Derivative works — US Patent 12003529

Defensive Disclosure Document: US Patent 12003529 Derivatives

This document describes derivative variations and combinations for the subject matter of US Patent 12003529, "Techniques for detecting artificial intelligence model cybersecurity risk in a computing environment," for the purpose of establishing prior art and rendering future incremental improvements obvious or non-novel. The derivations focus on expanding the scope and application of the core inventive concepts, particularly those outlined in Independent Claim 1.

Core Claim 1: Method for Detecting AI Cybersecurity Risk

Independent Claim 1: A computer-implemented method for detecting a cybersecurity risk of an artificial intelligence (AI), the method comprising:
a. inspecting a computing environment for an AI model deployed therein;
b. generating a representation of the AI model in a security database, the security database including a representation of the computing environment;
c. inspecting the AI model for a cybersecurity risk;
d. generating a representation of the cybersecurity risk in the security database, the representation of the cybersecurity risk connected to the representation of the AI model in response to detecting the cybersecurity risk; and
e. initiating a mitigation action based on the cybersecurity risk.

Derivative Variations

1. Material & Component Substitution: Neuromorphic Computing Environment and Analog AI Models

Enabling Description:
This derivative implements the method of US12003529 within a neuromorphic computing environment, utilizing analog AI models instead of conventional digital AI models. The "computing environment" inspection (Claim 1a) would involve monitoring the state and connectivity of spiking neural networks (SNNs) on specialized neuromorphic hardware (e.g., Intel Loihi, IBM TrueNorth). The "AI model" itself (Claim 1b) would be an analog neural network, with its representation capturing network topology, synapse weights, and neuron firing characteristics. Inspection for cybersecurity risks (Claim 1c) would focus on anomalies in synaptic plasticity, unexpected neuron firing patterns, or deviations in analog signal processing indicative of data poisoning through analog noise injection, adversarial analog input perturbation, or malicious modification of physical resistance states representing weights. The security database (Claim 1d) would store representations of these analog states and their historical deviations. Mitigation actions (Claim 1e) could include dynamically reconfiguring neuromorphic hardware routing, resetting synaptic weights from a trusted baseline, or physically isolating compromised neuromorphic processing units to prevent further propagation of analog malfeasance.

graph TD
    A[Neuromorphic Compute Environment] -- Inspect (SNNs, Hardware State) --> B(Analog AI Model Detection)
    B -- Generate Representation --> C{Security Database (Analog States, Topology)}
    C -- Inspect for Risk (Synaptic Anomalies, Firing Patterns) --> D(Cybersecurity Risk Detection)
    D -- Generate Risk Representation --> C
    D -- Initiate Mitigation --> E[Neuromorphic Reconfiguration / Isolation]

2. Material & Component Substitution: Quantum Computing Environment and Quantum AI Models

Enabling Description:
This derivative applies the cybersecurity risk detection framework to quantum computing environments hosting quantum AI models. The "computing environment" (Claim 1a) would be a quantum processor unit (QPU) or a quantum annealer. "AI models" (Claim 1b) would be represented as quantum circuits (e.g., parameterized quantum circuits for variational quantum algorithms, quantum neural networks) or problem Hamiltonians for quantum annealing. The representation in the security database would include quantum circuit diagrams, qubit entanglement maps, gate sequences, and the coherence properties of the qubits. Inspection for cybersecurity risks (Claim 1c) would involve analyzing quantum circuit injection attacks, adversarial quantum samples, or decoherence-induced errors that are statistically significant beyond expected noise. This would necessitate monitoring gate fidelity, entanglement entropy, and quantum state tomography results. Risks detected (Claim 1d) would include backdoored quantum circuits or altered training Hamiltonians. Mitigation actions (Claim 1e) could involve rolling back to a known-good quantum circuit state, re-initializing qubits, or pausing execution on compromised QPUs, potentially leveraging quantum error correction codes for specific threat remediations.

graph TD
    A[Quantum Compute Environment (QPU)] -- Inspect (Circuit States, Qubit Coherence) --> B(Quantum AI Model Detection)
    B -- Generate Representation --> C{Security Database (Quantum Circuits, Entanglement)}
    C -- Inspect for Risk (Adversarial Samples, Decoherence Anomaly) --> D(Cybersecurity Risk Detection)
    D -- Generate Risk Representation --> C
    D -- Initiate Mitigation --> E[Quantum Circuit Rollback / Qubit Re-init]

3. Operational Parameter Expansion: Ultra-Low Latency Edge AI Risk Detection

Enabling Description:
This derivative focuses on detecting cybersecurity risks in AI models deployed on edge devices requiring ultra-low latency inference and real-time mitigation. The "computing environment" (Claim 1a) would encompass a fleet of heterogeneous edge devices (e.g., IoT gateways, smart cameras, industrial controllers) operating with limited resources. "AI models" (Claim 1b) would be highly optimized, lightweight models (e.g., TinyML, quantized neural networks) embedded directly on microcontrollers or specialized edge ASICs. Inspection (Claim 1c) would occur continuously, potentially leveraging hardware-level performance monitoring units (PMUs) and on-device Trusted Execution Environments (TEEs) to detect subtle deviations in inference results, power consumption, or memory access patterns at microsecond granularity. The "security database" (Claim 1d) could be a distributed ledger or a highly optimized time-series database running on a local edge aggregator, focused on storing only critical, actionable risk indicators. Mitigation actions (Claim 1e) would be pre-programmed, hardware-accelerated responses such as immediate model disabling, fail-safe mode activation, or local network isolation (e.g., disabling specific network interfaces on the edge device) within milliseconds of detection.

sequenceDiagram
    participant EdgeDevice
    participant OnDeviceInspector
    participant EdgeAggregator/DB
    participant CloudSecurityPlatform

    EdgeDevice->>OnDeviceInspector: Real-time AI Inference Data
    OnDeviceInspector->>OnDeviceInspector: Inspect AI Model (PMU, TEE)
    alt Anomalous Behavior Detected
        OnDeviceInspector-->>EdgeAggregator/DB: (Ultra-low Latency) Send Risk Indicator
        EdgeAggregator/DB-->>EdgeAggregator/DB: Generate Risk Representation
        EdgeAggregator/DB-->>EdgeDevice: Initiate Mitigation (Hardware-accel)
        EdgeDevice->>EdgeDevice: Apply Mitigation (Disable Model, Fail-Safe)
    else No Anomaly
        OnDeviceInspector-->>EdgeAggregator/DB: (Periodic) Send Heartbeat/Summary
    end
    EdgeAggregator/DB-->>CloudSecurityPlatform: (Batch/Async) Send Aggregate Risk Data

4. Operational Parameter Expansion: Planetary-Scale Federate Learning (FL) Risk Monitoring

Enabling Description:
This derivative extends the patent's methodology to monitor AI models in planetary-scale federated learning (FL) environments, where models are trained collaboratively across vast numbers of distributed clients without centralizing data. The "computing environment" (Claim 1a) is effectively a global mesh of client devices (e.g., smartphones, IoT devices, medical imaging machines). "AI models" (Claim 1b) are the local model weights or gradients contributed by each client to a global model. The "security database" (Claim 1d) must be a highly distributed, eventually consistent ledger or graph database capable of representing millions or billions of individual client model states and their aggregation history. Inspection for cybersecurity risks (Claim 1c) involves detecting adversarial contributions (e.g., data poisoning on client devices, model inversion attacks on shared gradients), model leakage, or backdoor insertions within aggregated model updates. This would require sophisticated differential privacy analysis, anomaly detection on aggregated gradients, and cryptographic proofs of training data integrity from client devices. Mitigation actions (Claim 1e) would include isolating malicious clients, rejecting suspicious model updates, or rolling back the global model to a previous verified state, implemented through a distributed consensus mechanism.

graph LR
    subgraph Client Fleet (Global)
        C1(Client 1)
        C2(Client 2)
        ...
        CN(Client N)
    end

    C1 -- Local AI Model / Gradients --> Aggregator[Federated Aggregator]
    C2 -- Local AI Model / Gradients --> Aggregator
    CN -- Local AI Model / Gradients --> Aggregator

    Aggregator -- Inspect (Gradient Anomaly, Data Leakage, Backdoors) --> RiskDetector(FL Risk Detector)
    RiskDetector -- Generate Representation --> SecurityDB(Distributed Security Database)
    SecurityDB -- Connect Risk to Model Rep --> SecurityDB
    RiskDetector -- Initiate Mitigation --> Aggregator
    Aggregator -- Mitigation Action --> C1, C2, CN (Isolate Client, Reject Update)

5. Cross-Domain Application: Cybersecurity Risk Detection for Autonomous Vehicle AI

Enabling Description:
This derivative applies the core method to AI models governing critical functions in autonomous vehicles (AVs). The "computing environment" (Claim 1a) is the vehicle's onboard computational platform, including its various Electronic Control Units (ECUs) and domain controllers. The "AI model" (Claim 1b) refers to perception models (e.g., object detection, lane keeping), prediction models, planning models, and control models embedded within the AV's software stack. The security database (Claim 1d) would maintain representations of these models, their training datasets, and their operational parameters in various driving scenarios. Inspection for cybersecurity risks (Claim 1c) would involve real-time monitoring of AI model outputs against expected behavior, detecting adversarial attacks on sensor inputs (e.g., LiDAR spoofing, camera pixel manipulation), unexpected decision-making patterns, or deviations in model confidence scores. This would also include analyzing model updates for unauthorized modifications or embedded backdoors. Mitigation actions (Claim 1e) could range from engaging a minimum risk maneuver (MRM), transferring control to a human driver, isolating compromised ECUs, or triggering a diagnostic and lockdown procedure for the affected AI module.

flowchart TD
    A[AV Onboard Platform] --> B{Inspect Vehicle ECUs for AI Models}
    B --> C[Detect Perception, Prediction, Planning AI Models]
    C --> D{Generate AI Model Representation in Security DB}
    D --> E{Inspect AI Models for Cybersecurity Risk (Adversarial Input, Malicious Updates)}
    E --> F{Detect Risk (e.g., Perception Anomaly, Control Deviation)}
    F --> G[Generate Risk Representation in Security DB, Link to AI Model]
    G --> H[Initiate Mitigation Action (MRM, Human Takeover, ECU Isolation)]

6. Cross-Domain Application: Cybersecurity Risk Detection for AI in Drug Discovery and Genomics

Enabling Description:
This derivative applies the patent's method to AI models used in sensitive drug discovery and genomic analysis platforms. The "computing environment" (Claim 1a) would be a high-performance computing (HPC) cluster, cloud environment, or specialized bioinformatics workstation used for drug design, protein folding, or genomic sequencing analysis. The "AI model" (Claim 1b) would include models for molecular docking, de novo drug design (e.g., generative models), predictive toxicology, or disease diagnosis from genomic data. The security database (Claim 1d) would store representations of these models, their proprietary training datasets (e.g., chemical compound libraries, patient genomic data), and provenance metadata. Inspection for cybersecurity risks (Claim 1c) would involve detecting model inversion attacks to reconstruct sensitive training data (e.g., patient genomes, proprietary molecular structures), adversarial perturbations to drug candidates, or manipulation of predictive models to bias research outcomes. This also includes verifying the integrity of AI-generated molecular structures or genomic insights. Mitigation actions (Claim 1e) could involve revoking model access for specific users, quarantining potentially compromised research results, invalidating AI-generated designs, or initiating a full audit of the affected model and its associated data.

graph TD
    A[HPC Cluster/Cloud for Drug Discovery] -- Inspect --> B(Detect Molecular Docking, Generative AI Models)
    B -- Generate Representation (Model, Training Data) --> C{Security Database (Proprietary Data, Provenance)}
    C -- Inspect for Risk (Model Inversion, Adversarial Perturbations) --> D(Cybersecurity Risk Detection)
    D -- Generate Risk Representation --> C
    D -- Initiate Mitigation --> E[Quarantine Results / Invalidate Designs / Access Revocation]

7. Cross-Domain Application: Cybersecurity Risk Detection for AI in Smart Grid and Industrial Control Systems (ICS)

Enabling Description:
This derivative targets AI models deployed within critical infrastructure, specifically smart grid and ICS environments. The "computing environment" (Claim 1a) comprises a distributed network of SCADA systems, RTUs (Remote Terminal Units), PLCs (Programmable Logic Controllers), and cloud-connected operational technology (OT) components. The "AI model" (Claim 1b) would include predictive maintenance models, demand forecasting models, anomaly detection systems for grid stability, or optimization models for energy distribution. The security database (Claim 1d) would represent these AI models, their connections to physical assets, and their operational impact on the grid. Inspection for cybersecurity risks (Claim 1c) would focus on detecting adversarial attacks designed to disrupt grid operations (e.g., false data injection to manipulate forecasts, model degradation to hide equipment failures, or control command manipulation). This includes real-time analysis of model inputs from OT sensors, outputs sent to actuators, and internal model states for deviations. Mitigation actions (Claim 1e) would prioritize grid stability and safety, including activating emergency shutdown procedures for specific components, isolating compromised control loops, switching to manual control, or triggering predefined "black start" or "island mode" protocols for affected segments of the smart grid.

flowchart LR
    A[Smart Grid / ICS Environment] --> B{Inspect SCADA/OT for AI Models}
    B --> C[Detect Predictive Maint, Demand Forecast AI Models]
    C --> D{Generate AI Model Representation in Security DB}
    D --> E{Inspect AI Models for Cybersecurity Risk (False Data Injection, Model Degradation)}
    E --> F{Detect Risk (e.g., Grid Instability Prediction, Actuator Anomaly)}
    F --> G[Generate Risk Representation in Security DB, Link to AI Model]
    G --> H[Initiate Mitigation Action (Emergency Shutdown, Isolate Control Loop, Manual Control)]

8. Integration with Emerging Tech: AI-Driven Optimization of Risk Inspection and Mitigation

Enabling Description:
This derivative enhances the patent's method by introducing an AI-driven optimization layer for the inspection and mitigation processes. A meta-AI model (e.g., a reinforcement learning agent or an expert system) continuously analyzes the effectiveness of different inspection techniques (Claim 1c) and mitigation actions (Claim 1e) across various detected AI models (Claim 1b) and computing environments (Claim 1a), using historical risk data and mitigation outcomes stored in the security database (Claim 1d). The meta-AI dynamically adjusts inspection parameters (e.g., scan frequency, depth of analysis, specific anomaly detection algorithms) and mitigation strategies (e.g., choosing between soft quarantine vs. hard shutdown, selecting the optimal remediation script) to maximize security posture while minimizing operational disruption. This includes predicting emergent threat vectors against AI models and proactively adapting inspection profiles. The system learns which inspection heuristics are most effective for specific AI model types or deployment contexts and prioritizes resources accordingly.

graph TD
    subgraph Core Patent Method
        A(Inspect Environment for AI Model) --> B(Generate AI Model Representation)
        B --> C(Inspect AI Model for Risk)
        C --> D(Detect Cybersecurity Risk)
        D --> E(Generate Risk Representation)
        E --> F(Initiate Mitigation Action)
    end

    G[Meta-AI Optimization Engine] -- Analyze DB Feedback (Risk/Mitigation Outcomes) --> G
    G -- Dynamically Adjust --> C
    G -- Select Optimal --> F
    F --> B

9. Integration with Emerging Tech: IoT Sensor-Driven Real-time AI Model Monitoring

Enabling Description:
This derivative integrates real-time data from a pervasive network of IoT sensors to enrich the cybersecurity risk detection for AI models. The "computing environment" (Claim 1a) includes not only traditional IT/cloud infrastructure but also the physical IoT deployments from which AI models derive their inputs or influence outputs. The "AI model" (Claim 1b) is inspected for risks based on its internal state AND the integrity and contextual relevance of its real-time input data streams from IoT sensors. The security database (Claim 1d) is enhanced to include representations of IoT sensor networks, their data provenance, calibration states, and observed environmental conditions. Inspection for cybersecurity risks (Claim 1c) involves cross-referencing AI model performance and outputs with anomalous patterns detected by redundant IoT sensors, detecting sensor spoofing, data corruption at the edge, or environmental factors that could lead to AI model misbehavior or exploitation. Mitigation actions (Claim 1e) can be triggered not only by AI model anomalies but also by suspicious IoT sensor readings, leading to actions like isolating specific sensor feeds, requesting re-calibration, or temporarily switching the AI model to a more robust, less data-dependent mode.

flowchart LR
    subgraph IoT Sensor Network
        S1[Sensor 1 (Temp)]
        S2[Sensor 2 (Pressure)]
        S3[Sensor 3 (Video)]
    end

    S1 -- Real-time Data --> A[Computing Environment]
    S2 -- Real-time Data --> A
    S3 -- Real-time Data --> A

    A -- Inspect Env for AI Model --> B(Detect AI Model)
    B -- Generate AI Model Representation --> C{Security Database (AI Models, IoT Context, Sensor Data)}
    C -- Inspect AI Model & IoT Data for Risk --> D(Cybersecurity Risk Detection)
    D -- Generate Risk Representation --> C
    D -- Initiate Mitigation --> E[Isolate Sensor Feed / Model Fallback]

10. Integration with Emerging Tech: Blockchain-Verified AI Model Supply Chain and Provenance

Enabling Description:
This derivative incorporates blockchain technology to establish an immutable and verifiable supply chain for AI models and their components, significantly strengthening the "inspection" and "representation" steps. When "inspecting a computing environment for an AI model" (Claim 1a) and "generating a representation of the AI model" (Claim 1b), the system queries a distributed ledger (e.g., using Hyperledger Fabric or Ethereum) to verify the provenance of the AI model, its training data, version history, and associated code dependencies. Each significant event in the AI lifecycle (training completion, version release, deployment, data augmentation) is hashed and recorded on the blockchain. Inspection for cybersecurity risks (Claim 1c) now explicitly includes verifying the integrity and authenticity of the AI model against its recorded blockchain hash. Discrepancies indicate tampering or unauthorized modification. The "security database" (Claim 1d) links its internal representation to these blockchain records. Risks detected could include unverified model versions, unauthorized training data deviations, or compromised model weights not matching blockchain-registered hashes. Mitigation actions (Claim 1e) could include automatic rollback to a blockchain-verified model version, flagging the model as untrustworthy, or initiating a forensic audit using the immutable blockchain history.

graph TD
    subgraph AI Model Lifecycle
        TD[Training Data] -- HASH & Register --> BC(Blockchain Ledger)
        M_Train[Trained Model Version 1] -- HASH & Register --> BC
        M_Deploy[Deployed Model Version 1.1] -- HASH & Register --> BC
    end

    A[Computing Environment] -- Inspect AI Model (Local) --> B(Local AI Model Detected)
    B -- Generate Representation & Query BC --> C{Security Database (Local Rep, BC Hash Link)}
    C -- Inspect AI Model for Risk (Compare Local Hash to BC) --> D(Cybersecurity Risk Detection)
    D -- Detect Risk (Hash Mismatch, Unverified Provenance) --> E[Generate Risk Representation & Link to BC Record]
    E -- Initiate Mitigation --> F[Rollback to BC-Verified Version / Flag Untrustworthy]

11. The "Inverse" or Failure Mode: Fail-Safe AI Risk Detection with Graceful Degradation

Enabling Description:
This derivative describes a fail-safe mode for the AI cybersecurity risk detection system, enabling graceful degradation of service rather than catastrophic failure. In scenarios where the core inspection environment (130 in US12003529) experiences resource constraints or partial compromise, the system enters a "limited-functionality mode." "Inspecting a computing environment" (Claim 1a) and "inspecting the AI model" (Claim 1c) would be automatically scaled back. For example, instead of deep dynamic analysis, only static analysis of critical AI model binaries and configuration files occurs at reduced frequency. The "security database" (Claim 1d) might only store aggregated, high-level risk indicators, reducing data ingestion and processing load. "Generating a representation of the cybersecurity risk" (Claim 1d) would prioritize only high-severity, directly exploitable vulnerabilities. Mitigation actions (Claim 1e) would default to pre-approved, lowest-impact interventions, such as generating alerts to human operators for manual review rather than automated actions like model shutdowns. The system would continuously monitor its own health and resource availability, dynamically adjusting its operational mode (e.g., from full inspection to limited-functionality, or to "alert-only" mode) to maintain a minimal level of cybersecurity coverage under duress.

stateDiagram
    [*] --> Healthy
    Healthy --> ResourceConstraint: System Overload
    Healthy --> PartialCompromise: Inspector Failure

    ResourceConstraint --> DegradedMode: Auto-Scale Back
    PartialCompromise --> DegradedMode: Fail-Safe Activation

    DegradedMode --> LimitedInspection: Reduced Freq/Depth
    DegradedMode --> BasicRiskReporting: High-Severity Only
    DegradedMode --> ManualMitigationPrompt: Human Intervention

    LimitedInspection --> Healthy: Resources Restored
    BasicRiskReporting --> Healthy: All Systems Online
    ManualMitigationPrompt --> Healthy: Issue Resolved

    DegradedMode --> AlertOnlyMode: Severe Failure
    AlertOnlyMode --> Healthy: Critical Systems Restored

Combination Prior Art Scenarios

Combination with Open Policy Agent (OPA) for Policy-Driven Mitigation:
The core method of US12003529 (inspecting AI models, detecting risks, generating representations) can be combined with the Open Policy Agent (OPA) framework (an open-source, general-purpose policy engine). The "security database" (Claim 1b, 1d) would export its graph-based representations of AI models and detected risks to OPA's data plane. OPA would then evaluate these representations against predefined security policies (e.g., "AI models with detected secrets must not be public-facing," "models trained on sensitive data must not have lateral movement paths to production resources"). The "initiating a mitigation action" step (Claim 1e) would be directly managed by OPA, which could enforce policy decisions by calling appropriate remediation APIs (e.g., modifying network security groups, revoking access permissions, or triggering CI/CD pipeline rollbacks) based on its policy evaluation results. This provides a standardized, declarative approach to automating mitigation.
Combination with OWASP Top 10 for Large Language Models (LLMs) as Risk Inspection Criteria:
The "inspecting the AI model for a cybersecurity risk" step (Claim 1c) can be explicitly structured around the publicly available OWASP Top 10 for LLMs. This open-source standard provides a widely recognized list of common vulnerabilities and attack vectors specific to LLMs (e.g., Prompt Injection, Insecure Output Handling, Training Data Poisoning, Model Denial of Service). The AI detector's inspection heuristics would directly map to these OWASP categories. For instance, detecting prompt injection risk might involve analyzing input sanitization routines, while training data poisoning detection would involve inspecting the provenance and integrity of the training dataset for anomalies as per OWASP guidance. The "generating a representation of the cybersecurity risk" (Claim 1d) would categorize detected risks according to the OWASP Top 10 for LLMs taxonomy, providing a standardized, openly understood framework for risk reporting and prioritization.
Combination with Cloud Native Computing Foundation (CNCF) projects like Falco (Runtime Security) and OpenTelemetry (Observability):
The "inspecting a computing environment" (Claim 1a) and "inspecting the AI model" (Claim 1c) steps can be augmented by integrating with Falco for real-time runtime security and OpenTelemetry for comprehensive observability. Falco (an open-source project for cloud-native runtime security) would monitor AI model containers or serverless functions for suspicious system calls, file access patterns (e.g., unexpected access to training data during inference), or network activity that deviates from established baselines, directly contributing to risk detection. OpenTelemetry (an open-source observability framework) would collect and export AI model telemetry data (e.g., inference latency, resource utilization, API calls made by the AI model) alongside contextual traces and logs. This data, stored or referenced in the "security database" (Claim 1b, 1d), would provide enriched context for anomaly detection in AI model behavior, allowing for more precise identification of cybersecurity risks beyond just static code analysis or metadata inspection. The collected data would feed into AI model risk analysis and provide detailed insights for automated or human-driven mitigation actions.