Idea
Access control platform for enterprise AI ensuring secure multi-user model fine-tuning and inference with sensitive data protection
Research Paper
Core Innovation
This paper identifies critical security risks in fine-tuning and RAG pipelines due to lack of access control. It introduces a deterministic framework enforcing participant-aware access control for all training, retrieval, and generation content. This approach shifts AI security from probabilistic defenses to rigorous authorization, uniquely addressing multi-user enterprise AI workflows.
Market Size (TAM)
$20–50B TAM for enterprise AI security and model management; $2–10B SAM from large enterprises and cloud service providers. Driven by increasing AI adoption and regulatory compliance needs.
Potential Customers & Pain Points
- Enterprises handling sensitive data needing secure AI model fine-tuning
- AI developers lacking robust access control in multi-user LLM systems
- Organizations deploying retrieval-augmented generation pipelines vulnerable to data leaks
Business Model
Subscription-based platform licensing to enterprises and cloud providers with tiered pricing based on data volume and user count
Competitive Landscape
- OpenAI Enterprise
- Anthropic
- Cohere
Implementation Challenges
- Integration complexity with existing AI workflows
- Balancing security with model performance
- Adoption resistance due to operational overhead
Validation Strategy
- Pilot deployment with Microsoft Copilot Tuning users
- Security audits demonstrating prevention of data exfiltration
- Customer feedback on usability and integration
Research Paper Overview
Enterprise AI Must Enforce Participant-Aware Access Control
Summary
Large language models in enterprises risk leaking sensitive data during fine-tuning and retrieval-augmented generation. Existing defenses are probabilistic and insufficient. This paper proposes deterministic, fine-grained access control ensuring all training, retrieval, and generation content is authorized for all users involved. The approach is deployed in Microsoft Copilot Tuning for secure enterprise model fine-tuning.