Patent 12001549
Derivative works
Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.
Active provider: Google · gemini-2.5-flash
Derivative works
Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.
Defensive Disclosure: US Patent 12001559
This defensive disclosure aims to broaden the scope of existing prior art related to US Patent 12001549, "Cybersecurity incident response techniques utilizing artificial intelligence," by detailing numerous derivative works. These variations are intended to render obvious or non-novel future incremental improvements by competitors, thereby strengthening the public domain in this technological area.
The core innovative method described in US12001549, as summarized in the patent, includes:
- Mapping Incident Input to Scenario: Mapping a received incident input into a scenario of a plurality of scenarios, each scenario including a plurality of sub-scenarios.
- Generating Query: Generating a query based on the received incident input and a selection of a sub-scenario of the plurality of sub-scenarios.
- Executing Query: Executing the query on a security database, the security database including a representation of a computing environment.
- Initiating Mitigation Action: Initiating a mitigation action based on a result of the executed query.
The following derivatives expand upon these core functionalities across various technical axes.
1. Material & Component Substitution
Derivative 1.1: Graph Database Engine Substitution
Enabling Description: Instead of Neo4j®, the security database utilizes an Apache TinkerPop™ compatible graph database (e.g., JanusGraph, Amazon Neptune, Azure Cosmos DB Graph API) for storing the representation of the computing environment. The query generation component is adapted to produce Gremlin queries, and the query execution component is configured to interact with the TinkerPop™ traversal engine. This substitution leverages an open-source graph computing framework, enabling interoperability with a wider range of graph processing tools and visualization platforms. The core LLM for query generation would be trained on Gremlin query structures and graph schema representations.
graph TD
A[Incident Input] --> B(LLM-based Scenario Mapper)
B --> C{Scenarios & Sub-scenarios}
C --> D(User Selection / LLM Sub-scenario Selector)
D --> E(LLM-based Gremlin Query Generator)
E --> F{Apache TinkerPop Compatible Graph DB}
F --> G(Query Execution Engine)
G --> H[Query Result]
H --> I(Mitigation Action Initiator)
Derivative 1.2: Distributed Ledger for Incident Input Immutability
Enabling Description: The received incident input is first committed to a permissioned blockchain (e.g., Hyperledger Fabric, Ethereum Quorum) as an immutable record. Each incident record is cryptographically linked, providing an auditable trail. The mapping and query generation components retrieve incident data from this ledger via a blockchain oracle or API. This ensures non-repudiation and integrity of the initial incident data before processing, which is crucial for forensic analysis and compliance. The distributed ledger acts as the primary input channel, with a hash of the incident data serving as the "incident input" for the LLM.
sequenceDiagram
participant S as Security Event Source
participant B as Blockchain/DLT
participant NLQP as NLQP (LLM)
participant SD as Security Database
participant MA as Mitigation Action System
S->>B: Submit Incident Input (Transaction)
B-->>S: Transaction Hash
NLQP->>B: Retrieve Incident Input (via API/Oracle)
NLQP->>NLQP: Map Incident to Scenario
NLQP->>NLQP: Generate Query (Cypher/Gremlin)
NLQP->>SD: Execute Query
SD-->>NLQP: Query Result
NLQP->>MA: Initiate Mitigation Action (with Incident Hash)
MA->>B: Record Mitigation Action (Transaction)
Derivative 1.3: Quantum-Inspired Annealing for Sub-Scenario Selection
Enabling Description: Instead of a pure LLM for sub-scenario selection, a quantum-inspired annealing algorithm (e.g., D-Wave's quantum annealing, Fujitsu's Digital Annealer) is employed. The problem of selecting the optimal sub-scenario from a vast number of possibilities, given the incident input and current security context, is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The annealing process identifies the sub-scenario with the highest correlation score (lowest energy state) to the incident's characteristics and potential impact, providing a more globally optimal selection than sequential LLM inference. The LLM would generate the QUBO problem parameters from the incident input and scenario context.
graph TD
A[Incident Input & Scenario] --> B(QUBO Problem Formulator (LLM))
B --> C{Quantum-Inspired Annealer}
C --> D(Optimal Sub-scenario Selection)
D --> E(LLM-based Query Generator)
E --> F[Security Database]
Derivative 1.4: In-Memory Graph Database for High-Speed Query Execution
Enabling Description: For rapid incident response, the security database leverages an in-memory graph database (e.g., RedisGraph, Apache Ignite, SAP HANA Graph). This allows for extremely low-latency query execution, critical for time-sensitive mitigation actions. The representation of the computing environment is continuously streamed or batch-loaded into the in-memory store from persistent storage, ensuring that queries operate on the most current data with minimal overhead. The LLM-generated queries are optimized for in-memory traversal and pattern matching.
graph TD
A[Incident Input] --> B(LLM Scenario & Query Gen)
B --> C(Optimized Query)
C --> D[In-Memory Graph Database]
D --> E(High-Speed Query Execution)
E --> F[Rapid Query Result]
F --> G(Mitigation Action)
Derivative 1.5: Rust-based High-Performance Query Engine
Enabling Description: The query execution engine, traditionally implemented in languages like Java or Python, is re-engineered using Rust. Rust's memory safety and performance characteristics allow for the development of a highly efficient and secure query execution engine that can handle complex graph traversals and large datasets with minimal overhead. This provides deterministic high performance, reducing the latency between query generation and result availability, especially beneficial for real-time cybersecurity threat analysis.
graph TD
A[LLM Query Output] --> B(Rust-based Query Engine)
B --> C[Security Database]
C --> D(Data Retrieval)
B -- Performance Metrics --> E(Monitoring System)
D --> F[Query Result]
F --> G(Mitigation Action Initiator)
2. Operational Parameter Expansion
Derivative 2.1: Hyper-Scale Enterprise Cloud Environment
Enabling Description: The system operates within a hyper-scale enterprise cloud environment comprising hundreds of thousands of interconnected resources (virtual machines, containers, serverless functions, storage buckets, network devices) across multiple cloud providers (AWS, Azure, GCP, Alibaba Cloud) and on-premises data centers. The security database is sharded and replicated globally, utilizing a distributed graph database architecture (e.g., Dgraph, TigerGraph) to handle petabytes of security data and trillions of relationships. Incident input processing scales dynamically using serverless functions (e.g., AWS Lambda, Azure Functions) to accommodate bursts of tens of thousands of alerts per second. Query execution is parallelized across distributed database instances.
flowchart LR
A[Global Incident Streams] --> B(Distributed Ingestion & LLM Gateway)
B --> C{Cloud Provider 1 (AWS)}
B --> D{Cloud Provider 2 (Azure)}
B --> E{On-Premise DC}
C -- Data Schema Standardization --> F[Distributed Graph Database]
D -- Data Schema Standardization --> F
E -- Data Schema Standardization --> F
F --> G(Parallel Query Execution)
G --> H[Global Mitigation Orchestration]
Derivative 2.2: Nano-Scale IoT Edge Device Security
Enabling Description: The system is miniaturized to operate on nano-scale IoT edge devices with extremely limited computational resources and intermittent connectivity. The LLM for scenario mapping and query generation is replaced with a highly optimized, quantized small language model (SLM) or a rule-based expert system, specifically trained for common IoT attack patterns and device-specific telemetry. The "security database" is a local, highly compressed, embedded graph database (e.g., SQLite with a graph extension) storing a minimal representation of the device's immediate environment and trusted configurations. Mitigation actions are pre-defined, low-power operations, such as temporary network isolation, firmware rollback, or reporting a critical alert to a central management system.
graph TD
A[IoT Sensor Data / Device Anomaly] --> B(Edge Pre-processor)
B --> C(Quantized SLM / Rule Engine)
C --> D{Local Embedded Graph DB}
C --> E(Local Query Generator)
E --> D
D --> F[Local Mitigation Trigger]
F --> G(Limited Action / Report to Cloud)
Derivative 2.3: Ultra-Low Latency Financial Trading Systems Protection
Enabling Description: The system is optimized for ultra-low latency detection and response within high-frequency financial trading environments, where decisions must occur in microseconds. Incident inputs are real-time market data anomalies, suspicious transaction patterns, or unauthorized access attempts. Scenario mapping and query generation are performed by specialized hardware (e.g., FPGAs, ASICs) pre-loaded with highly optimized models for specific threat scenarios and pre-compiled query templates. The security database is an in-memory transactional database (e.g., KDB+, VoltDB) integrated directly into the trading infrastructure, enabling query execution within nanoseconds. Mitigation actions are automated circuit breakers, immediate transaction reversals, or temporary suspension of trading privileges.
sequenceDiagram
participant MD as Market Data Feed
participant HWA as Hardware Accelerator (FPGA/ASIC)
participant IMDB as In-Memory DB (KDB+)
participant TCA as Trading Control System
MD->>HWA: Real-time Incident Data (<1µs)
HWA->>HWA: Scenario Map & Query Gen (<1µs)
HWA->>IMDB: Execute Query (<1µs)
IMDB-->>HWA: Query Result (<1µs)
HWA->>TCA: Initiate Mitigation Action (<1µs)
TCA-->>MD: Circuit Breaker / Action
Derivative 2.4: Extreme Cold Climate Industrial Control Systems
Enabling Description: The system is designed for Industrial Control Systems (ICS) operating in extreme cold (e.g., Arctic oil rigs, remote mining facilities) where hardware resilience and minimal power consumption are paramount. Incident inputs are derived from SCADA system telemetry, sensor readings, and network flow data. The NLQP and LLM components are hosted centrally in a hardened, temperate data center, processing data transmitted over low-bandwidth, high-latency satellite links. The local edge component uses robust, low-power processing units and a secure, offline, read-only security database replica for initial query execution. Critical mitigation actions are designed for fail-safe states, such as shutting down equipment or isolating network segments, with central approval for re-enabling.
graph TD
A[SCADA/Sensor Data (Local)] --> B(Local Processing Unit)
B --> C(Offline Security DB Replica)
B --> D(Low-Bandwidth Uplink)
D --> E(Central LLQP & Security DB)
E --> F(Query & Mitigation Decision)
F --> D
D --> G(Local Mitigation Executor)
G --> H[ICS Equipment Control]
3. Cross-Domain Application
Derivative 3.1: Supply Chain Risk Management
Enabling Description: The incident input is a supply chain event (e.g., geopolitical disruption, natural disaster, supplier bankruptcy, quality control failure). The system maps this input to supply chain "risk scenarios" (e.g., "Component X shortage," "Logistics Route Y compromised," "Supplier Z financial instability"), each with sub-scenarios like "Identify alternative suppliers," "Evaluate impact on production," "Trigger contract clauses." The security database represents the global supply chain network as a graph (suppliers, manufacturers, logistics, materials, contracts, geopolitical risks). Queries identify affected components, critical dependencies, and potential bottlenecks. Mitigation actions include rerouting shipments, activating contingency contracts, or notifying affected customers.
erDiagram
SUPPLIER ||--o{ COMPONENT : supplies
COMPONENT ||--o{ PRODUCT : used_in
PRODUCT ||--o{ CUSTOMER : delivered_to
RISK ||--o{ SUPPLIER : affects
RISK ||--o{ LOGISTICS : affects
LOGISTICS ||--o{ COMPONENT : transports
EVENT ||--o{ RISK : triggers
Derivative 3.2: Financial Fraud Detection and Response
Enabling Description: The incident input is a suspicious financial transaction or a pattern of unusual account activity. The system maps this input to "fraud scenarios" (e.g., "Account takeover," "Money laundering," "Insider trading attempt"), each with sub-scenarios like "Verify user identity," "Analyze transaction history," "Assess counterparty risk." The security database models financial entities as a graph (accounts, transactions, customers, merchants, devices, IP addresses, known fraud rings). Queries identify linked fraudulent entities, transaction anomalies, and potential financial exposure. Mitigation actions include freezing accounts, blocking transactions, or flagging for human review by a fraud analyst.
graph TD
A[Suspicious Transaction] --> B(LLM Fraud Scenario Mapper)
B --> C{Fraud Scenarios & Sub-scenarios}
C --> D(LLM Query Generator)
D --> E[Financial Transaction Graph DB]
E --> F(Fraud Detection Engine)
F --> G[Query Result (Fraud Score, Linked Accounts)]
G --> H(Automated Mitigation / Human Review)
Derivative 3.3: Agricultural Pest and Disease Management
Enabling Description: The incident input is a field observation (e.g., crop discoloration, insect infestation report, weather anomaly). The system maps this input to "agricultural threat scenarios" (e.g., "Fungal infection X," "Insect pest Y outbreak," "Drought condition"), with sub-scenarios such as "Identify affected area," "Recommend pesticide/treatment," "Predict spread." The "security database" contains a spatiotemporal graph representation of agricultural land (crop types, soil conditions, weather patterns, historical pest data, treatment efficacy, adjacent fields). Queries identify at-risk crops, optimal treatment strategies, and predict disease propagation. Mitigation actions include automated drone-based spraying, localized irrigation adjustments, or generating alerts for agricultural extension workers.
graph TD
A[Field Observation (Image/Sensor/Report)] --> B(LLM Agricultural Threat Mapper)
B --> C{Threat Scenarios & Sub-scenarios}
C --> D(LLM Query Generator)
D --> E[Agricultural Spatiotemporal Graph DB]
E --> F(Query Execution)
F --> G[Treatment Recommendation / Spread Prediction]
G --> H(Automated Farm Equipment / Agronomist Alert)
Derivative 3.4: Healthcare Patient Safety & Adverse Event Response
Enabling Description: The incident input is an adverse drug event report, a medication error, a patient fall, or an unusual vital sign trend. The system maps this to "patient safety scenarios" (e.g., "Medication interaction," "Hospital-acquired infection," "Equipment malfunction"), with sub-scenarios like "Identify contributing factors," "Assess patient risk," "Recommend corrective action." The "security database" is a clinical graph database (e.g., electronic health records, medication data, patient history, device logs, hospital protocols). Queries identify medication interactions, correlations between staff shifts and errors, or equipment maintenance records. Mitigation actions include alerting healthcare providers, adjusting medication dosages, or triggering a root cause analysis protocol.
classDiagram
class Patient {
+patientID
+age
+diagnoses
+medications
}
class Medication {
+medicationID
+name
+dosage
+interactions
}
class Event {
+eventID
+type
+timestamp
+description
}
class Provider {
+providerID
+specialty
}
Patient "1"--"*" Medication : takes
Patient "1"--"*" Event : experiences
Event "1"--"*" Medication : related_to
Event "1"--"*" Provider : involves
4. Integration with Emerging Tech
Derivative 4.1: AI-Driven Multi-Agent Optimization of Mitigation
Enabling Description: Upon receiving the query result, a multi-agent system (MAS) composed of specialized AI agents is invoked. Each agent (e.g., a "Network Isolation Agent," a "Patching Agent," a "User Suspension Agent") evaluates the query result and potential mitigation actions within its domain, considering factors like operational impact, cost, and urgency. A central "Orchestration Agent" uses an AI planner (e.g., STRIPS, PDDL-based planner, Reinforcement Learning agent) to determine the optimal sequence and combination of mitigation actions, resolving conflicts and ensuring minimal disruption while maximizing security posture. The LLM then generates the specific API calls or commands for these optimized actions.
graph TD
A[Query Result] --> B(Orchestration Agent)
B --> C(Network Isolation Agent)
B --> D(Patching Agent)
B --> E(User Suspension Agent)
C --> F[Network Firewall API]
D --> G[Patch Management System]
E --> H[Identity Management System]
B -- Optimized Action Plan --> LLM(LLM for Command Generation)
LLM --> F & G & H
Derivative 4.2: Real-time Incident Input from IoT Sensor Network
Enabling Description: The incident input is primarily sourced from a vast network of IoT security sensors (e.g., environmental anomaly detectors, physical access control sensors, network traffic sniffers embedded in industrial machinery). These sensors continuously stream telemetry data. An edge-based anomaly detection AI (e.g., an unsupervised learning model like Isolation Forest or autoencoders) preprocesses this raw data, identifying deviations from normal behavior. These anomalies are then packaged as incident inputs for the central NLQP, allowing for immediate response to physical and cyber-physical security events, reducing reliance on human observation or traditional log analysis.
flowchart LR
S1[IoT Sensor 1] --Telemetry--> ED(Edge Device Gateway)
S2[IoT Sensor 2] --Telemetry--> ED
S_N[...] --Telemetry--> ED
ED --Anomaly Detection AI--> AD(Anomaly Data Stream)
AD --> I[Incident Input (NLQP)]
I --> LLM_S(LLM Scenario Mapper)
LLM_S --> Q(LLM Query Generator)
Q --> DB[Security Database]
DB --> M[Mitigation Actions]
Derivative 4.3: Blockchain for Immutable Audit Trails and Smart Contract Mitigation
Enabling Description: Beyond immutable incident input (Derivative 1.2), the entire incident response workflow, including scenario mapping decisions, generated queries, query results, and executed mitigation actions, is recorded on a blockchain. Each step is timestamped and signed, creating an undeniable audit trail for compliance and post-incident analysis. Furthermore, certain pre-approved mitigation actions are implemented as smart contracts on the blockchain. When specific conditions from the query result are met, these smart contracts are automatically executed (e.g., revoking access, isolating a resource), ensuring transparent, self-executing, and tamper-proof responses.
sequenceDiagram
participant NLQP as NLQP (LLM)
participant SD as Security Database
participant BC as Blockchain (DLT)
participant SC as Smart Contract
participant MA as Mitigation Action System
NLQP->>BC: Record Incident Input Hash
NLQP->>NLQP: Map Incident to Scenario
NLQP->>BC: Record Scenario Mapping Decision
NLQP->>NLQP: Generate Query
NLQP->>BC: Record Generated Query
NLQP->>SD: Execute Query
SD-->>NLQP: Query Result
NLQP->>BC: Record Query Result
NLQP->>SC: Evaluate Conditions for Smart Contract
alt Smart Contract Conditions Met
SC->>MA: Execute Automated Mitigation (e.g., Revoke Access)
else No Smart Contract Match
NLQP->>MA: Initiate Traditional Mitigation Action
end
MA->>BC: Record Mitigation Action Execution
Derivative 4.4: Federated Learning for LLM Training on Diverse Security Datasets
Enabling Description: The LLM used for scenario mapping and query generation is trained using a federated learning approach. This allows multiple organizations to collaboratively train a shared LLM model on their proprietary security incident data (e.g., alerts, threat intelligence, database schemas, query-answer pairs) without centralizing the raw data. Each organization trains the LLM locally on its data, and only model updates (weights or gradients) are aggregated, preserving data privacy and enabling the LLM to learn from a more diverse and comprehensive set of real-world cybersecurity incidents and environments, leading to more robust scenario mapping and query generation.
graph TD
A[Client 1 Local Data] --> B(Client 1 LLM Training)
C[Client 2 Local Data] --> D(Client 2 LLM Training)
E[Client N Local Data] --> F(Client N LLM Training)
B --> G(Secure Aggregator)
D --> G
F --> G
G --Aggregated Model Updates--> H(Global LLM Model)
H --> B & D & F
H --> I[Production NLQP (LLM)]
5. The "Inverse" or Failure Mode
Derivative 5.1: Fail-Safe Read-Only Investigation Mode
Enabling Description: In the event of an internal system anomaly, critical resource exhaustion (e.g., database overload, LLM inference saturation), or a detected attempt at malicious manipulation of the incident response system itself, the system automatically transitions into a "fail-safe read-only investigation mode." In this mode, all mitigation action initiation is disabled, and the system exclusively generates queries and retrieves results for human review, without executing any changes in the computing environment. The LLM may also generate diagnostic queries about its own operational status or the integrity of the security database. This prevents unintended or malicious automated actions during compromised or unstable states, prioritizing safe observation and analysis.
stateDiagram
[*] --> Operational
Operational --> FailSafeReadonly: System Anomaly Detected
Operational --> FailSafeReadonly: Resource Exhaustion
Operational --> FailSafeReadonly: Self-Integrity Check Failure
FailSafeReadonly --> Operational: Anomaly Resolved
FailSafeReadonly --> FailSafeReadonly: Diagnostic Querying
state FailSafeReadonly {
MitigationDisabled --> HumanReviewRequired
HumanReviewRequired --> DiagnosticQuerying
}
Derivative 5.2: Low-Power/Limited-Functionality Standby Mode
Enabling Description: For environments with constrained power (e.g., battery-powered edge devices, disaster recovery scenarios) or during periods of low threat activity, the system can enter a "low-power/limited-functionality standby mode." In this mode, the LLM for scenario mapping operates with a reduced model size or in a less frequent batch processing cycle. Query generation uses only pre-compiled templates, limiting the complexity of generated queries. Mitigation actions are restricted to low-impact notifications or logging, deferring resource-intensive actions or complex analyses to a fully operational state or human intervention. The system prioritizes essential monitoring over comprehensive response.
graph TD
A[Incident Input] --> B{Power/Threat Level?}
B -- High/Normal --> C(Full Operational Mode)
B -- Low --> D(Low-Power Standby)
D --> E(Reduced LLM Inference)
D --> F(Pre-compiled Query Templates)
D --> G(Limited Mitigation: Notify/Log)
C --> H[Full Analysis & Mitigation]
Derivative 5.3: Human-in-the-Loop Override with Confidence Scoring
Enabling Description: For every proposed mitigation action, the system generates a confidence score based on the LLM's certainty in scenario mapping, query accuracy, and the predicted efficacy of the mitigation. If this confidence score falls below a predefined threshold, or if the action is classified as "high-impact," the system automatically defers to a "human-in-the-loop override." This involves rendering a detailed explanation of the incident, the proposed mitigation, and the underlying reasoning (generated by the LLM) on a graphical user interface, requiring explicit human approval before execution. This prevents potentially erroneous or over-aggressive automated responses in ambiguous or critical situations.
sequenceDiagram
participant LLM as LLM (NLQP)
participant M as Mitigation Initiator
participant H as Human Operator
participant E as Execution System
LLM->>M: Propose Mitigation Action & Confidence Score
alt Confidence Score < Threshold OR High-Impact Action
M->>H: Request Human Approval (with explanation)
H->>M: Approve / Reject
alt Approved
M->>E: Initiate Mitigation Action
else Rejected
M->>H: Provide Further Context / Adjust
end
else Confidence Score >= Threshold AND Low-Impact Action
M->>E: Initiate Mitigation Action Automatically
end
Derivative 5.4: Adaptive Degradation for Resource Overload
Enabling Description: When the system detects an impending or actual resource overload (e.g., CPU, memory, network bandwidth saturation), it initiates an "adaptive degradation" protocol. This dynamically adjusts operational parameters to maintain core functionality. For instance, the LLM's output precision for scenario mapping might be reduced (e.g., favoring broader categories over specific sub-scenarios), the frequency of database query execution could be lowered, or certain non-critical data enrichments might be skipped. The system prioritizes critical incident detection and essential mitigation actions, gracefully degrading less vital functions to ensure system stability and responsiveness during peak loads.
stateDiagram
[*] --> Full_Functionality
Full_Functionality --> Degraded_Mode_1: Resource_Utilization_High
Degraded_Mode_1 --> Degraded_Mode_2: Resource_Utilization_Critical
Degraded_Mode_2 --> Full_Functionality: Resource_Recovery
state Full_Functionality {
LLM_High_Precision
Realtime_Queries
Full_Mitigation_Scope
}
state Degraded_Mode_1 {
LLM_Reduced_Precision
Reduced_Query_Frequency
Prioritized_Mitigation
}
state Degraded_Mode_2 {
LLM_Fallback_Templates
Essential_Queries_Only
Critical_Mitigation_Only
}
Combination Prior Art Scenarios
Here are three scenarios where US12001549's core technology can be combined with existing open-source standards to demonstrate prior art.
Combination Prior Art 1: MITRE ATT&CK and Open Cyber Security Alliance (OCSF)
Scenario Description: The system disclosed in US12001549 for "mapping a received incident input into a scenario" is implemented by integrating with the MITRE ATT&CK framework (an open-source knowledge base). The incident input, which could be an alert or a natural language query describing a cyber event, is analyzed by the LLM to classify it against specific ATT&CK tactics, techniques, and procedures (TTPs). These TTPs form the "scenarios" and "sub-scenarios" within the patent's framework. The "security database" representing the computing environment stores its security findings and events structured according to the Open Cyber Security Alliance (OCSF) schema (an open-source standard for security data). This combination enables the LLM-generated queries to leverage a standardized, vendor-agnostic representation of security data for more effective threat hunting and incident correlation.
graph TD
A[Incident Input (Raw Alert/NL Query)] --> B(LLM-based ATT&CK Mapper)
B --Classified TTPs--> C{ATT&CK Scenarios/Sub-scenarios}
C --> D(LLM Query Generator)
D --OCSF-compliant Query--> E[Security Database (OCSF Schema)]
E --OCSF Result--> F(Mitigation Action Initiator)
Combination Prior Art 2: OpenC2 for Standardized Mitigation Actions
Scenario Description: After "executing the query on a security database" and determining a "mitigation action" based on the result, the system initiates this action using the OpenC2 (Open Command and Control) standard (an open-source OASIS standard for command and control of cyber defense components). The LLM or a specialized action generation module translates the identified mitigation (e.g., "isolate host," "block IP," "patch vulnerability") into an OpenC2 command structure (e.g., action: isolate, target: {type: ip_address, specifiers: {ip_addr: "192.168.1.100"}}). This OpenC2 command is then sent to a compliant actuator in the computing environment. This standardizes the execution of mitigation actions across heterogeneous security tools, making the response more interoperable and efficient.
graph TD
A[Query Result] --> B(LLM-based OpenC2 Command Generator)
B --OpenC2 Command--> C[OpenC2 Actuator (Firewall/EDR/IAM)]
C --> D[Mitigation Action Executed]
Combination Prior Art 3: GraphQL for Flexible Data Schema and Querying
Scenario Description: The "security database" that includes a representation of the computing environment exposes its data via a GraphQL API (an open-source query language and runtime for APIs). The LLM-generated queries, instead of being directly tied to a specific database language like Cypher or Gremlin, are translated into GraphQL queries. This allows the LLM to construct more flexible and precise data requests, fetching exactly the information needed from the underlying graph or relational database, regardless of its specific implementation. The GraphQL schema defines the "data schema" the LLM can reference for accurate query construction. This provides an abstraction layer over the database, improving adaptability and maintainability of the query generation process.
graph TD
A[Incident Input] --> B(LLM Scenario & Query Gen)
B --GraphQL Query--> C[GraphQL API Layer]
C --> D[Security Database (Underlying)]
D --> C
C --GraphQL Result--> E[Query Result]
E --> F(Mitigation Action Initiator)
Generated 5/16/2026, 12:48:49 PM