#4177 Modeling a Business Computing and Communications Network to Support Cybersecurity

#4177 To modeling business computing and communications network to support cybersecurity implementation based on standards, what are the various entities that should be added to the model?

To build a robust model for a business computing and communications network—specifically through the lens of cybersecurity standards like NIST SP 800-53, ISO/IEC 27001, or CMMC—you need to move beyond simple "nodes and links."

The model must capture the relationships between technical infrastructure, human roles, and administrative controls. Here are the essential entities categorized for a comprehensive cybersecurity model:

1. Physical and Logical Infrastructure

These represent the "where" and "what" of your network architecture.

Assets/Endpoints: Individual workstations, servers (on-prem and cloud), mobile devices, and IoT/OT sensors.
Network Segments: Virtual LANs (VLANs), DMZs, and micro-segmentation boundaries that define "trust zones."
Boundary Protection Devices: Firewalls, VPN gateways, Proxies, and Intrusion Detection/Prevention Systems (IDS/IPS).
Data Flows: Directed paths representing how information moves between entities, including protocol types (e.g., HTTPS, SSH, SMB).

2. Identity and Access Management (IAM)

Cybersecurity standards focus heavily on "who" can access "what."

Subjects (Users): Human actors, often categorized by personas (Admin, Employee, Contractor, Guest).
Service Accounts: Non-human identities used by applications or automated processes to interact with the system.
Roles/Groups: Logical containers for permissions that facilitate the Principle of Least Privilege.
Authentication Factors: Entities representing MFA methods, certificates, or biometric data points.

3. Data and Information Assets

You cannot protect what you haven't labeled.

Data Objects: Databases, file shares, or specific sensitive files.
Information Classifications: Tags or metadata defining the sensitivity level (e.g., Public, Internal, CUI, Secret).
Encryption States: Entities representing data-at-rest (storage), data-in-transit (network), and data-in-use (memory).

4. Security Controls and Monitoring

These entities represent the "how" of your defense-in-depth strategy.

Security Controls: Specific administrative, technical, or physical safeguards (e.g., "Policy 5.1," "Disk Encryption").
Vulnerabilities: Known weaknesses (CVEs) associated with specific Assets or Software.
Logging/Audit Points: Sources of telemetry, such as SIEM collectors or syslogs.
Threat Actors: Modeled profiles of potential attackers to test the network's resilience (Red Teaming/Threat Modeling).

5. Governance and Compliance (The "Standards" Layer)

To align with formal frameworks, the model needs these high-level anchors:

Regulatory Requirements: The specific clauses from NIST, ISO, or GDPR that the network must satisfy.
Policies & Procedures: Documentation that governs the configuration and behavior of all other entities.
Risk Assessments: The calculated likelihood and impact of a threat exploiting a vulnerability within the model.

Suggested Modeling Approach

Since you are modeling complex relationships, using a Knowledge Graph (RDF or Property Graph) is often superior to a flat spreadsheet. This allows you to query the model for "Blast Radius" or "Compliance Gaps" by tracing paths between a User, an Endpoint, and a Data Object.

Would you like me to help you draft a Cypher query or a Mermaid.js diagram to visualize how these entities interact within a specific segment of the network?

In addition to the entities, provide a list of the various relationships to be modeled

To move from a static inventory to a dynamic cybersecurity model, you must define how entities interact. In a security context, relationships often represent access, flow, dependency, and vulnerability.

Here are the primary relationships to model, categorized by their function within a business computing and communications network:

1. Access and Identity Relationships

These define the "Who" and "How" of your security perimeter.

MemberOf: Connects a User or Service Account to a Group/Role (e.g., User_A MemberOf Domain_Admins).
AssignedTo: Links a Security Role to a specific Permission or Access Right.
AuthenticatesVia: Maps a Subject to an Authentication Factor (e.g., Admin_User AuthenticatesVia Hardware_Token).
HasAccessTo: A high-level relationship between a Subject and a Data Object or Asset.

2. Structural and Network Relationships

These define the topology and the physical/logical "Where."

HostedOn: Connects an Application or Virtual Server to its physical Host/Hardware.
MemberOfSegment: Links an Endpoint to a specific Network Segment or VLAN.
Traverses: Defines the path a Data Flow takes through Boundary Protection Devices (e.g., Traffic Traverses Firewall_01).
CommunicatesWith: Represents a peer-to-peer or client-server connection between two Assets over a specific protocol.

3. Vulnerability and Risk Relationships

These are critical for threat modeling and impact analysis.

Affects: Links a discovered Vulnerability (CVE) to a specific Asset or Software version.
ExploitableBy: Connects a Vulnerability to a Threat Actor profile or specific Attack Vector.
Mitigates: Shows how a Security Control reduces the risk of a specific Vulnerability (e.g., Patch_KB501 Mitigates CVE-2023-XXXX).
DependsOn: Indicates a functional requirement between two entities (e.g., E-commerce_App DependsOn SQL_Database). This is vital for calculating the "Blast Radius" of an outage or breach.

4. Governance and Compliance Relationships

These map technical reality to the standards (NIST, ISO, CMMC).

GovernedBy: Connects an Asset or Process to a specific Policy or Procedure.
Satisfies: Maps a technical Security Control or Configuration to a Regulatory Requirement (e.g., AES-256_Encryption Satisfies NIST_800-53_SC-28).
Monitors: Links a Logging/Audit Point to an Asset or Network Segment to track activity.
Contains: Defines data residency (e.g., Database_A Contains PII_Data_Class).

Logical Example: The "Attack Path" Chain

By modeling these relationships, you can perform a graph traversal to identify risks that a simple list would miss:

[Threat Actor] —(Uses)—> [Phishing Vector] —(Compromises)—> [User] —(MemberOf)—> [Admin Group] —(HasAccessTo)—> [Server] —(HostedOn)—> [Cloud Tenant] —(Contains)—> [Sensitive Data].

Data Representation Table

Relationship	Source Entity	Target Entity	Security Value
Protects	Security Control	Asset / Data	Identifies defensive coverage gaps.
Exposes	Configuration	Vulnerability	Identifies misconfigurations leading to risk.
Transfers	Data Flow	Network Segment	Visualizes data exfiltration paths.
OwnerOf	Subject (Human)	Data / Asset	Establishes accountability for compliance.

Would you like me to generate a Knowledge Graph schema (in Cypher for Neo4j) that incorporates these entities and relationships?

Yes

Since you are a MAKER and an AI developer often working with knowledge graphs and NASA-related data structures, a property graph model is the most effective way to represent these multi-dimensional security relationships.

Below is a Cypher schema for Neo4j. This script defines the constraints (to ensure data integrity) and provides a template for creating the nodes and relationships discussed.

1. Schema Setup: Constraints

First, we ensure that our unique identifiers are indexed for fast traversal—essential for "blast radius" or "attack path" queries.

// Ensure unique IDs for primary entities
CREATE CONSTRAINT FOR (a:Asset) REQUIRE a.id IS UNIQUE;
CREATE CONSTRAINT FOR (u:User) REQUIRE u.id IS UNIQUE;
CREATE CONSTRAINT FOR (v:Vulnerability) REQUIRE v.cve_id IS UNIQUE;
CREATE CONSTRAINT FOR (c:Control) REQUIRE c.id IS UNIQUE;

2. Entity & Relationship Template

This block creates a small "micro-network" to demonstrate how the entities connect across infrastructure, identity, and compliance layers.

// 1. Create Infrastructure Entities
CREATE (ws:Asset {id: "WS-092", type: "Workstation", os: "Win11", critical: true})
CREATE (srv:Asset {id: "DB-PROD", type: "Server", role: "Database", ip: "10.0.5.20"})
CREATE (seg:Segment {name: "Production_VLAN", zone: "Trusted"})

// 2. Create Identity Entities
CREATE (user:User {id: "U402", name: "Alice", role: "DevOps"})
CREATE (role:Role {name: "DB_Admin", pPrivilege: "High"})

// 3. Create Security & Compliance Entities
CREATE (vuln:Vulnerability {cve_id: "CVE-2024-1234", severity: "High"})
CREATE (ctrl:Control {id: "AC-3", standard: "NIST 800-53", name: "Access Enforcement"})
CREATE (data:Data {type: "CUI", sensitivity: "High", name: "NASA_Project_X"})

// --- DEFINE RELATIONSHIPS ---

// Infrastructure & Data Flows
MERGE (ws)-[:MEMBER_OF_SEGMENT]->(seg)
MERGE (srv)-[:MEMBER_OF_SEGMENT]->(seg)
MERGE (srv)-[:CONTAINS]->(data)

// Identity & Access
MERGE (user)-[:MEMBER_OF]->(role)
MERGE (role)-[:HAS_ACCESS_TO {mode: "ReadWrite"}]->(srv)

// Risk & Vulnerability
MERGE (vuln)-[:AFFECTS]->(srv)
MERGE (ctrl)-[:PROTECTS]->(srv)
MERGE (ctrl)-[:SATISFIES]->(data) // Compliance mapping

3. High-Value Cyber-Queries

Once this graph is populated, you can run queries that a traditional relational database would struggle with:

A. The "Blast Radius" Query

Find out exactly what data is at risk if a specific user's workstation is compromised.

MATCH (u:User {id: "U402"})-[:MEMBER_OF]->(r:Role)-[:HAS_ACCESS_TO]->(a:Asset)-[:CONTAINS]->(d:Data)
RETURN u.name, a.id, d.name

B. The Compliance Gap Query

Find assets that have a "High" severity vulnerability but no active security control protecting them.

MATCH (a:Asset)<-[:AFFECTS]-(v:Vulnerability {severity: "High"})
WHERE NOT (a)<-[:PROTECTS]-(:Control)
RETURN a.id, v.cve_id

C. Trust Boundary Violation

Identify data flows that cross from an "Untrusted" segment directly to a "Trusted" segment containing sensitive data.

MATCH (un:Segment {zone: "Untrusted"})-[f:DATA_FLOW]->(tr:Segment {zone: "Trusted"})
MATCH (tr)-[:CONTAINS]->(d:Data {sensitivity: "High"})
RETURN un.name, tr.name, d.name

Implementation Note for your "Idea Miner" or Agentic AI projects:

Since you're exploring agentic AI, you could feed this graph schema into an LLM context. This allows an AI agent to "reason" through a network map to suggest remediation steps based on the specific standards (NIST/CMMC) you've modeled.

Would you like me to generate a Python script using pyvis (as we've discussed previously for your other graphs) to visualize this network in an interactive HTML format?

Yes

This Python script uses pyvis to generate an interactive, searchable HTML visualization of the cybersecurity model. Given your work with knowledge graphs and NASA S&MA standards, I’ve styled this to reflect a "Security Control Center" aesthetic.

The script categorizes entities by color and shape, allowing you to visually trace the path from a Vulnerability to a Data Asset.

Python Script: CyberGraph Generator

from pyvis.network import Network
import pandas as pd

def generate_cyber_graph():
    # Initialize the network with a dark theme
    net = Network(height="750px", width="100%", bgcolor="#222222", font_color="white", directed=True)
    
    # Enable search/filter menus for "Needle in a Haystack" analysis
    net.select_menu = True
    net.filter_menu = True

    # 1. DEFINE ENTITIES (Nodes)
    # Categories: Infrastructure, Identity, Data, Risk, Compliance
    nodes = [
        {"id": "WS-092", "label": "Workstation 092", "group": "Infrastructure", "shape": "dot", "color": "#00dbff", "title": "OS: Win11 | Critical: Yes"},
        {"id": "DB-PROD", "label": "Prod Database", "group": "Infrastructure", "shape": "database", "color": "#00dbff"},
        {"id": "VLAN-SEC", "label": "Secure Segment", "group": "Infrastructure", "shape": "hexagon", "color": "#7a7a7a"},
        
        {"id": "Alice", "label": "Alice (DevOps)", "group": "Identity", "shape": "face", "color": "#ffca00"},
        {"id": "Role-Admin", "label": "DB Admin Role", "group": "Identity", "shape": "diamond", "color": "#ffca00"},
        
        {"id": "CUI-Data", "label": "NASA Project X (CUI)", "group": "Data", "shape": "star", "color": "#ff4d4d", "title": "Classification: High"},
        
        {"id": "CVE-1234", "label": "CVE-2024-1234", "group": "Risk", "shape": "triangle", "color": "#ff007c", "title": "Severity: Critical"},
        
        {"id": "AC-3", "label": "NIST AC-3", "group": "Compliance", "shape": "box", "color": "#00ff87", "title": "Access Enforcement Control"}
    ]

    for n in nodes:
        net.add_node(n['id'], label=n['label'], shape=n['shape'], color=n['color'], title=n.get('title', ''), group=n['group'])

    # 2. DEFINE RELATIONSHIPS (Edges)
    relationships = [
        ("Alice", "Role-Admin", "MEMBER_OF"),
        ("Role-Admin", "DB-PROD", "HAS_ACCESS_TO"),
        ("WS-092", "VLAN-SEC", "MEMBER_OF_SEGMENT"),
        ("DB-PROD", "VLAN-SEC", "MEMBER_OF_SEGMENT"),
        ("DB-PROD", "CUI-Data", "CONTAINS"),
        ("CVE-1234", "DB-PROD", "AFFECTS"),
        ("AC-3", "DB-PROD", "PROTECTS"),
        ("AC-3", "CUI-Data", "SATISFIES")
    ]

    for src, dst, rel in relationships:
        net.add_edge(src, dst, label=rel, arrows="to", color="#ffffff", font={"size": 10, "align": "top"})

    # 3. SET PHYSICS (Optional: improves layout for complex graphs)
    net.barnes_hut(gravity=-8000, central_gravity=0.3, spring_length=250)

    # 4. GENERATE AND SAVE
    output_file = "cybersecurity_model.html"
    net.save_graph(output_file)
    print(f"Graph generated successfully: {output_file}")

if __name__ == "__main__":
    generate_cyber_graph()

How to use this visualization:

Hover: Mouse over any node to see metadata (e.g., NIST standard ID or Vulnerability severity).
Search: Use the dropdown menu in the resulting HTML file to highlight specific clusters (e.g., "Show me everything related to Alice").
Trace: Visually identify if there is a path from a Vulnerability (triangle) to your CUI Data (star).

Next Step for the "Idea Miner"

Since you are developing agentic AI, you could modify the relationships list to be dynamic. You could have an agent scan your system logs or a NIST document and automatically append new SATISFIES or AFFECTS edges to this graph in real-time.

Would you like me to show you how to automate the Data Classification tagging using a small Python NLP script to feed into this graph?

Yes

To automate the classification of data entities for your cybersecurity model, you can use spaCy—an industrial-strength NLP library—to scan unstructured text (like project descriptions or server logs) and automatically assign sensitivity labels.

Since you are a NASA subcontractor and AI applier, this script is designed to bridge the gap between "raw text" and "structured knowledge graph nodes" by identifying keywords that trigger specific compliance standards (e.g., CUI for NIST 800-171/CMMC).

Python Script: Automated Sensitivity Tagger

This script uses a rule-based Entity Ruler in spaCy. This is more reliable than pure machine learning for compliance because it allows you to define "Hard Rules" for what constitutes sensitive data.

import spacy
from spacy.pipeline import EntityRuler

def get_cyber_nlp():
    # Load a small English model
    nlp = spacy.load("en_core_web_sm")
    
    # Define our classification rules (The "Standards" layer)
    # GPE = Global Political Entity, ORG = Organization (Built-in)
    # We add custom labels: SENSITIVE_DATA, COMPLIANCE_STD
    ruler = nlp.add_pipe("entity_ruler", before="ner")
    
    patterns = [
        # Data Sensitivity Patterns
        {"label": "SENSITIVE_DATA", "pattern": [{"LOWER": "cui"}]},
        {"label": "SENSITIVE_DATA", "pattern": [{"LOWER": "proprietary"}]},
        {"label": "SENSITIVE_DATA", "pattern": [{"LOWER": "personally"}, {"LOWER": "identifiable"}]},
        
        # Compliance Standard Patterns
        {"label": "COMPLIANCE_STD", "pattern": [{"LOWER": "nist"}, {"TEXT": {"REGEX": "800-\\d+"}}]},
        {"label": "COMPLIANCE_STD", "pattern": [{"LOWER": "cmmc"}]},
        {"label": "COMPLIANCE_STD", "pattern": [{"LOWER": "iso"}, {"TEXT": "27001"}]}
    ]
    
    ruler.add_patterns(patterns)
    return nlp

# --- EXAMPLE USAGE ---
nlp = get_cyber_nlp()

# Sample unstructured text from a project description or log
raw_text = "The Gateway Project contains CUI and must be protected under NIST 800-171 standards."

doc = nlp(raw_text)

print(f"{'Entity':<20} | {'Label':<15}")
print("-" * 40)

for ent in doc.ents:
    print(f"{ent.text:<20} | {ent.label_:<15}")

# Logic to feed into your Pyvis Graph:
# if ent.label_ == "SENSITIVE_DATA":
#    node_color = "#ff4d4d" (Red)
# elif ent.label_ == "COMPLIANCE_STD":
#    node_color = "#00ff87" (Green)

Why this approach works for your projects:

Context Over Match: By using spaCy's Token attributes (like LOWER or REGEX), you avoid the "False Positive" traps of simple string matching.
Mapping to the Graph: Each extracted entity with a label like SENSITIVE_DATA can be automatically converted into a Data node in your Neo4j or Pyvis graph.
Agentic Ready: You can wrap this in a function that your AI Agent calls whenever it "reads" a new document, allowing the Knowledge Graph to grow and self-classify without manual entry.

Suggested Next Step

Since you work with NASA S&MA, would you like me to help you refine the regex patterns to specifically catch NASA-specific classification markings (like SBU, Export Controlled, or specific ITAR clauses) to automate your compliance modeling?

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

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