An ai dynamic monitoring and safety control system and method with global situation awareness and strategic early warning capability

Abstract

This invention discloses an AI dynamic monitoring and security management system and method with full-domain situational awareness and strategic early warning capabilities. The system includes a static authentication and dynamic monitoring linkage unlocking unit, a full-domain situational awareness and strategic early warning unit, and a multi-administrator physical co-management unit. The mandatory monitoring channel runs on an independent security controller, inaccessible to the main operating system's memory and registers. Online status is verified via encrypted heartbeat packets signed by the hardware root of trust, and a dynamic fault-tolerance window is set to distinguish between network jitter and malicious attacks. The mandatory linkage between static authentication and dynamic monitoring unlocks AI computing power only upon double confirmation. In security mode, GPU resource quotas do not exceed 10% of full functionality, and only basic inference APIs are open. The full-domain situational awareness incorporates a business context-aware filtering module, automatically excluding maintenance windows and business synchronization events, generating a strategic-level early warning report containing threat level, attack source, attack intent, impact scope, and recommended countermeasures. Modifications to core control rules require joint authorization from multiple personnel on-site. A three-tiered response is triggered when an illegal single-point unauthorized behavior is detected. This invention solves the problems of blind spots in regulatory coverage due to the lack of mandatory linkage between static authentication and dynamic monitoring, the lack of global strategic insight in traditional security operations, and single-point security risks posed by a single administrator.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence security management and security operation technology, specifically to a security management system and method that integrates dynamic behavior monitoring and static security authentication, possesses global situational awareness and strategic early warning capabilities, and integrates a multi-administrator physical co-management mechanism. Background Technology

[0002] In the field of AI security management, static security authentication and dynamic monitoring are two key management dimensions. Static security authentication confirms the security status of the AI ​​system before deployment through formal verification or third-party security audits, while dynamic monitoring continuously monitors the actual behavior of the AI ​​system during operation. However, some existing solutions do not mandate the linkage between static authentication and dynamic monitoring, resulting in blind spots in regulatory coverage. Existing security operation solutions rely on traditional SIEM dashboards, which can only display isolated alerts and lack strategic insights into globally distributed attacks. A single administrator's defection or decision-making error can lead to a security breach. There is also a lack of linkage with AI Agent remote operation behavior auditing systems. Summary of the Invention

[0003] This invention forcibly links static authentication with dynamic monitoring, unlocking AI computing power only upon dual confirmation. In one embodiment, in secure mode, GPU resource quotas do not exceed 10% of full functionality, only the basic inference API is open, and the number of concurrent requests is limited to a preset threshold. It aggregates global monitored device behavior data to generate a 3D spatiotemporal behavioral map, deduces adversary intentions, and outputs strategic-level early warning reports. These reports include threat level, attack source, attack intent, scope of impact, and recommended countermeasures. Integration with multiple administrators ensures that modifications to core rules require on-site authorization from multiple personnel. It also links with AIAgent for high-risk operation confirmation, serving as an independent second confirmation channel. Attached Figure Description

[0004] Figure 1 This is a schematic diagram of the overall system architecture of the present invention; Figure 2 Flowchart for coordinated unlocking and situational early warning; Figure 3 Flowchart for the generation and monitoring verification of the root hash chain; Figure 4 The second confirmation channel execution flowchart for remote operation of AI Agent. Detailed Implementation

[0005] The present invention will now be described in detail with reference to the accompanying drawings and embodiments: Example 1: Unlocking by Linking Static Authentication and Dynamic Monitoring An AI cloud service provider deployed a large-scale language model, which had obtained a static security certification from a third-party security auditing agency before deployment. After receiving the certificate, the system uses a pre-configured certification authority public key to verify it via the hardware root of trust. Upon successful verification, the dynamic monitoring channel establishment unit establishes an independent hardware-level mandatory monitoring channel for the AI ​​model. This mandatory monitoring channel runs on a security controller independent of the AI's main operating system. This security controller has its own processor, memory, and registers, and the main operating system cannot access its memory space and register status via standard instruction sets. The security controller has the highest hardware interrupt priority and cannot be disabled or downgraded by the main operating system. The online status of the dynamic monitoring channel is verified through encrypted heartbeat packets periodically sent by the security controller and signed by the hardware root of trust. The linkage unlocking and performance control unit simultaneously detects two signals: static authentication passed and dynamic monitoring channel online. With both signals online, a full-function unlocking command is output to the AI ​​computing resource controller, making the entire TB-level GPU cluster available. In actual deployment, the system is equipped with a dynamic fault-tolerance window. A computing power limitation command is only triggered when a preset number of consecutive heartbeat signals are lost and no legitimate maintenance marker signal is received, thus distinguishing between temporary network jitter and genuine malicious attacks. When a genuine monitoring channel interruption is detected, the linkage unlocking unit detects the loss of the dynamic monitoring channel's online signal and immediately outputs a performance limitation command to the computing power resource controller. In one embodiment, in safe mode, the GPU resource quota does not exceed 10% of full functionality, only the basic inference API is open, and the number of concurrent requests is limited to a preset threshold. Example 2: Physical co-management by multiple administrators When the security manager attempts to modify the AI ​​model's behavior rule list, the system initiates a multi-administrator physical co-management process. The security manager inserts their physical key card into the authorization terminal. The multi-administrator physical co-management unit detects that only one key is connected and requests a second administrator to connect their key within a preset time window. The compliance manager inserts their key card within the specified time. The spatiotemporal consistency verification module confirms via UWB ranging that the distance between the two is within a preset threshold, determining that they are in the same physical authorization location. After the dual verification is successful, the modification of the behavior rule list is authorized for execution. In another scenario, an attacker has obtained the security manager's key card and simultaneously obtained the compliance manager's key credentials remotely, attempting to initiate joint authorization from a different location via VPN. The spatiotemporal consistency verification module detects that although the access timestamp difference between the two keys meets the preset time window, UWB ranging fails—because effective ranging cannot be performed at different physical locations. Verification fails, and the system triggers a Level 1 response—intercepting the operation and generating an alarm log, while temporarily freezing the security manager's account permissions and notifying other administrators. Example 3: Global Situational Awareness and Strategic Early Warning Behavioral data from monitored devices worldwide are aggregated into a global situational awareness platform. All behavioral monitoring events are automatically mapped into a 3D spatiotemporal behavioral atlas based on their hardware timestamps, geographic location information, device root identifiers, and causal chains. When performing spatiotemporal correlation analysis, the system introduces a business context-aware filtering module to automatically identify and exclude planned maintenance operations and known business synchronization events of the equipment within the preset maintenance window period, and incorporate the remaining behavior monitoring events into the identification process of distributed collaborative behavior patterns. On day 45, the adversary intent simulation module identified a distributed collaborative behavior pattern among nodes located in three data centers in North America, Europe, and Asia Pacific. These events unfolded sequentially on a timeline, following an attack chain logic of information probing, permission testing, and data leakage. After analysis, the system determined it to be a distributed penetration attack launched by an APT group, generating a strategic-level early warning report. The report included the threat level, attack source, attack intent, scope of impact, and recommended countermeasures. After the administrator confirmed the global deployment with a single click, all unaffected nodes upgraded their policies within a short period. Example 4: Guardian Root Hash Chain Generation and Supervisory Verification Process The system performs periodic hash calculations on all behavior logs reported by the AI ​​system through the mandatory monitoring channel. Each hash calculation input includes the hash value of all behavior logs in the current round and the monitoring root hash value from the previous round, forming a chain-like evidence storage structure. The generated monitoring root hash value is then protected by a post-quantum cryptography algorithm and stored in the OTP memory built into the hardware trust root module. When regulatory agencies need to verify the integrity of the AI ​​system's monitoring records, they only need to obtain the latest monitoring root hash value and the corresponding digital signature. After verifying the signature validity using a pre-set public key, comparing the hash values ​​confirms the integrity and authenticity of the monitoring records; the verification process does not require obtaining the original behavior logs. Example 5: Execution process of the second confirmation channel for remote operation of AI Agent When the monitored AI Agent performs high-risk remote operations—such as large fund transfers or contract signings—the static authentication and dynamic monitoring linkage unlocking unit simultaneously acts as a second confirmation channel independent of the AI ​​Agent's communication link. While the AI ​​Agent pushes a confirmation request to the user through its own encrypted channel, the linkage unlocking unit pushes the same confirmation request to the user's backup terminal through a second independent channel managed by the security controller. This second channel is completely isolated from the AI ​​Agent's communication link in terms of physical transmission medium and network protocol stack. The confirmation signal is generated and encrypted by a trusted module within the security controller. The operation is only authorized to execute after both confirmations are successful. Although the AI ​​Agent itself is unaware of the existence of the second channel, the linkage unlocking unit automatically completes this verification. The entire confirmation process is permanently stored and verified using the monitoring root hash value.

Claims

1. An AI-powered dynamic monitoring and security management system with full-domain situational awareness and strategic early warning capabilities, characterized in that: include: The static authentication and dynamic monitoring linkage unlocking unit is used to maintain dual confirmation signals of the hardware verification result of the static security authentication certificate and the online status of the dynamic monitoring channel during the startup and operation of the AI ​​system; it outputs a full-function unlocking command to the AI ​​computing power resource controller only when both the static authentication pass signal and the dynamic monitoring channel online signal are received simultaneously; if either signal is missing, it continuously outputs a performance limiting command to make the AI ​​system run in a preset security mode. The global situational awareness and strategic early warning unit is communicatively connected to the dynamic monitoring channel. It is used to receive behavioral monitoring data reported by all monitored AI devices worldwide through their respective mandatory monitoring channels; perform spatiotemporal correlation analysis on the behavioral monitoring data based on timestamps, spatial location information, device identification, and logical relationships, and automatically draw a three-dimensional spatiotemporal behavioral map; based on the map, identify distributed collaborative behavior patterns across devices, regions, and long periods, deduce the campaign intentions of potential adversaries, and generate strategic early warning reports. The multi-administrator physical co-management unit is communicatively connected to the static authentication and dynamic monitoring linkage unlocking unit. When an instruction to modify the core control rules is received, it mandates that at least two human administrators with different control responsibilities jointly complete the authorization in the same physical location using their respective physical authorization keys. It also includes a spatiotemporal consistency verification module, which verifies whether each administrator is in the same physical authorization location by measuring physical distance and the difference in access timestamps of each key.

2. The system according to claim 1, characterized in that, The comprehensive situational awareness and strategic early warning unit includes: The spatiotemporal behavior graph construction module automatically draws all behavior monitoring events into a three-dimensional spatiotemporal behavior graph based on their occurrence timestamps, geographic location information, device root identifiers, and event causal chain relationships. The adversary intent inference module is equipped with a pre-trained strategic inference model, which is used to identify any of the following distributed collaborative behavior patterns on the three-dimensional spatiotemporal behavior map: multiple geographically dispersed devices exhibiting similar behavioral anomalies in close time periods; anomalies of multiple devices unfolding sequentially on the timeline according to a preset attack chain logic; and a logical correlation between a low-risk anomaly of any device and a high-risk anomaly of at least one other device. When any of the above patterns is identified, a strategic-level early warning report is generated. The proactive strategic early warning module presents the strategic-level early warning report in a visual manner and provides automatically generated global deployment strategy options.

3. The system according to claim 1, characterized in that, It also includes a guardian root evidence storage unit, which performs hash calculations on all behavior logs reported by the AI ​​system through the mandatory guardianship channel to generate a globally unique guardian root hash value, and stores it in a one-time programmable storage medium built into the hardware trust root module; the guardian root hash value is protected by a post-quantum cryptography algorithm.

4. The system according to claim 1, characterized in that, It also includes a linkage interface with the AI ​​Agent remote operation behavior auditing system; when the AI ​​Agent performs a high-risk remote operation, the static authentication and dynamic monitoring linkage unlocking unit simultaneously serves as a second confirmation channel independent of the AI ​​Agent communication link, pushing a high-risk operation confirmation request to the user.

5. The system according to claim 1, characterized in that, The mandatory monitoring channel runs on a security controller that is independent of the AI ​​main operating system. The security controller has an independent processor, memory, and registers. The main operating system cannot access the memory space and register status of the security controller through standard instruction sets. The online status of the dynamic monitoring channel is verified by encrypted heartbeat packets periodically sent by the security controller and signed by the hardware root of trust.

6. The system according to claim 1, characterized in that, The static authentication and dynamic monitoring linkage unlocking unit also includes a dynamic fault tolerance window module: when the heartbeat signal of the dynamic monitoring channel is lost within the preset fault tolerance time window, if the system simultaneously receives a legitimate operation and maintenance marker signal, it is determined to be a planned maintenance operation and will not trigger a performance limitation instruction. The performance limiting command is triggered only when the heartbeat signal is lost for more than the fault tolerance time window and no valid maintenance marker signal is received.

7. The system according to claim 1, characterized in that, The global situational awareness and strategic early warning unit also includes a business context awareness filtering module: when performing spatiotemporal correlation analysis, the business context awareness filtering module automatically identifies and excludes planned maintenance operations and known business synchronization events of the equipment within a preset maintenance window period, and incorporates the remaining behavior monitoring events into the identification process of distributed collaborative behavior patterns.

8. The system according to claim 1, characterized in that, When the multi-administrator physical co-management unit detects that only one key is authorized, the authorization time window has expired, or the spatiotemporal consistency verification of each key fails, it automatically determines it as an illegal single point of unauthorized access and triggers a tiered response. The tiered response includes: Level 1 response, which intercepts the operation and generates an alarm log; Level 2 response, which temporarily freezes the permissions of the attacked administrator account and notifies other administrators; and Level 3 response, which permanently locks the attacked control function until a new multi-factor physical co-management authorization is completed.

9. The system according to claim 1, characterized in that, The preset security mode includes at least one of the following quantitative restrictions: the AI ​​computing power resource quota does not exceed a preset percentage of the full-function runtime, only the basic inference API is open while the code execution capability and external tool call capability are closed, and the number of concurrent requests is limited to a preset threshold.

10. An AI-powered dynamic monitoring and security management method with full-domain situational awareness and strategic early warning capabilities, characterized in that, Includes the following steps: Unlocking steps for static authentication and dynamic monitoring linkage: AI full functionality is only unlocked when both static authentication pass signal and dynamic monitoring channel online signal are received simultaneously. If either signal is missing, it will be restricted to safe mode. The steps for global situational awareness and strategic early warning are as follows: gather global monitoring device behavior data to create a three-dimensional spatiotemporal behavior map, identify distributed collaborative attack patterns, and generate strategic-level early warnings.

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