Model Integrity Verifier

Model Integrity Verifier

“Trust in AI begins with proving it hasn’t been tampered with.”

NovaQ’s Model Integrity Verifier is a next-gen solution that simulates cryptographically verifiable attestation of AI models — without needing direct access to hardware Trusted Execution Environments (TEEs). It brings transparency, traceability, and trust into AI deployments using post-quantum security foundations.

Key Capabilities:

• Secure Enclave Simulation: Mimics Intel SGX-like environments virtually, allowing users to simulate the behavior of secure enclaves for model validation. This helps demonstrate that models were executed or deployed in trusted environments, without needing physical enclave hardware.

• Hash-Based Identity Fingerprinting: Each AI model snapshot is hashed using SHA3-512 or BLAKE3 — both considered robust against quantum attacks. These hashes serve as unforgeable identifiers, enabling reproducibility and instant detection of unauthorized changes.

• Merkle-Linked Logs: All logs related to deployment, training versions, inference endpoints, and model hashes are stored in a Merkle tree structure, ensuring tamper-proof evidence trails. Even a single bit change in the history creates a noticeable fingerprint mismatch.

• MCP Node Simulation: The bot can simulate a Multi-Consensus Protocol (MCP) where multiple “nodes” digitally sign off on model authenticity, mimicking decentralized attestation — useful for showcasing transparency in DeAI (Decentralized AI) environments.

Real-World Impact:

• Enterprises can demonstrate model compliance during audits.

• Developers can prove originality and integrity of AI products.

• Projects can resist claims of model tampering or data manipulation.

• Investors and partners gain trust in AI systems before integration.