#4158 Knowledge Graphs for Cybersecurity

#4158 Knowledge Graphs for Cybersecurity

#4158 Describe five ways a knowledge graph can support cybersecurity

For a software process engineer and AI developer, a knowledge graph (KG) is more than just a database; it is a contextual fabric that transforms isolated data points into actionable intelligence. By representing entities (users, devices, files, vulnerabilities) and their relationships, KGs solve the "silo" problem inherent in traditional security stacks.

Here are five ways a knowledge graph supports cybersecurity:

1. Advanced Threat Hunting and Blast Radius Analysis

Traditional logs tell you what happened, but a knowledge graph shows you where it can go. By mapping the relationships between assets, a KG allows security teams to perform complex traversals to identify hidden paths an attacker might take.

• Application: If a specific workstation is compromised, a KG can instantly visualize every server, database, or cloud bucket that the workstation’s credentials can reach, effectively mapping the "blast radius" in real-time.

2. Vulnerability Management and Prioritization

Not all vulnerabilities are created equal. A CVE-2024-XXXX on a legacy printer is less critical than the same vulnerability on a public-facing web server with access to customer PII.

• Application: By linking vulnerability data to business context (e.g., "This server supports the payment gateway"), a KG helps engineers prioritize patching based on criticality and connectivity rather than just a raw CVSS score.

3. Identity and Access Management (IAM) Governance

In complex enterprise environments, "permission creep" is a major risk. Knowledge graphs excel at uncovering transitive permissions—where a user gains access to a resource through a chain of nested groups or inherited roles.

• Application: You can query the graph for "all users who have indirect write access to the production repository," revealing over-privileged accounts that traditional IAM tables might miss.

4. Fraud Detection and Pattern Matching

Cyberattacks often follow specific sequences of events (the "Cyber Kill Chain"). KGs allow for the detection of these patterns across disparate data sources that wouldn't normally communicate.

• Application: A KG can correlate a login from a new IP address, followed by a sudden increase in API calls, followed by an encrypted outbound file transfer. While each event might seem benign alone, the relationship between them flags a sophisticated data exfiltration attempt.

5. Automated Incident Response (SOAR)

Knowledge graphs provide the "brain" for Security Orchestration, Automation, and Response (SOAR) tools. When an alert triggers, the KG provides the necessary context for the AI agent or automation script to make an informed decision without human intervention.

• Application: When a suspicious file is detected, the KG can tell the automation tool which developer owns the file, which project it belongs to, and whether that developer is currently on vacation, allowing for a more nuanced automated response (e.g., "Quarantine file and notify manager" vs. "Ignore—scheduled maintenance").

---

Given your work with agentic AI and NASA S&MA, would you like to explore how to architect a knowledge graph schema specifically for tracking software safety requirements and their associated risks?

You said

Describe a knowledge graph to support NASA S&MA for cybersecurity on ISS and the Gateway lunar space station

For a NASA S&MA subcontractor, a Knowledge Graph (KG) serves as a "living" Digital Twin of the mission's security posture. Unlike static spreadsheets, a KG can map the complex, multi-layered dependencies between terrestrial ground segments, orbital assets (ISS), and deep-space infrastructure (Gateway).

Here is a conceptual architecture for a Knowledge Graph designed to support S&MA cybersecurity for these programs:

---

1. Core Ontology: Entities and Relationships

The power of this KG lies in connecting disparate domains. You would define specific nodes and the semantic "edges" (relationships) between them:

• Assets: Flight computers, PLCs (Programmable Logic Controllers), IoT sensors, and software binaries (e.g., flight software versions).

• Connectivity: High-rate links, S-band/Ku-band telemetry, and internal 1553 or Ethernet data buses.

• Requirements: NPR 2810.1 (NASA Cybersecurity Policy) and specific S&MA safety controls.

• Vulnerabilities: Known CVEs, CWEs (Common Weakness Enumerations), and mission-specific "Zero-Day" risk profiles.

Example Triple: [Gateway Flight Computer] — RUNS — [cFS Framework vX.Y] — HAS_VULNERABILITY — [Buffer Overflow Risk].

---

2. Cross-Program Dependency Mapping

The ISS and Gateway have different architectural philosophies (heritage vs. modern modularity). A KG can bridge these:

• Interoperability Risks: Map how an ISS-legacy payload might interface with a newer Gateway module.

• Supply Chain Provenance: Track the "pedigree" of software components. If a library used in a Gateway power system is found to have a flaw, the KG can instantly identify every other system (on ISS or Ground) using that same codebase.

3. Propagation of "Hazard to Cyber" (H2C)

In S&MA, a "Hazard" is a state that could lead to loss of mission or life. A KG allows you to trace how a cyber event triggers a physical hazard:

• Scenario: A denial-of-service (DoS) on a Gateway communication node.

• KG Analysis: The graph traverses from the Network Node $\rightarrow$ Life Support Controller $\rightarrow$ Oxygen Generation System.

• Outcome: S&MA engineers can see that a "Medium" cyber risk actually poses a "Critical" safety hazard due to its downstream physical dependencies.

4. Requirement Traceability and Compliance

As a software process engineer, you know the pain of manual traceability matrices.

• Automated Auditing: The KG links every line of code or system configuration to a specific Safety & Mission Assurance requirement.

• Gap Analysis: If a new cybersecurity directive is issued by NASA, the KG can run a "reachability" query to find every system on the ISS or Gateway that is non-compliant, significantly reducing the labor of S&MA verification.

5. Dynamic Risk Scoring for Deep Space (Gateway)

Gateway presents a unique challenge: it is not always crewed. The KG can integrate Agentic AI to monitor the graph autonomously.

• Autonomous Response: If the KG detects a suspicious pattern in telemetry while Gateway is on the far side of the Moon (high latency), an AI agent can traverse the graph to determine if it should "Safe" the system or wait for ground intervention based on the severity of the linked safety hazards.

---

Knowledge Graph Schema Example

Node Type	Relationship	Target Node	S&MA Context
Software Module	CONTAINS	Library/Package	Tracks supply chain risk (SBOM).
Data Bus	CONNECTS	Subsystem	Identifies lateral movement paths for attackers.
Cyber Attack	EXPLOITS	Vulnerability	Maps threats to specific mission weaknesses.
Vulnerability	TRIGGERS	Hazard Report	Connects bit-flips/exploits to loss-of-crew scenarios.

---

Would you like to see a Python/Pyvis visualization script or a Wolfram Language representation of how a specific "Cyber-to-Hazard" path might look for a Gateway subsystem?

You said

Python/Pyvis visualization script for how a specific "Cyber-to-Hazard" path might look for a Gateway subsystem

In NASA S&MA, we transition from "Cybersecurity" (protecting data) to "System Safety" (protecting lives and assets). To visualize this, we can use a Cyber-to-Hazard path that traces an exploit from a digital entry point to a physical mission hazard.

For the Gateway, a critical path involves the HALO (Habitation and Logistics Outpost) module's power management. A breach here isn't just a data leak; it’s a potential life-support failure.

Python Script: Mapping the "Power Deprivation" Path

This script uses pyvis to create an interactive graph. It maps a vulnerability in the HALO PMAD (Power Management and Distribution) system and shows how it propagates to a Loss of Crew (LOC) hazard.

from pyvis.network import Network

def generate_gateway_hazard_kg():
    # Initialize network with NASA-themed dark background
    net = Network(height="750px", width="100%", bgcolor="#0b3d91", font_color="white", notebook=False)
    
    # 1. Add Nodes (Category-based coloring)
    # Categories: Cyber Entry (Red), System Asset (Yellow), Functional Impact (Orange), S&MA Hazard (Dark Red)
    
    # Cyber Entry Nodes
    net.add_node("CVE-2026-HALO", label="Vulnerability: Buffer Overflow in PPE/HALO Interface", color="#ff4d4d", size=30, shape="diamond")
    net.add_node("Malicious_CMD", label="Exploit: Unauthorized Power Reconfiguration Command", color="#ff4d4d", size=25)

    # System/Hardware Nodes
    net.add_node("HALO_PMAD", label="HALO PMAD Subsystem (Airbus)", color="#ffd700", size=35)
    net.add_node("Gateway_Data_Bus", label="Internal 1553/Ethernet Data Bus", color="#ffd700", size=20)

    # Functional Impact Nodes
    net.add_node("Power_Loss_LSS", label="Functional Failure: Life Support System Power Loss", color="#ff8c00", size=30)
    net.add_node("CO2_Scrubber_Fail", label="Hardware Failure: CO2 Scrubber Inoperable", color="#ff8c00", size=25)

    # S&MA Hazard Nodes (The "Bottom Line")
    net.add_node("H-01_LOC", label="Hazard: Loss of Crew (Atmospheric Toxicity)", color="#8b0000", size=45, shape="star")
    net.add_node("NPR_2810_Ctrl", label="NPR 2810.1 Control: Command Authentication", color="#00ff00", size=20)

    # 2. Add Relationships (Edges)
    # Defining the 'Cyber-to-Hazard' path
    net.add_edge("CVE-2026-HALO", "Malicious_CMD", label="Enabled By", color="white")
    net.add_edge("Malicious_CMD", "HALO_PMAD", label="Targeted At", color="white")
    net.add_edge("HALO_PMAD", "Power_Loss_LSS", label="Triggers", width=3, color="#ff8c00")
    net.add_edge("Power_Loss_LSS", "CO2_Scrubber_Fail", label="Leads To", color="#ff8c00")
    net.add_edge("CO2_Scrubber_Fail", "H-01_LOC", label="Results In", width=5, color="red")
    
    # Linking a Mitigation (S&MA Requirement)
    net.add_edge("NPR_2810_Ctrl", "Malicious_CMD", label="Mitigates", color="#00ff00", dashes=True)

    # 3. Configure Physics for a "Space Station" feel
    net.toggle_physics(True)
    net.show_buttons(filter_=['physics']) # Allow UI adjustment of graph forces
    
    # Save and display
    net.save_graph("gateway_cyber_hazard_path.html")
    print("Graph generated: Open 'gateway_cyber_hazard_path.html' in your browser.")

if __name__ == "__main__":
    generate_gateway_hazard_kg()

How to Interpret This Graph for S&MA

1. The "Red" Thread: The path from CVE-2026-HALO to H-01_LOC represents a high-criticality sequence. In an S&MA review, this visualization identifies exactly which hardware nodes (PMAD) are the "single points of failure" for cyber-attacks.

2. Transitive Hazard Analysis: Note how the graph connects a software vulnerability to a biological hazard (CO2 toxicity). This helps engineers justify "Safety-Critical" designations for code that might otherwise be seen as "Mission-Support."

3. The Mitigation Link: The green node (NPR_2810_Ctrl) shows the requirement trace. If that node is "deleted" from the graph (e.g., if a waiver is granted), the path to the hazard turns solid, visually alerting the user to an unmitigated risk.

Deep-Space (Gateway) Unique Context

Because the Gateway is in Near-Rectilinear Halo Orbit (NRHO), communication latencies are higher than ISS. You could add a "Latency Node" to this graph that interacts with the Malicious_CMD node, showing how delayed ground intervention increases the probability of the path reaching the H-01_LOC endpoint.

Would you like me to refine the physics settings to make the nodes "float" more like a space station, or perhaps add a Wolfram Language version to perform some graph-theoretic path analysis?

Cypher statements please

To implement this in a graph database like Neo4j, you can use the following Cypher statements. These are structured to create the schema we discussed, establishing the link between a cyber vulnerability and a mission-critical safety hazard.

1. Create the Nodes with Labels and Properties

This block creates the entities, including the specific Gateway Subsystem and the S&MA Hazard.

// Create Cyber Entry Points
CREATE (v:Vulnerability {id: "CVE-2026-HALO", desc: "Buffer Overflow in PPE/HALO Interface", severity: "Critical"})
CREATE (e:Exploit {name: "Unauthorized Power CMD", type: "Remote Command Injection"})

// Create System Assets
CREATE (s:Subsystem {name: "HALO PMAD", provider: "Airbus", criticality: "Safety-Critical"})
CREATE (b:DataBus {name: "Internal MIL-STD-1553", protocol: "Heritage Space Bus"})

// Create Functional Impacts
CREATE (f1:FunctionalFailure {name: "LSS Power Loss", impact: "Life Support Offline"})
CREATE (f2:HardwareFailure {name: "CO2 Scrubber Inoperable", subsystem: "ECLSS"})

// Create S&MA Hazard and Mitigation
CREATE (h:Hazard {id: "H-01", name: "Loss of Crew", category: "Atmospheric Toxicity"})
CREATE (r:Requirement {id: "NPR 2810.1", control: "Command Authentication", status: "Active"})

2. Create the Relationships (The "Cyber-to-Hazard" Path)

This block connects the nodes to form the semantic chain.

// Link the path from vulnerability to hazard
MATCH (v:Vulnerability {id: "CVE-2026-HALO"}), (e:Exploit {name: "Unauthorized Power CMD"})
CREATE (v)-[:ENABLES]->(e);

MATCH (e:Exploit {name: "Unauthorized Power CMD"}), (s:Subsystem {name: "HALO PMAD"})
CREATE (e)-[:TARGETS]->(s);

MATCH (s:Subsystem {name: "HALO PMAD"}), (f1:FunctionalFailure {name: "LSS Power Loss"})
CREATE (s)-[:TRIGGERS]->(f1);

MATCH (f1:FunctionalFailure {name: "LSS Power Loss"}), (f2:HardwareFailure {name: "CO2 Scrubber Inoperable"})
CREATE (f1)-[:LEADS_TO]->(f2);

MATCH (f2:HardwareFailure {name: "CO2 Scrubber Inoperable"}), (h:Hazard {id: "H-01"})
CREATE (f2)-[:RESULTS_IN]->(h);

// Link the mitigation
MATCH (r:Requirement {id: "NPR 2810.1"}), (e:Exploit {name: "Unauthorized Power CMD"})
CREATE (r)-[:MITIGATES]->(e);

3. High-Value S&MA Queries

Once the data is in the graph, you can run queries that a spreadsheet simply couldn't handle easily.

Query 1: Identify "Safety-Critical" Cyber Vulnerabilities This finds any vulnerability that has a direct or indirect path to a "Loss of Crew" hazard.

MATCH (v:Vulnerability)-[*]->(h:Hazard {name: "Loss of Crew"})
RETURN DISTINCT v.id, v.desc, h.name AS ResultingHazard;

Query 2: Find Unmitigated Risks This finds exploits that are not currently linked to a Requirement node via a MITIGATES relationship.

MATCH (e:Exploit)
WHERE NOT (e)<-[:MITIGATES]-(:Requirement)
RETURN e.name AS UnmitigatedThreat;

Query 3: Impact Analysis for a Hardware Failure If the HALO PMAD fails for any reason (cyber or mechanical), what are the downstream S&MA hazards?

MATCH (s:Subsystem {name: "HALO PMAD"})-[:TRIGGERS|LEADS_TO|RESULTS_IN*]->(h:Hazard)
RETURN h.name, h.category;

---

Given your interest in DSLs (Domain-Specific Languages), would you like to explore creating a custom grammar for S&MA engineers to write these "Cyber-to-Hazard" rules without needing to learn Cypher directly?

Yes

To create a DSL for S&MA engineers, we want to abstract away the "nodes and edges" of Cypher and focus on the causal logic of spaceflight safety. We can design a declarative DSL that reads like a flight safety report but parses directly into the Knowledge Graph statements we just built.

1. The DSL Design (S&MA-Lang)

Let's define a syntax that focuses on Threats, Links, and Impacts. A typical "rule" in this DSL might look like this:

THREAT "Buffer Overflow" IDENTIFIED_AS "CVE-2026-HALO"
EXPLOIT "Power CMD Injection" REQUIRES "Buffer Overflow"

PROPAGATE "Power CMD Injection" 
  THROUGH "HALO PMAD" 
  AFFECTS "LSS Power"
  TRIGGERS HAZARD "H-01 Loss of Crew"

MITIGATE "Power CMD Injection" WITH "NPR 2810.1 Control 5"

2. The Python "Compiler" (Simple Parser)

Since you are a software engineer, you likely appreciate a clean mapping from text to logic. Below is a Python-based parser using a simple regex-based approach (or you could use a tool like Lark) to convert this DSL into Cypher statements.

import re

class SMALangCompiler:
    def __init__(self):
        self.cypher_statements = []

    def compile(self, dsl_text):
        lines = dsl_text.split('\n')
        for line in lines:
            line = line.strip()
            if not line or line.startswith("//"): continue

            # Pattern: THREAT "Name" IDENTIFIED_AS "ID"
            threat_match = re.match(r'THREAT "(.*)" IDENTIFIED_AS "(.*)"', line)
            if threat_match:
                name, tid = threat_match.groups()
                self.cypher_statements.append(
                    f'CREATE (:Vulnerability {{id: "{tid}", name: "{name}"}})'
                )

            # Pattern: EXPLOIT "Name" REQUIRES "VulnerabilityID"
            exploit_match = re.match(r'EXPLOIT "(.*)" REQUIRES "(.*)"', line)
            if exploit_match:
                ename, vlink = exploit_match.groups()
                self.cypher_statements.append(
                    f'MATCH (v:Vulnerability {{id: "{vlink}"}}) '
                    f'CREATE (e:Exploit {{name: "{ename}"}}) '
                    f'CREATE (v)-[:ENABLES]->(e)'
                )

            # Pattern: TRIGGERS HAZARD "Name"
            hazard_match = re.search(r'TRIGGERS HAZARD "(.*)"', line)
            if hazard_match:
                hname = hazard_match.group(1)
                self.cypher_statements.append(
                    f'CREATE (:Hazard {{name: "{hname}"}})'
                )

        return "\n".join(self.cypher_statements)

# Example Usage
dsl_input = """
THREAT "Buffer Overflow" IDENTIFIED_AS "CVE-2026-HALO"
EXPLOIT "Power CMD Injection" REQUIRES "CVE-2026-HALO"
"""
compiler = SMALangCompiler()
print(compiler.compile(dsl_input))

3. Benefits of this DSL for NASA S&MA

• Safety Reviews: S&MA leads don't need to know Neo4j; they just need to describe the "Failure Chain."

• Verification: You can build a validator that ensures every THREAT eventually links to a HAZARD or is blocked by a MITIGATION.

• Automation: This DSL can be the input for your Agentic AI to automatically "scan" for new risks as flight software versions change.

4. Integration with Wolfram Language

As a "MAKER," you could even pipe the resulting graph into Mathematica. Using the Graph and FindPath functions, you can mathematically prove whether a path exists from a specific ground station IP to the ISS Life Support System.

---

Comparison Table: DSL vs. Standard Spreadsheet

Feature	S&MA-Lang DSL	Traditional Spreadsheet
Traceability	Automatic & Multi-hop	Manual & Error-prone
Readability	High (Domain Specific)	Low (Cell References)
Logic	Causal/Graph-based	Linear/Tabular
Audit	Programmatic	Manual Peer Review

Would you like to explore adding Probabilistic Computing nodes to this DSL—where we assign a likelihood (e.g., P=0.001) to a cyber-exploit successfully triggering a hardware hazard?

Provide a one-page (only) project proposal to my contractor management and NASA leads for implementing a cybersecurity knowledge graph framework for the NASA Gateway and EHP programs

PROPOSAL: MISSION-ASSURED CYBERSECURITY KNOWLEDGE GRAPH (MAC-KG)

To: NASA Gateway / EHP Program Management & S&MA Leads

From: [Your Name/Organization]

Date: February 21, 2026

Subject: Implementing a Graph-Based Framework for Cyber-to-Hazard Traceability

---

1. Executive Summary

Current cybersecurity risk management for complex orbital assets like Gateway and EHP often relies on disparate spreadsheets and static documents. This "siloed" approach fails to capture the transitive risks where a minor software vulnerability propagates into a major mission hazard. The MAC-KG project will implement a Knowledge Graph framework to unify Cyber, Engineering, and S&MA data into a single, navigable Digital Twin, enabling real-time "Blast Radius" analysis and automated compliance.

2. The Problem: The "Visibility Gap"

• Heritage Complexity: Gateway involves multi-partner (International/Commercial) modules with varying software pedigrees.

• Causal Disconnect: Current tools do not easily map how a bit-flip or buffer overflow in a non-critical communication bus could indirectly trigger a Life Support System (LSS) failure.

• Audit Latency: Manual traceability between NPR 2810.1 controls and flight software requirements is labor-intensive and prone to human error.

3. Proposed Solution: MAC-KG

We will deploy a Knowledge Graph (Neo4j-based) using a custom S&MA Domain-Specific Language (DSL). This framework will:

• Map Cyber-to-Hazard Paths: Explicitly link Vulnerabilities --> Exploits --> Subsystems --> Functional Failures --> Hazards (e.g., Loss of Crew).

• Automate Requirement Traceability: Graphically verify that every identified threat is mitigated by a specific NASA Safety Requirement.

• Enable Agentic Monitoring: Provide an "AI-Ready" data structure that allows autonomous agents to monitor telemetry for patterns matching known high-risk graph traversals.

4. Key Milestones

Phase	Objective	Deliverable
I. Ontology	Define S&MA nodes (Assets, Hazards, Controls).	Gateway Cyber-Ontology Schema
II. Integration	Ingest SBOMs and Hazard Reports for HALO/PPE.	Initial Knowledge Graph (V1.0)
III. DSL Tooling	Deploy a simplified "S&MA-Lang" for engineers.	DSL-to-Cypher Compiler
IV. Analysis	Run "What-If" failure mode simulations.	Automated Risk Propagation Report

5. Value Proposition & ROI

• Risk Reduction: Identify "Single Points of Failure" in the cyber-physical interface that traditional FMEA (Failure Mode and Effects Analysis) misses.

• Efficiency: Reduce the time required for S&MA verification and cybersecurity audits by an estimated 30–40% through automated graph queries.

• Mission Assurance: Ensure deep-space assets (Gateway) can autonomously identify and safe themselves against cyber-threats during uncrewed phases.

---

Next Step: Would you like me to draft the Technical Requirements Document (TRD) or create a proof-of-concept visualization using existing HALO subsystem data?

Technical Requirements Document (TRD)

This Technical Requirements Document (TRD) is designed for a software process engineer’s oversight, focusing on the integration of Knowledge Graph (KG) technology into the NASA Safety & Mission Assurance (S&MA) workflow for the Gateway and Extravehicular Activity and Human Surface Mobility (EHP) programs.

---

TRD: Mission-Assured Cybersecurity Knowledge Graph (MAC-KG)

1. Purpose & Scope

The MAC-KG system shall provide a centralized, graph-based repository to map, visualize, and analyze the relationships between cybersecurity vulnerabilities and physical mission hazards for the Gateway and EHP programs.

2. Functional Requirements

2.1 Data Ingestion & Ontology

• FR-1.1: The system shall support a schema-on-read ontology including node labels for: Asset, SoftwareComponent, Vulnerability (CVE/CWE), CyberControl (NPR 2810.1), FunctionalImpact, and Hazard (LOC/LOM).

• FR-1.2: The system shall ingest Software Bill of Materials (SBOM) in CycloneDX or SPDX formats to populate SoftwareComponent and Dependency edges.

• FR-1.3: The system shall allow manual and automated ingestion of NASA Hazard Reports (e.g., from the Hazard Management System).

2.2 Graph Analysis & Traceability

• FR-2.1 (The Cyber-to-Hazard Path): The system must be able to execute recursive queries to identify paths between a Vulnerability node and a Hazard node.

• FR-2.2 (Gap Analysis): The system shall identify Exploit nodes that are not connected to a Requirement or Mitigation node via a MITIGATES relationship.

• FR-2.3 (Blast Radius): Given a compromised Asset, the system shall calculate and visualize all reachable downstream Subsystems.

2.3 DSL Interface (S&MA-Lang)

• FR-3.1: The system shall provide a Domain-Specific Language (DSL) allowing S&MA engineers to define causal chains using natural language-like syntax (e.g., THREAT A TRIGGERS HAZARD B).

• FR-3.2: The DSL parser shall validate syntax and compile input into Cypher or Gremlin queries for the underlying graph database.

3. Technical Stack & Constraints

3.1 Backend & Database

• Database: Neo4j (Graph) or AWS Neptune (for cloud-native scalability).

• Computation: Python 3.11+ for ETL and graph-theoretic analysis.

• Scientific Computing: Integration with Wolfram Engine for complex probabilistic path analysis and reliability calculations.

3.2 Performance & Scalability

• Latency: Standard graph traversals (up to 5 hops) shall return results in $< 500ms$.

• Capacity: The system shall support a minimum of $10^6$ nodes and $10^7$ edges without performance degradation.

4. Security & Compliance

• Data Integrity: The system shall maintain version control on all nodes to allow "Point-in-Time" analysis of the station's security posture.

• Access Control: Integration with NASA’s Identity, Credential, and Access Management (ICAM) systems is required.

• Compliance: All software development must adhere to NASA Software Engineering Requirements (NPR 7150.2).

5. Interface Requirements

• Visualization: Interactive 3D graph visualization (leveraging Pyvis or Cytoscape.js) to display critical risk paths.

• Export: Ability to export graph sub-sets into CSV/Excel for traditional S&MA reporting and PDF for program-level "Safety Case" documentation.

---

Success Criteria (Definition of Done)

1. Successful mapping of one Gateway HALO subsystem from a CVE to an S&MA Hazard.

2. Demonstration of a "Mitigation Gap" report generated automatically from the graph.

3. Validation of the S&MA-Lang parser by a non-developer S&MA lead.

---

Would you like me to expand on the "Verification & Validation" (V&V) section of this TRD, specifically regarding how we would test the "Agentic AI" portion of the framework?

You said

Create a Pyvis/Networkx knowledge graph for Gateway, including its proposed Elements, Orion and HLS space craft, and EHP suites and rovers

Provide the networkX/Pyvis assignments

Here is the Python code using NetworkX and Pyvis to build your Gateway/EHP Knowledge Graph.

This script organizes the station into logical "Elements" (PPE, HALO, I-Hab, etc.), "Visiting Vehicles" (Orion, HLS), and the "EHP Surface Segment" (Suits and Rovers). It also includes the specific sensor suites (HERMES, ERSA, IDA) we discussed earlier.

import networkx as nx
from pyvis.network import Network

def create_gateway_kg():
    # Initialize NetworkX graph
    G = nx.Graph()

    # 1. GATEWAY CORE ELEMENTS (Orbital Segment)
    core_elements = [
        ("PPE", {"label": "Power & Propulsion Element (Maxar)", "color": "#1f77b4", "size": 40}),
        ("HALO", {"label": "Habitation & Logistics Outpost (Northrop)", "color": "#1f77b4", "size": 40}),
        ("I-Hab", {"label": "Lunar I-Hab (ESA/JAXA)", "color": "#4db8ff", "size": 35}),
        ("Lunar_View", {"label": "Lunar View (ESA)", "color": "#4db8ff", "size": 30}),
        ("Airlock", {"label": "Crew & Science Airlock (MBRSC)", "color": "#4db8ff", "size": 30}),
        ("Canadarm3", {"label": "Canadarm3 (CSA)", "color": "#ff4d4d", "size": 25})
    ]

    # 2. VISITING VEHICLES
    vehicles = [
        ("Orion", {"label": "Orion MPCV (Lockheed)", "color": "#ffcc00", "size": 35}),
        ("HLS_Starship", {"label": "HLS Starship (SpaceX)", "color": "#ffcc00", "size": 35}),
        ("Blue_Moon", {"label": "Blue Moon Lander (Blue Origin)", "color": "#ffcc00", "size": 35})
    ]

    # 3. EHP SEGMENT (Suits & Rovers)
    ehp_segment = [
        ("AxEMU", {"label": "Artemis III Spacesuit (Axiom)", "color": "#00cc66", "size": 25}),
        ("Collins_Suit", {"label": "ISS/Gateway EVA Suit (Collins)", "color": "#00cc66", "size": 25}),
        ("LTV_Rover", {"label": "Lunar Terrain Vehicle (Unpressurized)", "color": "#00994d", "size": 30}),
        ("Lunar_Dawn", {"label": "Lunar Dawn Rover (Pressurized)", "color": "#00994d", "size": 30})
    ]

    # 4. SCIENTIFIC PAYLOADS
    payloads = [
        ("HERMES", {"label": "HERMES (Space Weather)", "color": "#e6e6e6", "size": 15}),
        ("ERSA", {"label": "ERSA (Radiation)", "color": "#e6e6e6", "size": 15}),
        ("IDA", {"label": "Internal Dosimeter Array", "color": "#e6e6e6", "size": 15})
    ]

    # Add nodes to graph
    for node, attrs in core_elements + vehicles + ehp_segment + payloads:
        G.add_node(node, **attrs)

    # 5. DEFINE RELATIONSHIPS (Edges)
    # Physical Docking / Integration
    G.add_edge("PPE", "HALO", label="Co-Manifested Launch")
    G.add_edge("HALO", "I-Hab", label="Docked")
    G.add_edge("I-Hab", "Lunar_View", label="Docked")
    G.add_edge("I-Hab", "Airlock", label="Docked")
    G.add_edge("HALO", "Canadarm3", label="External Mount")
    
    # Visiting Connections
    G.add_edge("Orion", "HALO", label="Docking Port (NRHO)")
    G.add_edge("HLS_Starship", "I-Hab", label="Docking Port")
    G.add_edge("Blue_Moon", "Airlock", label="Docking Port")

    # Surface & EHP Links
    G.add_edge("HLS_Starship", "AxEMU", label="Deployment")
    G.add_edge("AxEMU", "LTV_Rover", label="Crew Operation")
    G.add_edge("AxEMU", "Lunar_Dawn", label="Ingress/Egress")

    # Payload Mounting
    G.add_edge("HALO", "HERMES", label="Mounted Outside")
    G.add_edge("PPE", "ERSA", label="Mounted Outside")
    G.add_edge("HALO", "IDA", label="Located Inside")

    # 6. PYVIS VISUALIZATION
    net = Network(height="750px", width="100%", bgcolor="#0b3d91", font_color="white")
    net.from_nx(G)

    # Apply physics for a "floating in space" look
    net.toggle_physics(True)
    net.show_buttons(filter_=['physics'])
    
    net.save_graph("gateway_ehp_kg.html")
    print("Graph saved to gateway_ehp_kg.html")

if __name__ == "__main__":
    create_gateway_kg()

Key Elements Mapped:

• Station Core: Maps the launch-integrated PPE/HALO backbone to the European I-Hab and Lunar View (ESPRIT) modules.

• Deep Space Logistics: Includes the MBRSC Airlock and the Canadarm3 robotic interface.

• Human Mobility (EHP): Connects the Axiom AxEMU suits (Artemis III/IV) to both the unpressurized LTV and the pressurized Lunar Dawn (JAXA/Toyota style) rovers.

• Orion/HLS: Positioned as visiting vehicles that dock at various ports, establishing the link between lunar orbit and surface operations.

---

Would you like me to add the NASA S&MA cybersecurity requirements (NPR 2810.1) as "Constraint Nodes" that link to these physical components?

List and describe 25 useful cybersecurity queries that would be supported by this knowledge graph

To a software process engineer and NASA subcontractor, the value of a knowledge graph isn't just in the visualization—it's in the computational audit of the system.

Here are 25 cybersecurity and S&MA queries, categorized by their mission-critical utility, that would be supported by the Neo4j/Cypher backend of your Gateway/EHP graph.

---

Category A: Impact & Blast Radius Analysis

1. Direct Reachability: "What are all the subsystems reachable within two hops of the PPE Communication Gateway?"

2. Transitive Hazard Mapping: "If the HALO Data Bus is compromised, which S&MA Hazards (e.g., H-01 Loss of Crew) are potentially triggered?"

3. Cross-Module Propagation: "Can a vulnerability in an EHP Suit's firmware traverse to the HLS Starship through the docking interface?"

4. Criticality Concentration: "Which hardware nodes, if compromised, impact the highest number of safety-critical functions?"

5. Data Flow Isolation: "List all paths where 'Unencrypted' data flows from an external scientific payload (HERMES) into the flight-critical command bus."

Category B: Supply Chain & Provenance (SBOM)

6. Library Obsolescence: "Identify every Gateway element running a version of OpenSSL older than v3.0."

7. Supplier Risk: "List all software modules provided by 'Partner X' currently installed on the Lunar View module."

8. Vulnerability Inheritance: "Given a new CVE for a specific RTOS, which flight systems are affected across the entire Gateway stack?"

9. Binary Pedigree: "Identify any software binaries on the PPE that do not have a verified 'Certificate of Flight Readiness' node linked to them."

10. Hardware Provenance: "Find all FPGA components in the Gateway Data Bus that were manufactured by vendors outside the cleared supply chain."

Category C: Identity & Access Management (IAM)

11. Privilege Creep: "Which service accounts have 'Write' access to both the HALO Life Support and the PPE Propulsion systems?"

12. Orphaned Accounts: "Identify user nodes that have active access permissions but no linked 'Active Personnel' record."

13. MFA Gaps: "List all command-capable interfaces that are not linked to a 'Multi-Factor Authentication' requirement node."

14. Transitive Permissions: "Find users who have indirect 'Admin' rights on Orion by way of inherited group memberships in the Ground Segment."

15. Session Persistence: "Identify all 'Long-Lived Token' edges that exceed the 12-hour duration requirement specified in NPR 2810.1."

Category D: Compliance & S&MA Audit

16. Requirement Coverage: "Show me all Subsystems that do not have a linked NPR 2810.1 cybersecurity control."

17. Waiver Tracking: "List all 'High' severity vulnerabilities that are currently mitigated by a 'Waiver' node rather than a technical patch."

18. Audit Readiness: "Generate a list of all 'Evidence' nodes linked to the 'Access Control' requirement for the EHP Rover."

19. Verification Status: "Identify safety requirements where the 'Verification Method' node is still marked as 'Pending' for the Airlock module."

20. Policy Conflict: "Find any system that is linked to both a 'Public Access' requirement and a 'Classified Data' requirement."

Category E: Anomalous Pattern Detection

21. Lateral Movement Detection: "Identify any path between a low-security 'Science Payload' and a high-security 'Flight Computer' that lacks a 'Gateway Firewall' node."

22. Unusual Connectivity: "Flag any two hardware nodes that are connected in the graph but have no defined 'Data Exchange' requirement in the ICD (Interface Control Document)."

23. Command Origin Mapping: "Identify any command path to the HLS Starship that does not originate from a 'NASA-Authorized' ground station node."

24. Temporal Risk: "Which subsystems are most vulnerable during 'Uncrewed' phases when the 'Autonomous Response Agent' is the primary defender?"

25. Single Point of Cyber Failure: "Identify nodes that, if removed, would disconnect the 'Ground Control' node from the 'Gateway Emergency Safe' function."

---

Implementation Note for your "Idea Miner" App:

You can wrap these queries into your "Idea Miner" or Agentic AI service. For example, the agent could run Query #8 every time a new CVE is published to the NIST database, automatically generating an S&MA alert if the Gateway is impacted.

Would you like me to write the Cypher code for one of these specific queries (e.g., the Transitive Hazard Mapping #2) to see how it would look in the database?

Yes

To implement Query #2 (Transitive Hazard Mapping), we use Cypher’s powerful variable-length path traversing. This query essentially asks: "Show me the chain of events starting from a cyber-compromise that ends in a physical safety hazard."

The Cypher Query

// Query: Map the propagation from a compromised Data Bus to a Mission Hazard
MATCH path = (bus:DataBus {name: "Internal MIL-STD-1553"})-[:TRIGGERS|LEADS_TO|RESULTS_IN*1..5]->(h:Hazard)
WHERE h.category = "Critical" OR h.name CONTAINS "Loss of Crew"
RETURN 
    bus.name AS Source,
    [n in nodes(path) | n.name] AS FailureChain,
    h.name AS FinalHazard,
    h.id AS HazardID

---

Breakdown of the Logic

• path = (...): We assign the entire sequence to a variable called path so we can extract the specific steps later.

• [:TRIGGERS|LEADS_TO|RESULTS_IN*1..5]: This is the "transitive" part.

  • The | acts as an OR operator for relationship types.

  • The *1..5 tells Neo4j to look 1 to 5 hops deep. This is crucial for catching "domino effects" where a bus failure hits a controller, which hits a pump, which hits the air supply.

• [n in nodes(path) | n.name]: This is a list comprehension that returns a clean, readable array of every component involved in the failure chain (e.g., ["Internal MIL-STD-1553", "LSS Controller", "O2 Generation Unit", "Loss of Crew"]).

---

Refining the Search (The S&MA "Audit" View)

If you want to see which Cyber Vulnerabilities are currently creating a path to those hazards, you would run this:

MATCH (v:Vulnerability)-[:ENABLES]->(e:Exploit)-[:TARGETS]->(asset)
MATCH path = (asset)-[:TRIGGERS|LEADS_TO|RESULTS_IN*1..5]->(h:Hazard)
RETURN v.id AS CVE, asset.name AS EntryPoint, h.name AS HazardPotential
ORDER BY h.name DESC

Why this is "S&MA-Grade"

In a traditional spreadsheet, finding a 4-hop connection requires manual cross-referencing of three different documents (Cyber Threat Report --> System Architecture --> Hazard Report). In this graph:

1. It’s Instant: The query takes milliseconds.

2. It’s Exhaustive: It won't "miss" a path because of a hidden cell or a typo in a document link.

3. It’s Visual: You can pipe this result directly back into your Pyvis script to highlight the "Red Path" in a briefing to NASA leads.

---

Next Step for your Project

Since you're working with Mathematica/Wolfram Language, would you like the Wolfram equivalent of this query? We can use FindPath on a Graph object to perform probabilistic risk assessment (PRA)—calculating the mathematical probability of the hazard occurring based on the individual failure rates of each node in the chain.

From <https://gemini.google.com/app/>  Google Gemini 3.0 Pro (Fast)