Genuine AI sovereignty demands cryptographic proof of hardware integrity, mathematical protection of data in use, and continuously verified security of every model you deploy. Sovarin delivers the complete technology portfolio to achieve it.
Deploying AI on domestic infrastructure is a starting point, not a destination. Genuine AI sovereignty & security is a multi-layered technical and operational discipline. These are the six non-negotiable requirements every sovereign AI programme must satisfy.
Every AI model must be continuously tested for adversarial vulnerabilities, data leakage risks, and behavioural drift. An untested model is an unverified model — and an unverified model cannot be trusted with sensitive decisions.
Sensitive training data must never leave sovereign boundaries in recoverable form. Privacy-enhancing technologies allow AI to learn from and operate on protected data without exposing it — providing mathematical guarantees, not contractual ones.
The hardware running your AI must be cryptographically authenticated. Counterfeit silicon, compromised firmware, and undisclosed supply chain dependencies are attack vectors that data-residency rules and cloud agreements cannot address.
87% of advanced logic chips are fabricated at a single foundry in a geopolitically contested region. Sovereign AI programmes must quantify and monitor these chokepoint dependencies before a supply shock forces the decision under duress.
The EU AI Act, ISO 42001, NIS2, NCSC CAF, and IEC 62443 demand cryptographically generated, tamper-evident audit artefacts that withstand regulatory scrutiny — not manually assembled spreadsheets and policy documents.
A sovereign AI programme must survive adversarial conditions — supply shocks, model compromise, regulatory investigation, and infrastructure failure. Resilience is designed in from the architecture, not bolted on after an incident.
Sovarin's portfolio integrates three capability domains into a coherent, end-to-end architecture. Each pillar addresses a different layer of the sovereignty stack. Together they deliver assurance that no single pillar can provide alone.
Systematic adversarial testing, membership inference analysis, behavioural auditing, and quantified trust scoring. Moves AI security posture from vendor attestation to independently verifiable evidence.
Federated learning, fully homomorphic encryption, synthetic data, anonymisation, and multi-party computation. Mathematical guarantees that data remains sovereign throughout the entire AI lifecycle.
TEE-protected BOM registry, PUF and HRoT silicon attestation, geopolitical risk engine, immutable audit ledger, and live sovereignty dashboard. Cryptographic proof of hardware trust down to the silicon.
Select a pillar to explore the individual tools and technologies — what each one does, the specific risk it addresses, and the regulatory obligations it supports.
Before a model is trusted with sovereign workloads, it must be tested — systematically, adversarially, and at every layer. Our five purpose-built instruments probe models for vulnerabilities, measure data leakage risk, audit reasoning integrity, and score trustworthiness against quantified thresholds. These tools move AI security from opinion to evidence.
Systematic adversarial probing engine that identifies prompt injection vulnerabilities, jailbreak attack surfaces, and boundary violations in large language models. PROBE maps the full attack surface of a model before deployment, generating a structured vulnerability register aligned to AI security frameworks.
Quantifies the risk that a model will reveal whether specific records were present in its training set. Essential for models trained on personal, clinical, or classified data — produces a statistically grounded leakage risk score that directly informs data governance decisions and GDPR Article 25 obligations.
Audits LLM behaviour for consistency, bias, and hallucination under structured test conditions. Produces a reproducible evidence pack mapping observed model behaviour to ISO 42001 and SOC 2 Trust Services Criteria — providing the artefact trail required for AI governance certification and third-party audit.
Generates a composite, quantified trust score for any LLM deployment across multiple security and reliability dimensions. Outputs a structured trust posture report enabling underwriters, procurement teams, and risk committees to make deployment decisions on the basis of measurable evidence, not vendor attestation.
Comprehensive evaluation framework measuring model performance, safety alignment, and robustness against benchmarked thresholds. Integrates with CI/CD pipelines to provide continuous assurance across model versions — ensuring that retraining or prompt modification does not introduce security regressions.
Sovereign AI cannot be built on data that leaves your control during training or inference. Our PET suite enables AI systems to learn from sensitive, distributed, and regulated datasets while providing mathematical guarantees that the underlying data is never exposed — in transit, at rest, or in use. These are not privacy controls; they are privacy proofs.
Trains AI models across distributed data sources without centralising the underlying data. Each data custodian retains full sovereignty over their dataset; only model gradient updates — never raw records — are exchanged. Directly enables cross-organisational and cross-jurisdictional AI collaboration without data sharing agreements.
Enables AI inference on fully encrypted data — the model operates on ciphertext and returns encrypted results, with only the data owner able to decrypt outputs. Delivers the highest-assurance form of data sovereignty: the compute provider cannot access the data even during active processing.
Generates statistically faithful synthetic datasets that preserve the analytical utility of real data without retaining identifiable records. Enables AI training on high-quality data where access to real datasets is restricted by regulation, sensitivity, or commercial confidentiality.
Applies k-anonymity, l-diversity, and differential privacy techniques to transform personal data into datasets satisfying regulatory anonymisation standards. Outputs a privacy risk assessment quantifying re-identification probability — the evidential basis for ICO compliance claims under UK GDPR.
Enables multiple parties to jointly compute AI model outputs or analytics without any party revealing their private inputs. Foundational for intelligence sharing, consortium analytics, and cross-agency AI collaboration where data pooling is legally or commercially prohibited.
AI sovereignty cannot be assured at the software layer alone. The GPU cluster training your model, the compute nodes serving inference, the firmware orchestrating every operation — all must be cryptographically verified as authentic, uncompromised, and free from undisclosed geopolitical dependencies. SovereignBOM delivers this assurance through mathematically verifiable hardware intelligence.
All Hardware BOM data is parsed inside hardware Trusted Execution Environments — AWS Nitro Enclaves, Azure Confidential VMs, or Intel TDX. Tenant-held keys ensure the platform never has cryptographic custody of component data. Zero plaintext memory exposure across all processing operations.
Physical Unclonable Function signatures from silicon manufacturers provide cryptographic proof of component authenticity at the hardware root of trust. Every AI accelerator can be attested as genuine — with counterfeit or unverified ASICs flagged CRITICAL before deployment into sovereign AI infrastructure.
Automatically maps hardware components to their fabrication origins, identifying chokepoint dependencies — TSMC advanced nodes, ASML EUV lithography, rare earth materials — with quantified monopoly index scores. Supply shock simulation models the operational impact of losing a jurisdiction before it happens.
An append-only, cryptographically hashed event log recording every BOM operation, component change, firmware update, and geographic movement. Ledger integrity is verified on every load. Provides the tamper-evident audit trail required by ISO SC27, NIS2, IEC 62443, and NCSC CAF — natively generated, never assembled manually.
Aggregates KPIs across all registered hardware BOMs: component totals, chokepoint counts, geopolitical exposure percentages, and HRoT verification rates. Live jurisdiction risk matrix with active sanctions and export control overlays. Operational sovereignty as a continuously monitored, evidence-backed state.
Each AI sovereignty & security requirement maps directly to capabilities within the Sovarin portfolio. This matrix shows exactly which pillar satisfies which obligation.
| Sovereignty Goal | What It Requires | AI Red Teaming | Privacy-Enhancing Tech | Hardware BOM |
|---|---|---|---|---|
| Model Integrity Assurance Verified, uncompromised AI models | Adversarial testing, vulnerability registers, behavioural audit artefacts | ✓ PROBE, LLM Integrity Auditor, Model Evaluator | ◐ MIA Analyser (leakage risk) | ◐ Firmware integrity via HRoT |
| Data Sovereignty Protected data throughout AI lifecycle | Encryption in use, no data centralisation, GDPR-compliant training pipelines | ◐ MIA Analyser (leakage quantification) | ✓ FHE, Federated Learning, Anonymisation, Synthetic Data, MPC | ✓ TEE processing, tenant-held keys |
| Hardware Trust Cryptographically authenticated compute | Silicon-level attestation, firmware verification, counterfeit detection | — | — | ✓ PUF attestation, HRoT verification, TEE processing |
| Supply Chain Sovereignty Geopolitical risk intelligence | Chokepoint mapping, jurisdictional exposure quantification, shock simulation | — | — | ✓ Geopolitical Risk Engine, Audit Ledger, Dashboard |
| Regulatory Evidence Cryptographic compliance artefacts | Tamper-evident audit trails, framework-mapped reports, verifiable artefacts | ✓ LLM Integrity Auditor (SOC 2, ISO 42001) | ✓ Anonymisation assessments, federated audit logs | ✓ Immutable ledger (ISO SC27, NIS2, IEC 62443) |
| Operational Resilience Continuity under adversarial conditions | Continuous monitoring, regression detection, incident response evidence | ✓ Model Evaluator (CI/CD, regression detection) | ◐ Distributed architecture reduces single-point exposure | ✓ Supply shock simulation, live sovereignty monitoring |
✓ Primary capability · ◐ Contributing capability · — Not applicable to this domain
Sovarin's portfolio is engineered for the sectors where AI sovereignty is not aspirational — it is a legal, national security, and operational imperative.
Sovarin is an AI security and privacy-enhancing technologies company built on the principle that intelligence must be protected at every layer of the stack. Our name encodes our purpose: Sovereign AI. Our team brings together expertise in applied cryptography, adversarial machine learning, hardware security, and regulatory compliance.
Our work is grounded in the conviction that true security is mathematical, not procedural. AI sovereignty is not a policy document or a data residency certificate — it is a set of cryptographic proofs and continuously verified states that can withstand adversarial scrutiny, regulatory examination, and operational stress.
We operate at the intersection of AI, cryptography, and national security — building tools and capabilities that enterprises, governments, and critical infrastructure operators need to deploy AI with confidence.
Every security claim we make is backed by cryptographic proof, not policy assertion.
We engineer for environments where failure has consequences — not for demonstrations.
Our outputs satisfy auditors, regulators, and risk committees — not just technical teams.
Your keys. Your data. Your infrastructure. We provide capability without custody.
Every engagement starts with a sector qualification call to scope your specific AI sovereignty & security requirements. We map your current posture, identify gaps, and recommend the appropriate combination of capabilities from the Sovarin portfolio.