Security Architecture & Zero-Trust Verification
How Membdel delivers provable model unlearning without compromising proprietary weights, network perimeters, or enterprise confidential data.
100% Air-Gapped
Operates inside customer VPCs or isolated on-premises clusters. Zero telemetry exfiltration or external cloud dependency.
Auditable Proofs
Every unlearning run produces cryptographic and statistical audit netlists (Yosys gate synthesis, shadow MIA, KS distribution tests).
Zero Retraining Waste
Saves millions in GPU cluster retraining while preserving >98.6% of base model reasoning and logic utility.
1. Zero-Trust Model Handling
Traditional AI services require uploading model weights or proprietary data to third-party APIs. For semiconductor firms, financial institutions, and defense enterprises, this creates unacceptable third-party supply chain risk. Membdel was engineered from the ground up to solve this:
- Containerized Local Deployment: Membdel unlearning engines are delivered as hardened OCI container images deployable into your own Kubernetes/Slurm clusters on AWS, GCP, Azure, or on-premises NVIDIA DGX infrastructure.
- No Phone-Home Telemetry: Verification audits and netlist generation run locally. Weights never leave your physical or logical perimeter.
- Memory-Resident Transformation: Weight parameter subspace projection is performed in GPU VRAM with atomic checkpoint commits, ensuring no orphaned temporary data remains.
2. Empirical EDA Synthesis Audits (Silicon IP)
For hardware teams protecting Verilog and PDK IP, theoretical metrics are insufficient. Membdel integrates directly with open-source (Yosys) and commercial logic synthesis toolchains. An unlearned model is prompted with adversarial prefix probes to regenerate circuit logic; the output is piped through real gate synthesis. The audit passes only when the synthesis netlist returns 0 cells and 0 wires, proving the circuit cannot be manufactured from extracted model completions.
3. Statistical Deletion Audits (GDPR Art. 17)
To satisfy stringent regulatory demands under GDPR and the EU AI Act, Membdel employs rigorous two-sample Kolmogorov-Smirnov (KS) tests and shadow Membership Inference Attacks (MIA). The audit mathematically certifies that the model's output distribution on forgotten records is statistically indistinguishable from a model that was never trained on the target data.
4. Responsible Vulnerability Disclosure Program
We take the security of our infrastructure and services seriously. If you believe you have discovered a vulnerability in our website or public services, we encourage you to disclose it responsibly:
- Contact: Email details to ankit@membdel.com with the subject line
[Security Vulnerability Report]. - Scope: Public endpoints under
*.membdel.com. - Guidelines: Do not access or modify customer data, do not execute denial-of-service attacks, and provide reasonable time for remediation prior to public disclosure.
- Safe Harbor: If you act in good faith and adhere to these guidelines, we will not pursue legal action against you.