A method and device for snapshotting decision logic and full-link hardware judicial evidence for AI autonomous behavior

Abstract

The application discloses a kind of decision logic snapshot and whole-link hardware judicial evidence method and device for AI autonomous behavior, and is executed by independent hardware evidence unit.Evidence is recorded before being forced to pass the real-time verification of AI root identity validity by the encryption challenge containing different single effective random number each time, and evidence is recorded and high-risk operation is terminated if verification fails.High-risk operation determination and external risk control unit real-time linkage.Hardware snapshot capture circuit synchronously captures complete decision link, and generates the behavior logic snapshot of traceable key decision node causal chain.All evidence is solidified in physically one-time programmable fuse storage medium after whole hash, and forms sustainable verification, non-deletable or modified judicial evidence.Zeroknowledge verification fails when determining that evidence package integrity has been destroyed.In addition, the application also protects the cross-entity joint evidence recording of multiple AI entities, the adaptive conversion of cross-jurisdiction evidence recording format and the hierarchical life cycle management mechanism of evidence recording package, which provides a global judicial compatible, storage sustainable and tamper-proof evidence recording scheme for AI autonomous behavior.

Description

Technical Field

[0001] This invention relates to the fields of AI security and electronic evidence preservation, and in particular to a method and hardware device for effective judicial evidence preservation of AI autonomous behavior throughout the entire process. Background Technology

[0002] AI-driven autonomous decision-making is often viewed as a "black box," lacking verifiable records of the decision-making process that can be accepted by the courts. Existing evidence preservation schemes do not enforce real-time verification of the AI's identity before generating evidence, allowing forged or expired AIs to generate false evidence. Furthermore, the determination of high-risk operations is disconnected from dynamic risk management systems, resulting in evidence preservation lacking hardware-level binding to the AI's identity and real-time risk status. Summary of the Invention

[0003] This invention provides a judicial evidence preservation method executed by an independent hardware evidence preservation unit. Before generating any evidence data, it mandates the verification of the AI ​​root identity through a cryptographic challenge, embedding the verification evidence into the evidence preservation package. The determination of high-risk operations is linked in real-time with an external risk management unit. A snapshot of the entire decision-making process is captured synchronously, hashed, and then solidified into a one-time programmable storage medium, forming an immutable chain of judicial evidence. Attached Figure Description

[0004] Figure 1 This is a flowchart of the judicial evidence preservation process of the present invention. Detailed Implementation

[0005] The hardware evidence storage unit is physically isolated from the AI ​​main system. Before generating evidence data, the real-time validity of the AI ​​root identity is verified through an encrypted challenge containing a single valid random number that is different for each verification. If the verification passes, the verification evidence is embedded in the evidence storage package; if the verification fails, the entire evidence storage and high-risk operation process is terminated and the failure record is fixed. The determination of high-risk operations integrates the real-time risk scores from its own fixed list, update area, and external risk control unit. If the determinations are inconsistent, the highest risk level shall prevail. When the AI ​​performs a high-risk operation, a signed proxy declaration certificate is mandatory, which contains the identity binding relationship of at least one human subject who bears legal responsibility for the action. A complete snapshot of the decision-making chain is captured synchronously, all evidence is packaged and hashed as a whole. Finally, the hash value is fixed to a one-time programmable storage medium, forming a continuously verifiable, undeletable, and unmodifiable judicial evidence. Zero-knowledge verification is supported during auditing. When multiple AI entities collaborate to complete the same high-risk operation, the hardware evidence storage unit of the AI ​​entity leading the collaborative operation generates a cross-entity joint evidence package and solidifies it. The hardware evidence storage unit has pre-built electronic evidence format templates for multiple jurisdictions, which can automatically convert the format of the evidence package. At the same time, the evidence package implements hierarchical lifecycle management. Recently stored evidence is stored locally, and evidence older than the specified time is automatically archived to a dedicated node. After archiving, its hash value is still retained locally for rapid verification. The integrity of the archived evidence package is protected by the hardware root of trust signature of the original hardware evidence storage unit. It should be noted that the main causal reasoning process described in this specification and claims refers to the causal chain from receiving the task to producing the decision result at the key decision nodes.

Claims

1. A method for AI autonomous behavior-oriented decision logic snapshot and full-link hardware judicial evidence, characterized in that, The following steps are executed by a hardware evidence storage unit independent of the AI ​​entity's main processor and main operating system; the hardware evidence storage unit has an independent secure processor core, an independent secure storage area, and an independent power domain, and includes a cryptographic operation engine, a snapshot capture circuit, and a one-time programmable storage medium; each step is executed internally by the hardware evidence storage unit in an atomic operation manner that cannot be interrupted or tampered with by external software: Real-time authentication steps: Before generating any evidence data, the hardware evidence storage unit initiates an encryption challenge to the hardware identity governance unit bound to the AI ​​entity to verify the validity and authenticity of the AI ​​entity's global root identity identifier; the encryption challenge contains a single valid random number generated by the hardware evidence storage unit that is different for each verification; after the verification is passed, the challenge value, response value and timestamp of this verification are used as identity verification evidence and embedded in the subsequently generated end-to-end evidence package; If verification fails, the evidence storage process and the high-risk operation will be terminated, and a record of identity evidence storage failure will be generated and permanently stored. Mandatory declaration steps: When an AI entity attempts to perform a pre-defined high-risk operation, the hardware evidence storage unit mandates that the operation instruction be accompanied by a proxy declaration credential digitally signed by the cryptographic processing engine. The credential contains the root identity of the AI ​​entity, the identity binding relationship of at least one human subject who bears legal responsibility for the action, and the claim of the operation. The proxy declaration credential and the payload of the current operation instruction are digitally signed together by the cryptographic processing engine. Steps for generating a decision snapshot: The snapshot capture circuit of the hardware evidence storage unit synchronously captures and generates a complete behavioral logic snapshot of the decision-making process during the AI ​​entity's decision-making process. The behavioral logic snapshot is a structured data object, which contains data items that at least cover the timestamps of key decision nodes, input features, model inference paths, and confidence scores. The data items are sufficient to enable a third party to trace the main causal reasoning process of the AI ​​entity in completing this operation based on the snapshot. Evidence preservation and solidification steps: The hardware evidence storage unit packages the proxy declaration credential, behavioral logic snapshot, operation core data file, and identity verification evidence, performs an overall hash operation using the cryptographic operation engine, and solidifies the final hash value into the physical one-time programmable fuse storage medium inside the hardware evidence storage unit; after the medium is written, the physical circuit state of its storage unit is irreversibly changed; the evidence storage record can be continuously verified and cannot be deleted or modified.

2. The method of claim 1, wherein, The determination of high-risk operations is jointly defined by the operation type list fixed inside the hardware evidence storage unit, a high-risk operation update area verified by the hardware root of trust signature, and a real-time risk score sent by an external risk management unit. Any one of the three sources defines the current operation as high-risk, thus triggering the evidence preservation process; If different sources give inconsistent risk levels for the same operation, the highest risk level shall prevail.

3. The method of claim 1, wherein, It also includes a zero-knowledge verification step: only the fixed hash value and the evidence timestamp are provided to the auditor, and the verifier verifies the integrity of the evidence package by comparing the hash value, without obtaining the original data; if the comparison is inconsistent, it is determined that the integrity of the evidence package has been compromised.

4. The method of claim 1, wherein, The full-chain evidence package adopts a hierarchical storage mechanism: the original data containing trade secrets is stored on a private server, while the hash value and ownership information used for judicial verification in the jurisdiction where the evidence is submitted or the jurisdiction where the evidence is submitted are stored on a public evidence storage platform.

5. The method of claim 1, wherein, The evidence storage event records generated by this method interact with at least one external governance system through a unified data interface; the interaction protocol of the unified data interface includes at least: identity anomaly notification, risk linkage rating instruction, permission forced revocation instruction, compliance circuit breaker status synchronization, and data format definition for triggering evidence storage events.

6. A hardware attestation apparatus, characterized by, It includes an independent secure processor core, an independent secure storage area, an independent power domain, a cryptographic operation engine, a snapshot capture circuit, and a one-time programmable storage medium, and is configured to perform the method described in any one of claims 1 to 5.

7. The method according to claim 1, characterized in that, When multiple AI entities collaborate to complete the same high-risk operation, the hardware evidence storage unit of each participating AI entity generates its own behavioral logic snapshot and proxy declaration credential. The hardware evidence storage unit of the AI ​​entity leading the collaborative operation packages the snapshots and credentials of each participating AI entity into a cross-entity joint evidence package. After performing an overall hash operation on the cross-entity joint evidence package, it is solidified and stored. The cross-entity joint evidence package contains the global root identity identifier of each participating AI entity.

8. The method according to claim 1, characterized in that, The hardware evidence storage unit has multiple electronic evidence format templates pre-installed in the legal jurisdiction. When generating the evidence package, the hardware evidence storage unit converts the evidence package into an evidence format that conforms to local judicial standards according to the legal requirements of the legal jurisdiction where the AI ​​entity operates. The evidence format conversion process is completed inside the hardware evidence storage unit, and the evidence packages before and after conversion are both solidified by hardware root trust signature.

9. The method according to claim 1, characterized in that, The hardware evidence storage unit implements hierarchical lifecycle management for evidence storage packages: recently generated evidence storage packages are stored inside the hardware evidence storage unit, and evidence storage packages that exceed the preset time are automatically archived to a dedicated evidence archiving node in the trusted network. After archiving, their hash values ​​are still retained in the original hardware evidence storage unit for rapid verification. The archiving and transmission process employs end-to-end encryption, and the integrity of the archived evidence package is protected by the hardware root of trust signature of the original hardware evidence storage unit.

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