Microsoft Copilot and Google Gemini collect more data than most IT teams realise. This article explains what is logged, whether prompts feed AI training, and how sovereign private AI changes the equation.
On-premise AI keeps models and data entirely inside your own infrastructure. This guide covers definitions, hardware requirements, and when a managed private cluster beats self-hosting.
Open-source AI models like Mistral and Llama now rival proprietary tools in quality. This guide explains what they are, how they perform, and how to deploy them privately.
The EU AI Act imposes concrete obligations on organisations using AI-powered workspace tools. This guide explains what triggers compliance duties and how sovereign AI reduces that burden.
Private AI keeps your data inside your own infrastructure; public AI sends it to third-party servers. Here is what that difference means for compliance, cost, and control.
ChatGPT sends your prompts to US servers. This article explains why regulated businesses need a private AI alternative, how sovereign deployments work, and which models qualify.
The G7 AI SBOM minimum elements and CRA Article 13 create overlapping obligations for sovereign on-premises AI deployers. This article explains how to satisfy both frameworks with a single artefact.
AI Act Article 43 requires third-party conformity assessment for high-risk AI systems. This article explains how to structure that process so training data, model weights and audit logs never leave sovereign infrastructure.
AI Act Article 50 imposes enforceable transparency and disclosure obligations on all deployers of AI systems from 2 August 2026. This article explains what sovereign on-premises operators must do technically and legally.
The GPAI Code of Practice creates distinct obligations for on-premises deployers of open-weight models. This guide shows compliance officers and CISOs how to build an audit-ready evaluation programme.
EuroHPC AI Factories offer regulated-sector organisations GPU compute under European jurisdiction, with GDPR-compliant processor agreements and no exposure to US extraterritorial laws.
Data poisoning is the silent threat to sovereign AI. Learn how regulated-sector organisations can protect fine-tuning and RAG pipelines running Mistral or Llama without exposing data to foreign cloud MLOps.
Privately hosted open-weight LLMs require structured adversarial testing that never exposes prompts or weights to external services. This article maps the methodologies, tooling, and governance controls required.
OpenEuroLLM offers EU-origin open-weight foundation models trained on EuroHPC compute. This article explains deployment, AI Act compliance, licence terms and multilingual trade-offs for regulated organisations.
The GPAI provisions of the EU AI Act entered application on 2 August 2025. This article explains when self-hosting an open-weight model makes you a provider, and how to document compliance before...
The EU AI Act imposes concrete logging, human oversight and retention obligations on high-risk AI systems. This article explains what sovereign infrastructure operators must build before the August 2026 enforcement date.
Federated learning and other sovereign privacy-enhancing technologies let hospitals, banks and public bodies train shared AI models while keeping raw data inside their legal perimeter. This article explains the governance, audit and...
Running LLM inference on US-controlled cloud GPU infrastructure exposes regulated organisations to CLOUD Act and FISA 702 risks. This guide covers on-premises GPU options, model sizing, inference frameworks, and compliance obligations under...
Training AI models on personal data triggers strict GDPR obligations. This article maps the lawful basis requirements, EDPB guidance, DPA enforcement signals and the technical measures that create a demonstrably compliant posture.
Agentic AI systems that act autonomously introduce exfiltration and jurisdictional risks that conventional LLM inference does not. This guide covers containment, AI Act obligations and audit-ready governance for sovereign on-premises deployments.
European open-weight models from EuroHPC and AI Factories offer a jurisdictionally clean alternative to US API services. This guide covers compliance, hardware baselines and supply-chain integrity for regulated sectors.
Fine-tuned model weights and proprietary training datasets represent irreplaceable competitive and legal assets. This article explains how to secure them on sovereign on-premises infrastructure against exfiltration, insider threats, and compliance failure.
The EU AI Act classifies many public-sector and regulated-sector AI systems as high-risk. This guide explains what that means for sovereign, on-premises deployments and how compliance can be made audit-ready.
Public AI services expose sensitive queries to foreign jurisdiction and training pipelines. This guide explains how regulated organisations deploy Mistral and Llama 3 on private infrastructure with full data sovereignty.
© 2026 • All rights reserved | Qsentinel AG | Obere Bahnhofstrasse 48, 9500 Wil, Switzerland
