Derivative works

Defensive Disclosure: Derivative Works for US Patent 11936693

This document outlines derivative works and technical disclosures for US Patent 11936693, "System and method for applying a policy on a network path." The objective is to establish prior art for potential future incremental improvements by competitors, rendering such advancements obvious or non-novel. The derivations are structured around the core functional steps of US11936693's independent claims:

1. Selecting a reachable resource (cloud object in a cloud computing environment with an external network access path).
2. Actively inspecting the network path (to determine external accessibility).
3. Applying a policy (on the accessible network path, with a conditional rule).
4. Initiating a mitigation action (if the conditional rule is not met).
5. Applying the policy on another network path (if the conditional rule is met).

These derivations expand upon the original invention across various technical axes, providing detailed, enabling descriptions.

1. Material & Component Substitution

1.1. Derivative: Hardware-Accelerated Active Inspection with Programmable Logic Arrays

Enabling Description:
This derivative implements the active inspection and policy application logic using Field-Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs) within a dedicated network security appliance or as a PCI Express (PCIe) accelerator card within a server. The "active inspector" (e.g., active inspector 125 of FIG. 1) functionality is offloaded to a custom hardware logic block. The processing circuitry (610) would be a heterogeneous architecture comprising a general-purpose processor for control plane logic (e.g., resource selection, policy rule management, report generation) and an FPGA/ASIC for data plane operations (packet generation, transmission, response parsing, real-time policy evaluation against incoming packets). Network paths are analyzed by the FPGA/ASIC generating custom packet sequences (e.g., malformed HTTP requests, specific SSH handshakes, port probes) at wire-speed. The conditional rules of the policy are translated into hardware-level finite state machines (FSMs) or lookup tables (LUTs) within the FPGA/ASIC, enabling nanosecond-latency policy enforcement decisions on detected network path accessibility. Mitigation actions (e.g., signaling a firewall to block an IP, resetting a connection) are triggered directly by the hardware logic via dedicated control signals or high-speed inter-process communication (IPC) to network enforcement points. The security graph (122) remains a software component, feeding path parameters and policy rules to the hardware through a defined API.

graph TD
    A[Cloud Security Platform Control Plane - GPP] --> B(Resource Selector)
    B --> C{Security Graph DB}
    C --> D(Policy Engine)
    D --> E[Hardware Transcoder: Policy Rules to H/W Logic]
    E --> F[FPGA/ASIC Data Plane: Active Inspector & Policy Enforcer]
    F -- Generate Access Instruction Packets --> G[External Network Interface Card]
    G -- Network Path Probes --> H[Reachable Cloud Resource in Cloud Environment]
    H -- Network Response --> G
    G -- Parse Response Packets --> F
    F -- Policy Evaluation (H/W FSM/LUT) --> I{Conditional Rule Met?}
    I -- No --> J[Mitigation Action Trigger (H/W Signal)]
    I -- Yes --> K[Apply Policy to Another Path / Log Compliance]
    J --> L[Network Enforcement Point (e.g., Firewall)]
    K --> B

1.2. Derivative: Quantum-Resistant Cryptographic Components for Inspection Instructions

Enabling Description:
This derivative focuses on the security of the active inspection process itself, specifically the "access instruction" (S320, S330) and credentials during inspection of the network path (S910). For cloud environments requiring utmost security, such as governmental or critical infrastructure, the communication channel between the active inspector and the reachable resource, especially when providing credentials or executing sensitive access instructions, is secured using quantum-resistant cryptographic algorithms. The processing circuitry (610) incorporates specialized quantum-safe cryptographic modules (e.g., hardware security modules based on lattice-based cryptography like CRYSTALS-Dilithium for digital signatures and CRYSTALS-Kyber for key encapsulation, or hash-based signatures like XMSS/LM-OTS). These modules are used to establish secure communication tunnels for active inspection probes, protecting against future quantum computing attacks on current asymmetric encryption standards. The access instructions (e.g., "ssh user@10.0.0.256:22") are encapsulated within these quantum-resistant secure channels. The memory (620) stores pre-shared quantum-resistant keys or certificates, and the network interface (640) is optimized for efficient processing of larger quantum-safe cryptographic primitives without introducing significant latency during active inspection attempts.

sequenceDiagram
    participant AI as Active Inspector (Q-Resistant Crypto Module)
    participant RES as Reachable Cloud Resource
    AI->>RES: Initiate Q-Resistant Handshake (e.g., Kyber KEM, Dilithium Signature)
    activate RES
    RES-->>AI: Q-Resistant Key Exchange & Auth
    deactivate RES
    AI->>RES: Establish Q-Resistant Secure Channel
    AI->>RES: Send Encrypted Access Instruction (e.g., "ssh user@...")
    activate RES
    RES-->>AI: Encrypted Response (e.g., ACK, Error)
    deactivate RES
    AI->>AI: Determine Accessibility & Apply Policy

2. Operational Parameter Expansion

2.1. Derivative: Hyperscale Multi-Cloud, Multi-Tenant Active Inspection

Enabling Description:
This derivative scales the active inspection system to operate across millions of cloud objects distributed across hundreds of distinct cloud computing environments (e.g., AWS, Azure, GCP, Alibaba Cloud, private OpenStack instances) belonging to thousands of distinct tenants (organizations). The "active inspector" (125) is deployed as a globally distributed, elastic cluster of microservices, each optimized for specific cloud provider APIs and network topologies. Resource selection (S905) involves federated queries across multiple security graph databases (122), which are sharded and replicated for high availability and low latency. Active inspection (S910) is orchestrated by a central scheduler that dynamically allocates inspection tasks to regional active inspector instances based on the geographical proximity to the target reachable resource, network latency, and current load. The system handles billions of network paths concurrently. Policy application (S915) is managed by a distributed policy engine (116) that can enforce conditional rules with varying scopes (global, regional, per-tenant, per-resource) and granularities (IP, port, protocol, application layer). Mitigation actions (S950) are triggered via API calls to cloud provider security services (e.g., Security Groups, Network ACLs, WAFs) or by issuing commands to tenant-specific orchestration platforms, handling concurrency and potential rate limiting across all connected environments.

graph TD
    A[Global Orchestrator & Scheduler] --> B[Regional Active Inspector 1 (AWS)]
    A --> C[Regional Active Inspector 2 (Azure)]
    A --> D[Regional Active Inspector 3 (GCP)]
    B --> E[Cloud Environment A: Tenant 1 Resources]
    C --> F[Cloud Environment B: Tenant 2 Resources]
    D --> G[Cloud Environment C: Tenant 3 Resources]
    E -- Network Paths --> B
    F -- Network Paths --> C
    G -- Network Paths --> D
    B -- Inspection Results --> A
    C -- Inspection Results --> A
    D -- Inspection Results --> A
    A --> H[Federated Security Graph DBs]
    H --> A
    A --> I[Distributed Policy Engine]
    I -- Apply Policy / Trigger Mitigation --> J[Cloud Provider Security APIs]

2.2. Derivative: Real-time, Millisecond-Latency Active Inspection for High-Frequency Trading Networks

Enabling Description:
This derivative targets extreme low-latency environments, specifically high-frequency trading (HFT) networks in cloud or hybrid deployments. The core method steps are adapted for real-time monitoring and policy enforcement within a sub-millisecond timeframe. Resource selection (S905) is pre-computed and cached for critical trading infrastructure components. Active inspection (S910) involves custom-built, ultra-low-latency network probes implemented on specialized Network Interface Cards (NICs) with FPGA offload or SmartNICs capable of generating and analyzing network packets within microseconds. These probes monitor for deviations in expected network path behavior, such as unexpected open ports or protocol responses, with latency requirements for detection and response typically under 100 microseconds. Policies (S915) are defined with strict real-time conditional rules (e.g., "any port other than 443 and 8070 accessible via external network from specific IP range triggers immediate blocking"). Mitigation actions (S950) are hardware-accelerated, directly interfacing with programmable network switches or firewalls to apply packet filtering rules (e.g., access control list (ACL) updates) within single-digit microseconds, or to trigger circuit breakers on specific trading application instances, thus preventing potential financial system manipulation or data exfiltration.

stateDiagram-v2
    [*] --> Initialized
    Initialized --> Pre_Compute_Paths: System Start
    Pre_Compute_Paths --> Monitor_Critical_Paths: Paths Cached
    Monitor_Critical_Paths --> Generate_Probe: Timer/Event Trigger
    Generate_Probe --> Send_Probe_HW: <10us
    Send_Probe_HW --> Receive_Response_HW: <10us
    Receive_Response_HW --> Evaluate_Policy_HW: <50us
    Evaluate_Policy_HW --> Mitigation_Action_HW: Rule Violated
    Evaluate_Policy_HW --> Monitor_Critical_Paths: Rule Met
    Mitigation_Action_HW --> Apply_ACL_HW: <10us
    Apply_ACL_HW --> Monitor_Critical_Paths: Action Applied

3. Cross-Domain Application

3.1. Derivative: Policy Application on Network Paths in Space-Based Satellite Constellations

Enabling Description:
This derivative applies the patent's core mechanism to the domain of space-based satellite constellations, specifically for maintaining the security of inter-satellite links and ground-to-satellite communication paths.

1. Selecting a reachable resource: Identifies individual satellites or specific payload modules on a satellite (acting as "cloud objects") within a constellation. A "network path" could be an uplink/downlink to a ground station, or an inter-satellite optical/RF link. The "external network" would be the ground control segment or other non-trusted satellite networks.
2. Actively inspecting the network path: Ground-based active inspectors or on-board autonomous active inspector modules send specially crafted telemetry or command packets across the identified communication paths. This checks for unexpected open command channels, vulnerable diagnostic ports, or deviations in expected cryptographic handshakes for authorized links. Due to high latency and potential intermittent connectivity, inspection might involve asynchronous challenges and responses.
3. Applying a policy: A policy engine (either ground-based or distributed across a subset of command satellites) applies conditional rules such as "inter-satellite links must use quantum-resistant encryption protocols for data plane," or "only authorized ground stations with specific orbital mechanics prediction parameters can establish a command link on port X."
4. Initiating a mitigation action: If a policy is violated (e.g., an unauthorized attempt to access a diagnostic port is detected), a mitigation action could involve commanding the satellite to temporarily close the compromised port, re-keying cryptographic modules for the affected link, initiating an evasive maneuver, or isolating the suspicious payload module.
5. Applying the policy on another network path: If an inter-satellite link is compliant, the policy engine proceeds to validate the next link in the chain or a different ground-to-satellite path.

graph TD
    A[Ground Control Segment] --> B(Active Inspector Module - Ground)
    B --> C{Satellite Constellation: Sat 1, Sat 2, ..., Sat N}
    C -- Uplink/Downlink/Inter-Sat Link --> B
    B --> D[Policy Engine - Ground]
    D -- Commands --> C
    C -- Telemetry/Status --> D
    D --> E{Policy Rule Met?}
    E -- No --> F[Mitigation Action (e.g., Re-key Link, Port Closure)]
    E -- Yes --> G[Next Path Evaluation]

3.2. Derivative: Policy Application on Network Paths in Industrial Control Systems (ICS) / Operational Technology (OT) Networks

Enabling Description:
This derivative applies the patent's principles to industrial control systems (ICS) and operational technology (OT) networks, such as those found in manufacturing plants, energy grids, or water treatment facilities. The focus is on securing communication paths between Programmable Logic Controllers (PLCs), Remote Terminal Units (RTUs), Human-Machine Interfaces (HMIs), and supervisory control and data acquisition (SCADA) systems.

1. Selecting a reachable resource: Identifies critical ICS/OT devices (e.g., specific PLCs controlling a turbine, RTUs managing a pipeline valve) as "cloud objects," even if physically on-premises. A "network path" could be a Modbus TCP/IP, Ethernet/IP, or OPC UA communication channel between a SCADA server and a PLC, or a maintenance laptop connected to an engineering workstation. The "external network" is typically the corporate IT network attempting to reach the OT segment for data collection or remote management, or even an internet connection for cloud-based SCADA.
2. Actively inspecting the network path: A specialized, non-intrusive active inspector (e.g., a "bump-in-the-wire" device or an out-of-band monitoring appliance) generates controlled, protocol-aware probes (e.g., Modbus read requests for specific registers, OPC UA discovery queries) to validate that only expected ports are open and that devices respond as anticipated on known communication paths. These probes are designed to be safe for OT environments, avoiding commands that could disrupt operations.
3. Applying a policy: A policy engine (potentially air-gapped from the IT network) applies conditional rules like "no internet-facing path to PLC X is permitted on any port," or "Modbus TCP communication on path Y must only originate from SCADA server Z and target function code 03 (read holding registers)."
4. Initiating a mitigation action: If a policy is violated (e.g., an unknown IP attempts to initiate an Ethernet/IP session with an HMI), mitigation could involve segmenting the network using an industrial firewall, generating an alert to the plant operator, or temporarily blocking the suspicious source IP at the network edge, ensuring process integrity and safety.
5. Applying the policy on another network path: If the policy for one critical data acquisition path is compliant, the system moves to inspect the control path for another critical asset.

flowchart LR
    A[SCADA Server] -- Network Path (e.g., OPC UA) --> B[PLC 1]
    A -- Network Path (e.g., Modbus TCP) --> C[RTU 1]
    X[External IT Network] -- Network Path (e.g., SSH, RDP) --> D[Engineering Workstation]
    D -- Network Path (e.g., Ethernet/IP) --> E[HMI]
    
    SubGraph Active Inspection & Policy Enforcement
        AI[OT Active Inspector]
        PE[OT Policy Engine]
    End
    
    AI -- Probes --> B
    AI -- Probes --> C
    AI -- Probes --> E
    PE -- Rules --> AI
    AI -- Policy Violation --> PE
    PE -- Mitigation Action --> F[Industrial Firewall/Segmentation]
    PE -- Policy Compliance --> G[Next Path]

3.3. Derivative: Policy Application on Network Paths for Medical Devices and Healthcare IoT

Enabling Description:
This derivative extends the patent's methodology to the critical domain of medical devices and Healthcare Internet of Things (HIoT) within a hospital or clinical setting. This includes devices like infusion pumps, patient monitors, MRI machines, and wearable sensors.

1. Selecting a reachable resource: Identifies individual medical devices (e.g., "smart" infusion pump, networked PACS server, patient vital sign monitor) as "cloud objects," even if residing on a local network. A "network path" could be a Wi-Fi connection from a patient monitor to a central nursing station, an Ethernet link from an MRI machine to an image archive, or a Bluetooth Low Energy (BLE) link from a wearable sensor to a gateway. The "external network" includes the hospital's administrative network, remote diagnostic VPNs, or the public internet if devices are cloud-connected.
2. Actively inspecting the network path: A dedicated medical device active inspector, possibly an embedded module or an isolated network appliance, sends benign, device-specific queries (e.g., SNMP polls for device status, DICOM handshake attempts, custom API calls) over identified network paths. This verifies that devices respond appropriately, only expose expected services/ports, and adhere to predefined communication profiles, without impacting patient care.
3. Applying a policy: A specialized policy engine, integrated with the hospital's Biomedical Engineering department, applies conditional rules such as "infusion pumps must only communicate with pharmacy servers on port X for dosage updates," or "patient data streams from device Y must be encrypted using FIPS 140-2 validated modules."
4. Initiating a mitigation action: If a policy is violated (e.g., a patient monitor attempts to connect to an unapproved external IP, or an unsecured diagnostic port is found open), mitigation actions could include isolating the device to a quarantined VLAN, generating a high-priority alert for clinical and IT staff, or even disabling non-essential network functions on the device to prevent patient data compromise or device malfunction.
5. Applying the policy on another network path: Upon confirming policy adherence for one critical medical device's communication path, the system moves to actively inspect another device or a different communication pathway.

sequenceDiagram
    participant AI as Medical Device Active Inspector
    participant MD1 as Infusion Pump
    participant PS as Pharmacy Server
    participant CNS as Central Nursing Station
    participant PE as Policy Engine (BioMed/IT)
    
    AI->>MD1: Select Reachable Resource (Infusion Pump)
    AI->>MD1: Actively Inspect Network Path (e.g., to PS)
    activate MD1
    MD1-->>AI: Response (Port Status, Protocol Version)
    deactivate MD1
    AI->>PE: Report Inspection Results
    PE->>PE: Apply Policy (e.g., "Only connect to PS on port X")
    alt Conditional Rule Not Met
        PE->>MD1: Initiate Mitigation Action (Quarantine VLAN, Alert)
        MD1->>PE: Mitigation Status
    else Conditional Rule Met
        PE->>AI: Apply Policy on Another Path (e.g., MD2 to CNS)
    end

4. Integration with Emerging Tech

4.1. Derivative: AI-Driven Adaptive Active Inspection and Policy Optimization

Enabling Description:
This derivative enhances US11936693 by integrating Artificial Intelligence (AI), specifically machine learning (ML) models, to dynamically optimize active inspection strategies and policy rule sets.

1. Selecting a reachable resource: An AI-powered resource prioritization module, trained on historical vulnerability data, attack patterns, and business criticality metrics, selects reachable resources and network paths for inspection, focusing on those with the highest potential impact or likelihood of compromise.
2. Actively inspecting the network path: An ML model (e.g., a deep reinforcement learning agent) generates "access instructions" (S320) by dynamically crafting probe payloads and timings. This model learns from past inspection successes and failures, as well as real-time threat intelligence feeds, to identify optimal probing techniques that maximize detection efficacy while minimizing network impact. It adapts to evasive behaviors by target resources.
3. Applying a policy: The policy engine (116) incorporates an AI-driven policy recommender. This ML model, trained on security best practices, compliance frameworks, and an organization's observed risk profile, suggests and refines conditional rules. It can identify patterns in network path vulnerabilities and automatically generate granular policy updates, such as "block all traffic on port 23 for VM-Group-X with observed SSH brute-force attempts."
4. Initiating a mitigation action: An AI-powered automation engine determines the most effective mitigation action (S950) from a predefined playbook. Using contextual data (e.g., asset criticality, current network load, potential blast radius), it predicts the impact of various actions and recommends or automatically executes the least disruptive yet most effective response, e.g., "soft-block" an IP range vs. "hard-shutdown" a compromised resource.
5. Applying the policy on another network path: The AI continuously monitors the effectiveness of applied policies and inspection routines, iteratively optimizing both for newly identified paths and evolving threat landscapes.

flowchart TD
    A[Threat Intelligence & Historical Data] --> B(AI Training Data)
    B --> C(AI Model: Resource Prioritization)
    B --> D(AI Model: Adaptive Probe Generation)
    B --> E(AI Model: Policy Recommender)
    B --> F(AI Model: Mitigation Optimization)

    C --> G(Resource Selector)
    G --> H(Network Paths)
    D --> I(Active Inspector)
    H -- Selected Paths --> I
    I --> J(Inspection Results)
    J --> E(Policy Engine)
    E --> K(Conditional Rule)
    K -- Not Met --> F
    F --> L(Mitigation Action Initiator)
    K -- Met --> M(Policy on Another Path)
    L --> N[Cloud Environment Enforcement]
    M --> G

4.2. Derivative: IoT-Sensor Enhanced Active Inspection with Edge-Based Policy Enforcement

Enabling Description:
This derivative integrates US11936693 with IoT sensors for real-time contextual awareness and pushes active inspection capabilities closer to the network edge.

1. Selecting a reachable resource: IoT sensors deployed throughout the cloud datacenter or hybrid environment (e.g., environmental sensors, network tap sensors, physical access sensors) provide real-time telemetry about resource state, physical security, and local network anomalies. This sensor data enriches the security graph (122) and influences the selection (S905) of "reachable resources" by indicating physical compromises or unusual local network activity around a cloud object.
2. Actively inspecting the network path: Active inspection (S910) is performed not only from external networks but also by distributed "edge active inspectors" deployed on IoT gateways or specialized network security appliances physically co-located with target cloud objects. These edge inspectors perform localized, high-fidelity active probes, leveraging their proximity to detect micro-segmentation failures or side-channel attacks on local network paths, complementing external inspections.
3. Applying a policy: Policies (S915) include conditional rules derived from both external network reachability and real-time IoT sensor data. For example, a policy might state: "if a network path is accessible from the external network AND an IoT physical access sensor detects unauthorized entry to the server rack hosting the virtual machine, THEN trigger immediate isolation."
4. Initiating a mitigation action: Mitigation actions (S950) can be initiated directly by edge devices for rapid local response (e.g., an IoT gateway-based firewall applying a micro-segmentation rule in response to a local network path violation) or escalated to the central policy engine for broader enforcement.
5. Applying the policy on another network path: The system uses aggregated IoT data to identify clusters of resources or paths that require policy re-evaluation or more intensive active inspection.

graph TD
    A[External Active Inspector] --> B[Cloud Load Balancer]
    B --> C[Cloud VM / Resource]
    
    SubGraph Edge Inspection & IoT Context
        D[IoT Gateway / Edge Active Inspector]
        E[Physical Access Sensor]
        F[Network Tap Sensor]
        G[Environmental Sensor]
    End
    
    D -- Local Probes --> C
    E --> D
    F --> D
    G --> D
    
    D --> H[Central Policy Engine]
    C -- Network Response --> D
    C -- Network Response --> B
    
    H -- Policy Rules --> D
    H -- Policy Rules --> A
    
    H --> I{Conditional Rule Met? (External & IoT Context)}
    I -- No --> J[Mitigation Action (Edge/Central)]
    I -- Yes --> K[Evaluate Next Path]

4.3. Derivative: Blockchain-Anchored Active Inspection Results and Immutable Policy Auditing

Enabling Description:
This derivative integrates blockchain technology to ensure the immutability and verifiable integrity of active inspection results and policy application records.

1. Selecting a reachable resource: The selection process (S905) and the definition of network paths are transparently recorded on a private or consortium blockchain as cryptographic hashes of the security graph state, providing an auditable history of the scope of inspection.
2. Actively inspecting the network path: After active inspection (S910) determines a network path's accessibility, the inspection results (e.g., successful access, error codes, timestamps, specific probe details) are cryptographically signed by the active inspector (125) and then hashed into a transaction. This transaction is added to a distributed ledger (blockchain) as an immutable record of the path's accessibility status at that point in time. This prevents tampering with inspection reports.
3. Applying a policy: Each instance of policy application (S915), including the specific policy rule, the target network path, and the outcome of the conditional rule evaluation, generates a signed transaction that is recorded on the blockchain. This creates an unalterable audit trail of all policy decisions.
4. Initiating a mitigation action: The initiation of any mitigation action (S950) is similarly recorded as a blockchain transaction, including the action taken, the timestamp, and the enforcing entity. This provides verifiable proof of remediation efforts for compliance and forensic purposes.
5. Applying the policy on another network path: The continuous policy evaluation and application loop has its state transitions (e.g., moving to the next path) also hashed and recorded, maintaining a complete and tamper-proof log of the entire security governance process. Smart contracts on the blockchain can be used to automatically trigger audits or alerts if specific policy conditions or mitigation states are not met within defined timeframes.

sequenceDiagram
    participant AI as Active Inspector
    participant PE as Policy Engine
    participant BC as Blockchain Network
    participant Res as Cloud Resource
    
    AI->>Res: Active Inspection Probe
    activate Res
    Res-->>AI: Inspection Response
    deactivate Res
    AI->>BC: Record Inspection Result (Signed Hash)
    BC->>BC: Validate & Add Block (Inspection Result)
    AI->>PE: Report Result & Path ID
    PE->>PE: Evaluate Policy Rule
    PE->>BC: Record Policy Application Decision (Signed Hash)
    BC->>BC: Validate & Add Block (Policy Decision)
    alt Rule Not Met
        PE->>AI: Initiate Mitigation Action
        AI->>BC: Record Mitigation Action (Signed Hash)
        BC->>BC: Validate & Add Block (Mitigation Action)
    else Rule Met
        PE->>PE: Select Next Path
    end

5. The "Inverse" or Failure Mode

5.1. Derivative: Graceful Degradation of Active Inspection during Network Congestion

Enabling Description:
This derivative focuses on the "failure mode" where the active inspection process itself could negatively impact the target production environment, particularly during periods of high network congestion or resource strain. The system is designed for graceful degradation to prevent self-inflicted denial of service.

1. Selection of reachable resource (S905) and active inspection (S910): The active inspector (125) continuously monitors the network health and resource utilization of the cloud environment (110) it is inspecting, potentially via SNMP, flow data (NetFlow/sFlow), or cloud provider metrics APIs.
2. Dynamic Adjustment of Inspection Intensity: If predefined thresholds for network latency, packet loss, CPU utilization of target resources, or API rate limits are exceeded, the active inspector automatically enters a "low-impact mode." In this mode, the frequency of active inspection probes is reduced (e.g., from hourly to daily), the number of concurrent probes is limited, or the type of probes shifts from aggressive port scanning to passive listening or credential-less checks (e.g., HTTP HEAD requests instead of full GETs).
3. Policy application (S915): Policies are updated to include conditional rules related to operational impact. For example, "if network path accessibility check causes more than 5% CPU spike on target VM, then suspend aggressive probing for 24 hours."
4. Mitigation action (S950): A mitigation action in this context would be the immediate cessation of active probing for affected resources/paths and the generation of an internal alert to the security operations center (SOC) that inspection is degraded, rather than a security vulnerability mitigation.
5. Resumption of full inspection: Once network/resource conditions return to normal, the system automatically transitions back to full active inspection, potentially prioritizing paths that were skipped during the degradation period.

stateDiagram-v2
    [*] --> Initialized
    Initialized --> Full_Inspection: Normal Operation
    Full_Inspection --> Monitor_Environment: Continuously
    Monitor_Environment --> Degraded_Inspection: High Congestion / Resource Strain
    Degraded_Inspection --> Low_Impact_Probes: Reduced Frequency/Intensity
    Degraded_Inspection --> Monitor_Environment: Continuously (Low Impact)
    Low_Impact_Probes --> Full_Inspection: Congestion Cleared / Resources Available
    Degraded_Inspection --> Full_Inspection: Manual Override

5.2. Derivative: "Stealth Mode" Active Inspector for Evasion Resistance

Enabling Description:
This derivative describes an "inverse" approach where the active inspector (125) operates in a "stealth mode" to avoid detection by advanced intrusion detection systems (IDS), intrusion prevention systems (IPS), or honeypots within the target cloud environment. The goal is to simulate sophisticated attacker behavior without triggering alarms.

1. Selecting a reachable resource (S905) and actively inspecting the network path (S910): The active inspector's probe generation module is augmented with evasion techniques. This includes:
   • Traffic Normalization: Mimicking legitimate traffic patterns (e.g., emulating specific user agents, varying inter-packet delays, using common ports).
   • Fragmentation/Reassembly Obfuscation: Sending fragmented packets or out-of-order segments to bypass stateless firewalls.
   • Slow Scan Techniques: Employing extremely slow port scanning (e.g., one port per hour) to avoid exceeding IDS thresholds.
   • Distributed Probing: Utilizing a large pool of rotating source IP addresses (e.g., through a network of compromised proxies or a botnet simulator) to distribute probes over time and source, making correlation difficult.
   • Protocol Fuzzing (Benign): Sending slightly malformed but non-malicious protocol headers to test resilience and unique responses without causing crashes.
2. Policy application (S915): Policies in this context would include rules like "an accessible network path should NOT be detectable by common IDS signatures for active scanning" or "successful stealth-mode access confirms a significant vulnerability for advanced persistent threats (APTs)."
3. Mitigation action (S950): If the stealth-mode active inspection is detected by a simulated IDS/IPS (or by the environment's actual defenses during a test), the mitigation action might be to immediately cease probing from that source IP, switch to a different evasion technique, or generate an alert indicating the effectiveness of the target's defensive controls against advanced scanning.
4. Application on another path: The effectiveness of different stealth techniques is continuously evaluated, and the most successful (undetected) methods are prioritized for subsequent path inspections.

flowchart LR
    A[Threat Intelligence] --> B(Evasion Technique Library)
    B --> C(Stealth Active Inspector Core)
    C -- Select Evasion Technique --> D(Probe Generator)
    D -- Stealth Probes (Low Rate, Obfuscated, Distributed) --> E[Network Path to Cloud Resource]
    E -- Network Response --> D
    D --> F(Response Analyzer - Detects IDS/IPS Alerts)
    F --> G{Detected?}
    G -- Yes --> H[Mitigation Action: Switch Evasion / Cease Probing]
    G -- No --> I[Policy Evaluator (Vulnerability Confirmed)]
    I --> J[Apply Policy on Another Path (with Stealth)]

Combination Prior Art Scenarios

Here are at least three scenarios combining US11936693 with existing open-source standards to demonstrate obviousness of future advancements.

1. Integration with Open Policy Agent (OPA)

Combination Scenario: A system implementing the active inspection and policy application of US11936693, where the "policy engine" (116) and its "conditional rules" (S915) are realized using the Open Policy Agent (OPA) standard and its Rego policy language.

Enabling Description:
The "policy engine" (116) of US11936693 is replaced or augmented by an OPA instance. Network path reachability data, including details from active inspection (S910) such as open ports, protocols, discovered applications, and associated resource metadata (e.g., cloud object type, tags, VPC ID), is transformed into JSON input for OPA. Conditional rules (S915) are defined as Rego policies. For example, a Rego policy could express: allow_external_ssh_access = false if { input.network_path.accessible == true; input.network_path.port == 22; not input.resource.tags.critical_bastion == true }. The active inspector (125) feeds inspection results to OPA, which then evaluates the Rego policies. If an OPA query returns false (policy violation), OPA's decision output triggers the "mitigation action" (S950) defined by the patent, such as calling a cloud API to close a security group port or generate an alert. This combination leverages OPA's declarative policy language and robust evaluation capabilities for managing complex security postures on actively validated network paths.

flowchart TD
    A[Active Inspector] -- Inspection Results (JSON) --> B(Open Policy Agent - OPA)
    B -- Rego Policy Rules --> C{Policy Data (Cloud Resource Metadata)}
    C --> B
    B -- Policy Decision (Allow/Deny) --> D{Conditional Rule Met?}
    D -- No --> E[Mitigation Action Initiator]
    D -- Yes --> F[Apply Policy on Another Path]
    E --> G[Cloud Enforcement APIs]

2. Integration with Kubernetes Network Policies and Operators

Combination Scenario: Applying the active inspection and policy enforcement framework of US11936693 to a Kubernetes-managed cloud computing environment, leveraging Kubernetes Network Policies and custom Operators for mitigation actions.

Enabling Description:
In this scenario, "reachable resources" (S905) are Kubernetes Pods, Deployments, or Services exposed to an "external network" (e.g., through an Ingress controller or Load Balancer). The "active inspector" (125) targets the external IP addresses and ports exposed by Kubernetes Services. After actively inspecting (S910) a network path and determining its accessibility, the "policy engine" (116) evaluates conditional rules. These conditional rules (S915) could be defined in terms of desired Kubernetes Network Policies. For example, a policy might state: "Only Pods with label app=frontend should have external ingress on port 80/443." If active inspection reveals a backend database Pod (without app=frontend label) is externally accessible on port 5432, a "mitigation action" (S950) is initiated by dynamically generating and applying a Kubernetes Network Policy via the Kubernetes API (e.g., restricting ingress to the database Pod). This could be managed by a custom Kubernetes Operator that observes active inspection results and reconciles them with desired Network Policy states. If the initial policy is met, the system moves to apply the policy on another network path within the Kubernetes cluster.

graph TD
    A[External Active Inspector] --> B(Kubernetes Ingress/LoadBalancer)
    B --> C[Kubernetes Service/Pod (Reachable Resource)]
    C -- Network Traffic --> B
    
    AI[Active Inspector (Controller)] -- Inspection Results --> PE[Policy Engine (Kubernetes Operator)]
    PE -- Conditional Rule Check --> NP[Kubernetes Network Policies]
    PE -- Desired State --> KAPI[Kubernetes API Server]
    KAPI -- Apply/Update --> NP
    
    A --> AI
    C --> AI
    
    PE -- Rule Not Met --> MA[Mitigation Action: Update Network Policy]
    PE -- Rule Met --> AP[Apply Policy on Another K8s Path]
    
    MA --> KAPI
    AP --> AI

3. Integration with Apache Metron (Network Security Monitoring) for Enhanced Context

Combination Scenario: Combining US11936693's active inspection capabilities with Apache Metron's network security monitoring and enriched data for better contextual policy enforcement.

Enabling Description:
In this scenario, "reachable resources" (S905) and their "network paths" are identified by US11936693's static analysis and active inspection. However, the decision-making for "applying a policy" (S915) and "initiating a mitigation action" (S950) is heavily informed by data enriched and analyzed by Apache Metron. Metron ingests various data sources (e.g., NetFlow, firewall logs, IDS alerts, DNS queries) from the "cloud computing environment" (110) and the "external network" (130). The "active inspector" (125) feeds its inspection results (e.g., "port 22 on 10.0.0.1.256 is actively open and responds to SSH probes") into Metron as a new data source. Metron's enrichment topologies correlate this active inspection data with passive monitoring data (e.g., "SSH traffic has been observed from this IP historically," "no legitimate SSH activity expected on this VM"). The "conditional rules" (S915) of the policy engine (116) are thus contextually richer: "if active inspection confirms external reachability on port 22 AND Metron's behavioral analytics indicates anomalous SSH activity AND the resource is not tagged as an SSH bastion host, THEN initiate mitigation." This provides a more intelligent and less false-positive-prone policy enforcement system. Mitigation actions (S950) could also be informed by Metron's alert prioritization, ensuring critical threats get immediate attention.

graph TD
    A[Cloud Environment (Firewall Logs, NetFlow, IDS)] --> B(Apache Metron Ingest)
    AI[Active Inspector] -- Inspection Results --> B
    B --> C(Apache Metron Enrichment & Analytics)
    C --> D[Policy Engine]
    D -- Conditional Rules (Contextual) --> E{Metron-Enriched Data}
    E --> D
    D --> F{Conditional Rule Met?}
    F -- No --> G[Mitigation Action (Prioritized by Metron)]
    F -- Yes --> H[Apply Policy on Another Path]
    G --> I[Cloud Enforcement / SOC Alert]