Sovereign AI factories run national models, government copilots and autonomous agents on GPU-dense infrastructure. Cygeniq adds the seventh security layer, a Runtime AI Trust control plane that governs, secures, validates and assures every AI workload, deployed in-jurisdiction and evidenced continuously.
A sovereign AI data center is architected around model-centric compute, not application-centric compute. Every layer is optimized for training, fine-tuning and inference at scale, and the risk landscape is AI-native: behavioral, probabilistic and continuously changing across prompts, models, data and agents.
Firewalls see packets, not prompts. SIEM correlates logs, not model drift. DLP matches patterns, not embeddings. EDR watches endpoints, not agent decision chains. IAM controls credentials, not model autonomy. None of the controls that protect a conventional facility can interpret a prompt, detect a poisoned retrieval corpus or constrain an autonomous agent, and that gap is precisely what nation-state adversaries now target.
For a sovereign facility the stakes are higher still. National models, ministerial copilots and defense workloads cannot legally or strategically run on foreign hyperscaler infrastructure, so the trust layer must be sovereign too: deployed in-jurisdiction, tenant-aware, and able to prove its assurance to regulators, accreditation bodies and allies.
Prompt injection, jailbreaks, adversarial manipulation at the prompt layer.
Drift, hallucinations, unintended outputs, poisoning during training or fine-tuning.
Leakage of sensitive data, contaminated embeddings, unauthorized RAG access.
Misaligned agents, cascading multi-agent failures, no deterministic control.
Cross-tenant leakage, inference-level attacks, weak multi-tenant isolation.
"The national-security question is no longer whether our AI is secure. It is whether we can continuously establish who and what our AI trusts, what is influencing its decisions, what it can execute, and whether an adversary is manipulating it at runtime."
Defense-in-depth for an AI data center spans seven layers. Physical, network, compute and GPU, data and sovereignty, cyber operations, and application security are Layers 1–6, delivered by the facility builder and operator. Layer 7 is the AI Trust layer: the single control plane that governs, secures, validates and assures every AI workload running on the infrastructure. Cygeniq is Layer 7.
The platform is three tightly integrated modules operating as one continuous control loop, and it is delivered with embedded human-in-the-loop expertise so that every feature is configured, tuned, operated, approved and attested by named specialists.
Runtime AI risk and security. Finds and blocks attacks against models in production: AI asset inventory and shadow-AI discovery, threat modeling, model and RAG/agentic red teaming, guardrail engineering, runtime monitoring, real-time blocking, misbehavior root-cause analysis. 9 features.
AI governance, risk and compliance. Proves to regulators, auditors, tenants and allies that controls work: governance assessment, risk register and compliance mapping, compliance assessment, continuous risk scoring with policy-as-code, tenant-scoped dashboards and audit trails. 5 features.
AI-native cyber defense and operations. Runs the facility SOC with AI in the loop: threat-readiness assessment, GenAI SOC co-pilot, alert triage and noise reduction, continuous threat hunting, case management, risk dashboards, telemetry engineering. 7 features.
Policy, lifecycle and accountability for every AI asset in the facility. Outcome: AI systems operate within policy and regulatory boundaries at all times.
Protect AI at runtime against adversarial inputs, data leakage and misuse. Outcome: AI interactions and outputs remain secure in real time.
Continuously stress-test models, pipelines and agents before and during production. Outcome: vulnerabilities identified proactively, before impact.
Deliver continuous evidence of compliance and sovereign control. Outcome: continuous trust validation for regulators, operators, tenants and allies.
| Layer of the AI Workload | Key Risks | Cygeniq Capability |
|---|---|---|
| 1 · User / Interaction | Prompt injection, jailbreaks, audit gaps | Hexashield, prompt filtering, intent detection |
| 2 · Application / Agent | Unsafe actions, cascading failures | CyberTix, behavior monitoring, anomaly detection |
| 3 · AI Model | Drift, hallucinations, model poisoning | GRCortex, model governance, risk classification |
| 4 · Data / RAG | Data leakage, unauthorized access, contaminated embeddings | Hexashield, data filtering, leakage prevention |
| 5 · Orchestration / Inference | API abuse, adversarial inputs | Hexashield, inference monitoring, risk scoring |
| 6 · Compute (GPU) | Unauthorized workloads, misuse | CyberTix, workload monitoring, anomaly detection |
| 7 · Network / Infrastructure | Data exfiltration, segmentation gaps | CyberTix, network anomaly detection |
View this page on a larger screen to see the full seven-layer coverage table.
Expand each plane for detail.
Cygeniq adds classified model governance on accredited infrastructure, defense AI red teaming with cleared operators and a classified adversarial library, responsible-AI principles enforcement (lawfulness, responsibility, explainability, traceability, governability, reliability), cleared human-in-the-loop oversight with chain-of-command integration, and classified SOC integration operating per classification domain with no telemetry crossing a cross-domain boundary.
Facility-level defense services, emanation control, cross-domain solutions and data diodes, GPU hardware root of trust and VRAM crypto-wipe, FIPS 140-3 Level 3 key management with post-quantum migration, and a cleared defense SOC, are designed alongside our infrastructure partners.
The control plane is deployed on-premise or private-hosted inside the facility, in-jurisdiction, with no cross-border data movement. It is tenant-aware from the ground up: every tenant has its own policies, risk register, red-team scope, compliance evidence and audit trail.
Where a facility must meet classified or alliance-grade requirements, the same platform is augmented, not replaced, with classified model governance, cleared human-in-the-loop operators, defense red teaming and classified SOC integration.
Model classification, risk scoring, policy definition, adversarial testing and red teaming. Trust is designed into AI systems from inception.
Policy enforcement, compliance validation, certification readiness, deployment controls. Trust is verified before production exposure.
Real-time monitoring, runtime protection, continuous compliance enforcement. Trust is maintained during live operations.
Threat detection, bounded autonomous response, incident triage with human approval gates. Trust is actively protected against runtime AI risks.
Immutable audit trails, dynamic reporting, continuous compliance evidence. Trust is continuously proven, not periodic.
AI asset discovery, shadow-AI detection, AI-BOM.
Risk assessment, regulatory mapping, threat modeling by mission impact.
Adversarial red teaming across LLM, RAG and agentic systems.
Prompt and context monitoring, policy enforcement, tool-call control.
Source provenance, retrieval validation, poisoning and memory-integrity checks.
AI identity, least privilege, action approval, transaction limits, kill switches.
Immutable logging, decision traceability, oversight support.
Machine-assisted detection, bounded containment, cross-team coordination.
A red-team finding in Hexashield automatically updates the risk register in GRCortex and generates a detection in CyberTix. Discovery, proof and defense are one workflow, not three procurements, three data models and no shared evidence. The hand-offs between separate tools are where evidence and time are lost, and where audit failures and breaches actually happen.
| Continuous (24×7) | Daily | Weekly | Monthly / Quarterly |
|---|---|---|---|
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Platform alone is not sufficient for a sovereign facility. Every Cygeniq feature is paired with embedded experts who configure, tune, operate, approve and attest, aligned to EU AI Act Article 14 meaningful human oversight, and delivered through a 24×7 AI Trust Operating Center (AITOC) that plugs into the facility's own SOC rather than creating a separate silo.
Continuous adversarial validation of models, RAG pipelines and agents; guardrail engineering; runtime protection tuning; model and data integrity boards; AI supply-chain review.
Explore ARTaaS AIGRCaaSGovernance assessment, policy-as-code, regulatory mapping, continuous control monitoring, per-tenant attestations, regulator and accreditation liaison, certification support.
Explore AIGRCaaS AINDaaS24×7 runtime threat response desk, human approval of containment, agentic control and kill-switch operations, AI-native threat hunting, incident evidence and disclosure support.
Explore AINDaaSKick-off with steering committee and named leads; operator and tenant discovery; AI asset inventory; policy baseline; platform stand-up alongside the facility SIEM/SOAR.
Platform live on pilot workloads; policy-as-code active; initial vulnerability detection and red teaming; regulatory gap assessment.
Full rollout across all AI assets; 24×7 AI SOC co-pilot and runtime threat response live; regulatory mapping complete; first compliance evidence cycle.
Tuning on real data; certification evidence mature; continuous red teaming at full cadence; tenant attestations; quarterly executive reviews, steady state.
Continuous monitoring, scheduled validation and routine compliance cycles with standard service levels.
Triggered by defined conditions (large-scale training runs, multi-tenant onboarding bursts, elevated national or alliance threat level, pending audit, active incident): 24×7 adversarial validation, dedicated sovereign GPU pools, accelerated response with auto-containment for known patterns, surge analyst staffing. Human oversight is fast-tracked, never removed.
Four-part structure: fixed-fee mobilization and design; milestone-based implementation; recurring annual managed assurance (platform subscription per AI asset plus human-in-the-loop operations); optional specialist retainers for surge staffing, additional red-team campaigns and accreditation support. Deployment in-jurisdiction on the facility's own infrastructure.
Commercial detail, including per-asset rates, is provided in partner and customer proposals only.
| Article | What the Act Requires | How Cygeniq Meets It | Module |
|---|---|---|---|
| Art. 9 | Continuous, iterative risk management across the lifecycle | Dynamic AI risk register, threat modeling and re-assessment on every change | GRCortex |
| Art. 10 | Data governance, relevance, representativeness, integrity | Source provenance, retrieval validation, RAG and memory integrity checks | Hexashield |
| Art. 12 | Automatic recording of events over the system lifetime | Immutable runtime logging of what the AI received, retrieved, decided and executed | CyberTix |
| Art. 14 | Effective human oversight, including the ability to intervene or stop | Action approval on high-impact steps, transaction boundaries, agent kill switches, HITL services | CyberTix + HITL |
| Art. 15(5) | Resilience to data poisoning, model poisoning, adversarial examples and confidentiality attacks | Adversarial red teaming pre-production plus runtime prompt, context and tool-call defenses | Hexashield + CyberTix |
| Art. 26 | Deployer duties, use per instructions, monitor operation, retain logs | Policy enforcement in the runtime and six-month-plus retained decision evidence | CyberTix + GRCortex |
| Art. 50 | Disclose AI interaction; mark generated or manipulated content | Control enforcement and audit evidence that disclosure and marking are actually applied | GRCortex |
| Art. 72–73 | Post-market monitoring and serious-incident reporting within deadline | Continuous behavioral monitoring with an investigation-ready incident evidence pack | CyberTix + GRCortex |
View this page on a larger screen to see the full EU AI Act obligations table.
| Obligation | Deadline | Cygeniq Support |
|---|---|---|
| NIS2 Early Warning / Full Notification | 24 h / 72 h | Evidence pack, classification and reporting workflow |
| GDPR Personal-Data Breach | 72 h | Data-flow and impact evidence from runtime logs |
| EU AI Act Serious Incident | 15 days | Investigation-ready incident evidence pack |
| Alliance / National CERT Notification | Immediate | Classification-aware reporting channel |
Nationally controlled AI infrastructure with no hyperscaler dependency and a trust layer that stays in-jurisdiction.
Data-centric labeling and per-tenant attestation enable secure AI workload sharing across partners and alliances.
Continuous compliance with the EU AI Act, NIS2 and GDPR, with evidence available immediately rather than assembled before an audit.
Regulated tenants onboarded in weeks, premium positioning versus generic colocation, and a facility that matures from initial deployment to optimized operations over a defined six-year path.
We define the layer above AI security and above GRC.
Hexashield AI, GRCortex AI and CyberTix AI share one data model and one evidence trail.
Embedded experts and a 24×7 AI Trust Operating Center behind every feature.
Deployed in-jurisdiction, tenant-aware, with classified augmentation available.
Start with a 90-minute architecture session: where the AI Trust layer sits in your facility design, how it integrates with your SIEM, SOAR and identity stack, and what a 180-day path from unknown exposure to evidenced control looks like for your first workloads.
AI Trust Infrastructure for secure, governed and accountable enterprise AI
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