By Salinthip Keereerat
2nd year Digital Engineering student at PSU Phuket 🇹🇭
Impatient of AI, Cybersecurity and UX/UI
Artificial Intelligence (AI) is no longer just hype—it’s now making decisions that can significantly impact our lives. From medical diagnoses to determining your creditworthiness, AI is everywhere.
But while many ask, “Does this AI model work?”, the real question should be:“How do we know the model was built correctly in the first place?”
🌐 Belief Isn’t Enough Anymore
Today, we rely on documents like model cards and training datasheets to tell us what data was used and what a model is intended for. While these tools promote transparency, they’re still based on trust—and when AI regulations (like the EU AI Act) become stricter, that’s just not enough.
What if instead of hoping, we could actually verify those claims?
🔒 The Real Challenge: Trust Isn’t a Strategy
As global AI regulations take shape, especially across Europe and the West, trust-based claims won’t cut it anymore. We’ll need iron-clad, verifiable evidence to prove that an AI model does what it claims—and that it was ethically and safely trained.
That’s where verifiable ML property cards come in. These are not just statements—they’re backed by cryptographic proof.
🖥️ Enter TEEs: The Hardware Behind the Trust
At the heart of this solution are Trusted Execution Environments (TEEs)—secure areas in computer hardware where sensitive processes are isolated from the rest of the system.
Here’s what TEEs bring to the table:
✅ Models are trained and evaluated inside a protected zone
✅ Model behavior is measured and recorded with zero tampering
✅ Cryptographic proof (remote attestation) confirms the model's integrity
✅ Sensitive data stays secure throughout the process
This hardware-based approach offers a solid foundation for AI trustworthiness.
⚙️ Meet Laminator: A New Standard for Verified AI
Laminator is a powerful framework that uses TEEs to produce verifiable ML property cards. Here’s how it works:
🧠 ML training and inference are conducted inside a TEE
🔎 A “measurer” inside the TEE evaluates the model’s properties
📄 A property card fragment is created to certify the claims
🔐 These fragments are packaged into a verifiable assertion
This approach enables anyone to independently validate the claims—no blind trust required.
🚀 Why Laminator Stands Out
Compared to other methods like Zero Knowledge Proofs (ZKPs) or Multi-Party Computation (MPC)—which can be slow or overly complex—Laminator offers:
⚡ Speed
🔐 Strong security
📈 Scalability
🔄 Flexibility for different types of models
It’s a practical solution ready for real-world deployment.
🛠️ What’s Next for Laminator?
The team is already working on future enhancements, such as:
🧩 Running Laminator natively on Intel TDX for even better performance
💻 Leveraging NVIDIA H100 GPUs for training larger, more complex models
🧠 Supporting next-gen models like LLMs and text-to-image diffusion
🛡️ Real-time runtime attestation for live environments
🌐 A distributed ecosystem that enables cross-organization trust and validation
These innovations aim to make Laminator the gold standard for AI accountability.
✅ Conclusion: From Trust to Proof
As AI regulation tightens, verifiable ML property cards are becoming a must, not a luxury.
Laminator offers a scalable, secure, and efficient path to trustworthy AI—powered by hardware-based attestation.
In a world where “just trust us” no longer works, Laminator bridges the gap between belief and proof, ensuring AI is not just powerful—but also accountable. 🌱
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