Govern AI at the Hardware Level
SecureMind Technologies provides tamper-evident, hardware-rooted monitoring for AI training and inference — so trust is built on cryptographic proof, not promises.
The Problem
Software-Only AI Safety Is Not Enough
As AI capabilities accelerate, the gap between what models can do and what we can verify grows wider. Software guardrails can be bypassed, logs can be falsified, and safety evaluations can be detached from production deployments.
Bypassable Software Controls
Application-level safety mechanisms can be circumvented by anyone with sufficient access to the model or its infrastructure. Code can be modified, guardrails removed.
Undetectable Training Runs
Without hardware-level monitoring, it is impossible to know whether an organization is secretly training a model that exceeds agreed-upon compute limits or safety thresholds.
Evaluation-Deployment Mismatch
The model that passes safety evaluations may not be the same model deployed in production. Without cryptographic verification, there is no way to know.
AI Lifecycle Monitoring Gaps
Core Products
Hardware-Rooted Governance Infrastructure
Four integrated product areas that provide comprehensive oversight of AI systems from training through deployment.
Training Oversight Engine
Continuous monitoring of AI training clusters. Detects unauthorized training runs by analyzing chip-to-chip communication patterns and compute consumption.
- Interconnect traffic analysis
- FLOP budget enforcement
- Anomaly detection for covert training
- Real-time alerting and logging
Inference Verification Suite
Ensures deployed AI models match their evaluated versions through cryptographic model fingerprinting and runtime attestation.
- Cryptographic model fingerprinting
- Runtime attestation
- Tamper-evident audit trails
- Regulatory compliance documentation
Compliance & Attestation Platform
Aggregates hardware-level attestation data into compliance reports aligned with the EU AI Act, U.S. Chip Security Act, and NIST AI RMF.
- Automated compliance reporting
- Multi-framework alignment
- Dashboard and API access
- Audit-ready documentation
Location & Asset Verification
Cryptographic verification that AI chips are operating in their authorized geographic locations, supporting export control enforcement.
- Geolocation attestation
- Export control compliance
- International treaty support
- Asset tracking and provenance
How It Works
From Silicon to Compliance Dashboard
A non-invasive, privacy-preserving architecture that creates a hardware root of trust without exposing proprietary data.
AI Chip + GPU Accelerator
Modern AI accelerators from NVIDIA, AMD, and others ship with Trusted Execution Environments (TEEs) and confidential computing capabilities. SecureMind integrates directly with these hardware security primitives.
TEE + Monitoring Layer
Our tamper-evident monitoring layer operates alongside existing TEE capabilities (NVIDIA Confidential Computing, AMD SEV-SNP). It captures workload metadata, compute utilization, and communication patterns without accessing the actual computation.
Cryptographic Attestation
Hardware-generated attestation reports provide cryptographic proof of what workloads ran, on what hardware, in what location, and within what compute budget. These reports are tamper-evident and independently verifiable.
Compliance Dashboard
Attestation data flows into the SecureMind platform, where it is aggregated into compliance reports aligned with the EU AI Act, U.S. Chip Security Act, and NIST AI RMF. Operators and regulators get real-time visibility.
Privacy-Preserving by Design
SecureMind verifies compliance without ever accessing model weights, training data, or proprietary algorithms. All attestation is cryptographic — we prove what happened, not what was computed.
Use Cases
Built for Every Stakeholder in AI Governance
From frontier labs to regulators, SecureMind provides the verification infrastructure each stakeholder needs.
AI Laboratories
Frontier model developers need to demonstrate responsible training practices to regulators and the public. They require hardware-level proof that safety evaluations match production deployments.
Scenario
A frontier lab needs to prove to regulators that their safety evaluations were run on the actual production model — and that no unauthorized training run exceeded the agreed compute budget.
Key Capabilities
- Training run verification and FLOP accounting
- Model identity attestation across evaluation and deployment
- Tamper-evident safety evaluation logs
- Automated compliance reporting to oversight bodies
Regulatory Landscape
The Compliance Enabler Across Frameworks
AI regulation is accelerating globally. SecureMind provides the hardware-level verification infrastructure that makes compliance provable, not just claimed.
EU AI Act
Hardware-level attestation provides the transparency and accountability mechanisms required for high-risk AI systems under the EU AI Act.
U.S. Chip Security Act
Cryptographic verification of chip usage and location supports export control enforcement and authorized-use compliance.
NIST AI RMF
Continuous hardware monitoring aligns with NIST AI Risk Management Framework requirements for governance, mapping, measuring, and managing AI risk.
International Treaties
Independent, tamper-evident verification enables enforcement of international AI governance agreements and compute-cap treaties.
Want to learn how SecureMind maps to your regulatory obligations?
Read our compliance researchResearch & Insights
Technical Findings & Policy Analysis
Advancing the field of hardware-level AI governance through research, policy analysis, and industry commentary.
Verification Methods for International AI Agreements
A comprehensive suite of ten verification methods for international AI safety agreements, from satellite-based detection of data center power draws to hardware-dependent embedded sensors.
EU AI Act: Technical Documentation & Hardware Attestation
Annex IV of the EU AI Act requires providers to describe hardware configurations, firmware versions, and maintain up-to-date attestation records for competent authorities.
Governing Through the Cloud: Compute Providers as AI Gatekeepers
How cloud platforms and specialized hardware vendors can act as intermediaries enforcing export-control rules, usage-limits, and audit trails for AI workloads.
Laminator: Verifiable ML Property Cards via Hardware Attestation
Proposes ML property cards signed by hardware-assisted attestation engines to prove model provenance, integrity, and compliance with safety policies.
Computing Power and the Governance of AI
Examines how controlling compute is a tractable lever for AI safety since the most capable systems are tightly linked to the amount of FLOPs they consume.
Accelerating AI Data Center Security
Addressing the convergence of physical-asset controls and policy enforcement for AI-focused data center infrastructure as capital spending surges.