Privacy Risk Profiling in Claude Code–Based LLM Access Architectures – A Systematic Analysis

The proliferation of Large Language Model (LLM) coding assistants has created a fragmented ecosystem of access architectures, ranging from fully local deployments to cloud-based commercial solutions. Organizations face increasing regulatory pressure under GDPR Article 25 (privacy by design) and the NIS2 Directive to justify their data processing choices. This study provides the first systematic privacy risk profile of 20 LLM access configurations documented in a 21-entry ontology, comparing self-hosted solutions against standard commercial offerings. We employed STRIDE threat modeling per configuration, network traffic analysis in controlled environments (Wireshark/tcpdump), and CVSS-adapted quantitative scoring (0–10 Privacy Risk Score). Regulatory compliance was mapped to GDPR Art. 25, NIS2 Directive, and EU data residency requirements. Pure local solutions achieved a mean Privacy Risk Score of 0.8/10 (range 0–2), outperforming all commercial baselines (mean 7.3/10, range 6.5–8.3). Hybrid routing architectures, which route sensitive projects locally and other traffic to the cloud, scored 3.2/10 in mixed use, offering optimal trade-offs. Windows 7–compatible solutions (S1.5, S2.8, S2.9) introduce NIS2 non-compliance risks due to absent security updates; of these, only S1.5 also offers pure local privacy. Self-hosted LLM architectures significantly reduce privacy risk compared to commercial alternatives. Organizations handling sensitive code should prioritize pure local deployments. The ontology-driven methodology is reproducible and extensible.