> For the complete documentation index, see [llms.txt](https://novaq-docs.gitbook.io/novaq/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://novaq-docs.gitbook.io/novaq/novaq-ecosystem/model-integrity-verifier.md).

# 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.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://novaq-docs.gitbook.io/novaq/novaq-ecosystem/model-integrity-verifier.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.