWS Bedrock, or Fireworks.ai offer gated, tenant-isolated access to powerful models without sending data into shared queues.

Advantages:

• Managed infrastructure
• Enterprise-grade SLAs and security guarantees
  

Challenges:

• Still a third party
• Prompt retention policies vary
  

Good For:

• Enterprises without GPU infrastructure
• Products that require GPT-4 quality but with better governance
  

3. BYO Embeddings + RAG on Small Models

You can combine smaller open models (7B–13B range) with custom embedding generation, vector search, and context synthesis using a lightweight architecture:

• Embeddings: text-embedding-3-small or e5-base
  
• Vector DB: Qdrant, Weaviate, or Elasticsearch
  
• Context compression: Tokenizers + summarizers
  
• Model: Mixtral 8x7B or Code Llama 34B for inference
  

This allows you to keep everything in-house and still answer questions based on internal knowledge.

Building a Secure RAG Pipeline

RAG works by retrieving relevant documents from your data store and injecting them into the model context window as part of the prompt.

This sounds simple, but each step must be carefully secured.

Step 1: Ingest and Preprocess Documents

• Strip any sensitive metadata or tokens from documents before indexing
• Break content into chunks with overlapping tokens (e.g. 512-1024 words)
  

Step 2: Index With Private Vector DBs

Use self-hosted options like Qdrant with TLS and role-based access or ChromaDB for lightweight embedded indexing. Avoid public APIs unless you verify data is encrypted in transit and not retained.

Step 3: Context Assembly With Filters

Before injecting documents into the model prompt, apply:

• Classification: Exclude sensitive documents from use;
  
• Policy rules: Mask user-specific tokens, access control roles.
  

Step 4: Prompt Construction

Construct prompts programmatically using templates with guardrails.

Example structure:

You are a technical assistant with read-only access to our API documentation.

Answer only based on the following context:

<INSERT CONTEXT HERE>

If you are unsure or the context is insufficient, say: “I cannot answer based on current information.”

This approach minimizes hallucinations and prevents the model from guessing.

Securing GenAI APIs and Endpoints

Every GenAI interaction is an API call, and it must be protected like any other high-privilege system interface.

Authentication and Authorization

• Require OAuth2 or JWT tokens for any LLM interaction;
• Apply user and role-level permissions for different endpoints.
  

Rate Limiting and Abuse Detection

• Throttle per-user and per-IP usage to avoid prompt flooding;
• Log all prompts and completions for auditing.
  

Prompt Injection and Output Hardening

Models are vulnerable to prompt manipulation unless explicitly constrained.

Defensive strategies include, on top of using sandboxing where it’s applicable:

• Explicitly disallow output that uses certain keywords (e.g. secrets, tokens);
• Use output validators for format enforcement (e.g. regex for JSON responses).
  

Auditing, Logging, and Governance

Security is not just about blocking bad access. It also requires visibility.

What to Log:

• Input prompt;
• Context documents retrieved;
• Output from the model;
• User who made the request;
• Time, latency, model version.
  

Logs must be stored securely, access-controlled, and GDPR/CCPA compliant if applicable.

Governance Models

You can treat LLM integrations as part of your existing API governance, using the same controls:

• Versioning;
• Access expiration;
• Feature flag gating;
• Feature-level usage caps.
  

Combine logs with APM tools or observability platforms to detect anomalies or misuse.

Managing Tradeoffs: Full Control vs Performance vs Cost

Not every team needs to host their own models, and not every use case justifies the overhead of complete isolation.

Here’s a decision matrix to help evaluate your security posture:

Teams often start with managed APIs and move to self-hosted LLMs as scale, privacy, and compliance needs grow.

Takeaways

Building GenAI products securely must be part of the architecture from day one, especially when working with sensitive inputs like code, logs, or proprietary data.

Private LLMs, secure RAG design, API-level controls, and audit-ready observability can all work together to make GenAI systems both powerful and safe. The trade-offs are real, but so are the opportunities. With the right structure, you can deliver fast AI experiences without compromising on privacy or platform integrity.

If you’re building an AI assistant, internal dev tool, or embedded LLM product, we can help you do it securely, from infrastructure to implementation, so get in touch.