From Stochastic to Deterministic: Grounding Regulatory AI with Knowledge Graphs and GraphRAG

Introduction

The fundamental tension in regulatory AI lies between the stochastic nature of large language models and the deterministic requirements of medical device compliance. While standard LLMs excel at fluid text generation, they operate on probability rather than proof. In the context of EU MDR 2017/745 or FDA submissions, a high‑probability guess is a liability. Achieving the necessary technical rigor requires a structural shift in how AI interacts with regulatory data. This is achieved through the integration of Knowledge Graphs and GraphRAG.

The Problem of Semantic Drift

Generic Large Language Models often suffer from semantic drift. When asked to perform a gap analysis or draft a Clinical Evaluation Report (CER), an LLM might conflate similar but distinct requirements from different Annexes. It lacks a native understanding of the hierarchical and interconnected nature of GSPR clauses. Without a grounded reference, the AI can produce “hallucinations” that sound authoritative but lack statutory validity.

The Knowledge Graph as the Source of Truth

A Knowledge Graph (KG) serves as the formal, versioned representation of regulatory requirements. Unlike a flat database or a vector store, a KG maps the complex dependencies between clauses, harmonized standards, and MDCG guidance.

• Nodes represent specific requirements, definitions, or evidence types.
• Edges define the relationships, such as “Annex II Section 4 requires evidence of ISO 10993 compliance.”

By encoding the MDR as a graph, we transform the regulation from a set of documents into a machine‑readable logic layer. This ensures that the system understands that a missing biocompatibility report is not just a missing document, but a failure of a specific evidence chain required for a Class IIb device.

Example trace (how one GSPR clause maps to concrete evidence):

MDR Annex I / GSPR 23 (shelf‑life & storage claims) requires evidence from: Risk Management File, Accelerated Ageing Report, and IFU Section 4. If the Accelerated Ageing Report is missing from the technical file, a generic AI might note that “some ageing data is absent.” A Knowledge Graph–grounded system directly flags: “GSPR 23 → missing Accelerated Ageing Report — evidence chain broken for a Class IIa device.”

GraphRAG: Precision Retrieval for Regulatory Drafting

GraphRAG (Graph Retrieval‑Augmented Generation) is the mechanism that bridges the gap between the structured Knowledge Graph and the generative LLM. Traditional RAG relies on vector similarity, which can be imprecise when terminology is highly specialized. GraphRAG improves this by traversing the Knowledge Graph to retrieve contextually relevant nodes before the generation step.

When the system drafts a PMS Plan, GraphRAG identifies the specific device classification, retrieves the exact Annex III requirements, and pulls the relevant harmonized standards. The LLM is then “fenced” within this retrieved context. It is no longer guessing based on its training data. It is synthesizing a response based on the retrieved graph nodes. This ensures that every assertion made by the AI has a direct line of provenance back to the source regulation, fulfilling the ALCOA+ principles of traceability and accountability.

ALCOA+ stands for: Attributable, Legible, Contemporaneous, Original, Accurate — plus Complete, Consistent, Enduring, and Available. These are the gold‑standard data‑integrity criteria used in regulated life‑science environments.

Strategic Implications for PRRCs and QA/RA Teams

The move to GraphRAG‑enabled regulatory intelligence shifts the role of the PRRC (Person Responsible for Regulatory Compliance) from a technical writer to a technical auditor.

• Deterministic Verification: Every gap identified and every sentence drafted includes a metadata tag linking it to the Knowledge Graph node. This allows for immediate human verification.
• Audit‑Ready Remediation: Instead of vague advice, the system provides clause‑linked findings. It identifies exactly which MDR requirement is unmet and suggests a remediation path based on the logic of the graph.
• Risk Mitigation: By grounding the LLM in a deterministic structure, the risk of data leakage and improper handling of missing data is minimized. The system knows what it does not know because the graph defines the boundaries of the required evidence.

This graph‑grounded approach is already live. Every finding surfaced on the RunaReg platform is clause‑linked and auditable by design.

Conclusion

For medical device manufacturers, speed is irrelevant without accuracy. The objective of using GraphRAG and Knowledge Graphs is to replace “black‑box” uncertainty with a transparent evidence chain. By prioritizing deterministic logic and verifiable traceability over raw text generation, we build a regulatory operating system that supports high‑stakes submissions with the confidence of engineerable truth.