Can AI Write Proposals Under CMMC 2.0?

The short answer: yes, but only with the right architecture. The longer answer involves understanding why most AI proposal tools will fail your CMMC assessment.

This article expands on concepts from our Compliant AI Proposal Automation guide. Read that first for the complete framework.

The Problem With Cloud AI for Proposals

Defense contractors are adopting AI tools to accelerate proposal development. The productivity gains are real: AI can draft sections, suggest compliance language, and synthesize past performance data. But the compliance implications are often ignored until assessment time.

When you paste RFP requirements into ChatGPT or use a SaaS proposal tool, you're sending potentially controlled information to systems you don't control. The RFP itself may contain CUI. Your past performance data certainly does. Technical approaches often reference controlled specifications.

Under CMMC 2.0, this creates two compliance failures:

1. Unauthorized disclosure - CUI leaves your controlled environment and enters a multi-tenant cloud system
2. Insufficient audit trails - You can't demonstrate what CUI was processed, by whom, or what the AI did with it

Cross-Tenant Data Risks in AI Systems

Multi-tenant AI systems process your data alongside every other customer's data. Even with logical tenant separation, risks exist at multiple layers:

• Shared GPU memory: During inference, your prompts may co-exist in GPU memory with other tenants' data
• Embedding co-location: Vector databases often store embeddings from multiple tenants in shared indexes
• Model training: Many AI vendors reserve the right to train on user data, potentially leaking your information into model weights
• Caching layers: Response caching for performance can expose previous queries to subsequent users

What Compliant AI Architecture Looks Like

Compliant AI for proposal automation requires isolation at every layer:

• Private RAG infrastructure: Your past performance, technical approaches, and proposal content must be stored in isolated vector databases that only your organization can access
• Dedicated inference: AI processing must occur on infrastructure where your data never co-mingles with other tenants
• Complete audit trails: Every prompt, every retrieval, every generated response must be logged with user attribution and timestamps
• No training on your data: Contractual and technical guarantees that your CUI is never used to improve shared models

RAG Isolation: The Technical Requirement

Retrieval-Augmented Generation (RAG) is what makes AI useful for proposals. It lets you ground AI responses in your actual past performance, win themes, and technical capabilities. But RAG creates the biggest compliance exposure.

In a multi-tenant RAG system, your embeddings—mathematical representations of your proposal content—are stored alongside other customers' embeddings. Retrieval queries search across this shared space. Even if results are filtered by tenant, the search process itself creates exposure.

Compliant RAG requires physical or cryptographic isolation of your vector stores. This is the same principle that applies to CUI handling in CRM systems—controlled information must stay within controlled boundaries.

Audit Requirements for AI-Generated Content

CMMC assessors will ask: 'Show me every time AI accessed CUI in your proposal process.' Your system must be able to answer:

• Which user initiated the AI query?
• What CUI-containing documents were retrieved?
• What prompt was sent to the model?
• What response was generated?
• Was the response used in the final proposal?

If you can't answer these questions with documented evidence, your AI usage is a compliance gap.