#4166 Knowledge Graph: Cybersecurity

Describe how a knowledge graph for Cybersecurity could be useful to individuals and businesses to learn and apply cybersecurity
YES :: Would you like to draft a basic ontology (defining the core nodes and relationships) for a specific cybersecurity domain, such as cloud infrastructure or application security?
YES :: Would you like me to write a Python script using the pyvis library to generate and visualize this specific Application Security ontology in an interactive HTML format?
What is "infrastructure-as-code"?
Could this knowledge graph plus a knowledge graph of the business IT framework be used together to help identify and fight cyber attacks?
Provide a business and project plan for implementing this combined cybersecurity knowledge graph framework for a business
Describe how this setup would be used to constantly audit the business infrastructure for cybersecurity vulnerabilities

#4166 Describe how a knowledge graph for Cybersecurity could be useful to individuals and businesses to learn and apply cybersecurity

A Cybersecurity Knowledge Graph (CKG) transforms fragmented security data—like isolated alerts, static vulnerability lists, and disconnected asset inventories—into a unified, semantic network. By mapping nodes (assets, vulnerabilities, threat actors, controls) and their edges (relationships, attack paths, mitigation links), a CKG becomes a powerful engine for both conceptual understanding and automated defense.

Here is how a CKG serves both individuals learning the field and businesses actively defending their infrastructure.

For Individuals: Learning and Upskilling

For someone mastering cybersecurity, the sheer volume of acronyms, frameworks, and disparate concepts can be overwhelming. A CKG shifts the learning process from rote memorization to relational understanding.

Contextualizing Threats: Instead of reading a flat description of a CVE (Common Vulnerabilities and Exposures), an individual can traverse the graph to see exactly how it works. They can trace the path from a specific software version (Node: Asset) to its weakness (Node: Vulnerability), the exploit used (Node: Exploit), the threat actors known to use it (Node: APT Group), and the required mitigation (Node: Patch/Control).
Navigating Frameworks: Frameworks like MITRE ATT&CK or NIST are naturally graph-structured. A learner can use the CKG to map a high-level tactic (e.g., "Credential Access") down to specific techniques, procedures, and the specific log events required to detect them.
Building Mental Models: By querying the ontology, learners can visually or programmatically explore how different domains intersect—such as how a social engineering tactic directly correlates to an identity and access management (IAM) misconfiguration.

For Businesses: Application and Active Defense

In an enterprise environment, a CKG moves security from reactive alert-chasing to proactive, context-aware defense. It provides the semantic layer necessary for advanced automation and reasoning.

Automated Attack Path Analysis: Traditional vulnerability scanners prioritize based on CVSS scores, which lack business context. A CKG allows a business to run graph queries (e.g., using Cypher or SPARQL) to find realistic attack vectors. A query can instantly reveal if a low-severity vulnerability exists on a development server that has an open SSH connection to a mission-critical database containing PII.
Powering Agentic AI and RAG: LLMs and autonomous security agents require high-quality context to be reliable. A CKG serves as the perfect grounding data for Retrieval-Augmented Generation (RAG). When a security alert triggers, an AI agent can query the graph to pull the exact blast radius of the affected asset, its owner, and related threat intelligence, presenting a fully contextualized summary to a human analyst or taking automated remediation steps.
Dynamic Asset and Risk Management: Businesses struggle to maintain accurate CMDBs (Configuration Management Databases). A CKG can ingest data from cloud providers, endpoint detection systems, and identity providers to create a real-time, interconnected map of the attack surface, automatically recalculating risk scores as new nodes (like an unpatched laptop) connect to the network.
Semantic Search for Threat Hunting: Security operations centers (SOCs) can execute complex semantic searches across their telemetry. Instead of writing complex SQL joins across different log databases, analysts can query the graph for behavioral patterns, such as "Find all internal endpoints communicating with external IP addresses recently flagged for malware hosting."

By treating cybersecurity as a web of relationships rather than a checklist of issues, a knowledge graph provides the structural foundation needed for continuous learning and highly automated, intelligent defense systems.

Would you like to draft a basic ontology (defining the core nodes and relationships) for a specific cybersecurity domain, such as cloud infrastructure or application security?

Yes

Let's focus on Application Security and the Software Supply Chain. This is a highly relevant domain right now, especially when building out complex software architectures, integrating various libraries, or deploying AI-driven chat applications where data flow and dependencies are critical.

Here is a foundational ontology—the blueprint defining the core entities (nodes) and how they interact (edges)—for mapping application security.

1. Core Nodes (Entities)

These are the nouns of the knowledge graph. In a graphical database, these would be the distinct object labels.

Application / Service: The proprietary software being built or run (e.g., ChatSequenceApp, AuthService).
Component / Library: Third-party open-source packages, frameworks, or APIs used by the application (e.g., PyYAML v5.3.1, OpenAI API).
Vulnerability: A specific, identified weakness, usually tracked by standard identifiers (e.g., CVE-2024-1234, CWE-79: Cross-Site Scripting).
Threat Actor: An entity (individual, group, or automated botnet) attempting to exploit a vulnerability (e.g., APT29, Automated Scanner).
Environment: Where the application is running (e.g., Production Server, Local Jupyter Sandbox).
Security Control: The mechanism implemented to reduce risk (e.g., Input Sanitization Function, Web Application Firewall (WAF)).
Data Asset: The information the application handles, categorized by sensitivity (e.g., User Session Tokens, Public Metadata).

2. Core Edges (Relationships)

These are the verbs. They define the semantic links that allow for complex querying and reasoning.

[DEPENDS_ON]: Links an Application to a Component. (e.g., ChatSequenceApp DEPENDS_ON PyYAML)
[HAS_VULNERABILITY]: Links a Component or Application to a Vulnerability. (e.g., PyYAML HAS_VULNERABILITY CVE-2024-1234)
[EXPLOITS]: Links a Threat Actor to a Vulnerability.
[MITIGATES]: Links a Security Control to a Vulnerability or Application. (e.g., Input Sanitization MITIGATES CWE-79)
[HOSTS]: Links an Environment to an Application.
[PROCESSES]: Links an Application to a Data Asset.

3. The Ontology in Action (The Attack Path)

When these nodes and edges are populated with real telemetry and architecture data, the graph structure allows you to move away from flat lists of software bugs to tracing actual risk trajectories.

For example, a semantic query could traverse this exact path to evaluate blast radius:

Threat Actor [EXPLOITS] -> CVE-2024-1234 <- [HAS_VULNERABILITY] PyYAML <- [DEPENDS_ON] ChatSequenceApp -> [PROCESSES] User Session Tokens

If a Security Control [MITIGATES] that specific vulnerability, the graph query immediately recognizes the path is blocked, dynamically lowering the risk score of that specific asset without human intervention.

Would you like me to write a Python script using the pyvis library to generate and visualize this specific Application Security ontology in an interactive HTML format?

Yes

Here is a Python script using pyvis to generate an interactive, visual representation of the Application Security ontology we just outlined.

To keep things robust and avoid any iframe or sandbox display restrictions if you decide to run this in a Jupyter environment, this script is configured to generate and save a standalone HTML file that you can open in any web browser.

The Python Script (`app_sec_graph.py`)

from pyvis.network import Network

def create_app_sec_ontology():
    # Initialize a directed network graph with a dark theme
    # cdn_resources='remote' ensures the styling libraries load correctly across environments
    net = Network(height='800px', width='100%', bgcolor='#1e1e1e', font_color='white', directed=True, cdn_resources='remote')

    # Define node categories with specific colors and shapes for visual clarity
    node_styles = {
        "Application": {"color": "#4CAF50", "shape": "hexagon"},       # Green
        "Component": {"color": "#8BC34A", "shape": "box"},             # Light Green
        "Data_Asset": {"color": "#FFC107", "shape": "database"},       # Amber
        "Vulnerability": {"color": "#F44336", "shape": "triangle"},    # Red
        "Threat_Actor": {"color": "#9C27B0", "shape": "dot"},          # Purple
        "Security_Control": {"color": "#2196F3", "shape": "shield"},   # Blue
        "Environment": {"color": "#607D8B", "shape": "cloud"}          # Blue-Grey
    }

    # 1. Add Nodes (Entities)
    # Applications & Environments
    net.add_node("ChatSequenceApp", label="ChatSequenceApp", title="Application / Service", **node_styles["Application"])
    net.add_node("AuthService", label="AuthService", title="Application / Service", **node_styles["Application"])
    net.add_node("Prod_Server", label="Production Server", title="Environment", **node_styles["Environment"])

    # Components
    net.add_node("PyYAML", label="PyYAML v5.3.1", title="Component / Library", **node_styles["Component"])
    net.add_node("OpenAI_API", label="OpenAI API", title="Component / External Service", **node_styles["Component"])

    # Data
    net.add_node("Session_Tokens", label="User Session Tokens", title="Data Asset", **node_styles["Data_Asset"])
    
    # Risks & Threats
    net.add_node("CVE-2024-1234", label="CVE-2024-1234\n(Deserialization)", title="Vulnerability", **node_styles["Vulnerability"])
    net.add_node("APT29", label="APT29", title="Threat Actor", **node_styles["Threat_Actor"])

    # Controls
    net.add_node("Input_Sanitization", label="Input Sanitization", title="Security Control", **node_styles["Security_Control"])
    net.add_node("WAF", label="WAF (Web App Firewall)", title="Security Control", **node_styles["Security_Control"])

    # 2. Add Edges (Relationships)
    net.add_edge("ChatSequenceApp", "PyYAML", title="DEPENDS_ON", label="DEPENDS_ON", color="#aaaaaa")
    net.add_edge("ChatSequenceApp", "OpenAI_API", title="DEPENDS_ON", label="DEPENDS_ON", color="#aaaaaa")
    net.add_edge("ChatSequenceApp", "AuthService", title="CALLS", label="CALLS", color="#aaaaaa")
    
    net.add_edge("Prod_Server", "ChatSequenceApp", title="HOSTS", label="HOSTS", color="#aaaaaa")
    net.add_edge("AuthService", "Session_Tokens", title="PROCESSES", label="PROCESSES", color="#aaaaaa")
    
    net.add_edge("PyYAML", "CVE-2024-1234", title="HAS_VULNERABILITY", label="HAS_VULNERABILITY", color="#F44336")
    net.add_edge("APT29", "CVE-2024-1234", title="EXPLOITS", label="EXPLOITS", color="#F44336")
    
    net.add_edge("Input_Sanitization", "CVE-2024-1234", title="MITIGATES", label="MITIGATES", color="#2196F3")
    net.add_edge("WAF", "ChatSequenceApp", title="PROTECTS", label="PROTECTS", color="#2196F3")

    # Add physics for a nice layout
    net.repulsion(node_distance=150, central_gravity=0.2, spring_length=200, spring_strength=0.05, damping=0.09)

    # Generate and save the interactive HTML file
    output_file = "app_sec_ontology.html"
    net.save_graph(output_file)
    print(f"Knowledge Graph successfully generated and saved to '{output_file}'.")

if __name__ == "__main__":
    create_app_sec_ontology()

How to use this:

Make sure you have the library installed: pip install pyvis
Run the script.
Open the resulting app_sec_ontology.html file in your browser. You'll be able to click, drag, and zoom in on the nodes to see how the attack path and mitigations map out visually.

(Note: If you do adapt this to run inline within a Jupyter Notebook later, just switch to net = Network(notebook=True, cdn_resources='remote', ...) and use net.show('app_sec_ontology.html') to ensure it renders correctly in the cell output).

Would you like to expand this graph to include infrastructure-as-code (IaC) components, or keep focusing on the software layer?

What is "infrastructure-as-code"?

Infrastructure as Code (IaC) is the practice of managing and provisioning computing infrastructure—like servers, databases, and networks—using machine-readable configuration files or code, rather than manually configuring hardware or clicking through graphical user interfaces.

Instead of a system administrator logging into a cloud provider's dashboard to manually spin up a server, configure a firewall, and attach a database, all of those requirements are written down in a script or declarative file.

Here is a breakdown of how it works and why it is a foundational concept in modern computing and security.

How It Works

With IaC, your infrastructure is treated exactly like software source code.

Declarative vs. Imperative: Most IaC tools (like Terraform or AWS CloudFormation) are declarative. You write code that states the desired end-state (e.g., "I need a server with 16GB of RAM and a database"). The IaC tool figures out the steps required to make that happen.
Execution: An automation engine reads the code and makes the API calls to the cloud provider (AWS, Azure, Google Cloud) to build, modify, or destroy the resources exactly as specified.

Why It Matters

Shifting from manual setups to IaC provides several massive advantages:

Speed and Automation: You can deploy an entire complex application environment in minutes with a single command, rather than spending days doing it manually.
Consistency and Repeatability: Manual configuration is prone to human error (the "it works on my machine" problem). IaC ensures that your development, testing, and production environments are identical.
Version Control: Because the infrastructure is just text files, it can be stored in a version control system like Git. If a change breaks the system, you can instantly roll back to the previous, working configuration.

How IaC Connects to Our Cybersecurity Graph

When infrastructure becomes code, security can also become code. This is where it gets highly relevant to the knowledge graph we were building:

Because servers and networks are defined in text files before they exist, security tools can scan those files for misconfigurations. For example, if an IaC script attempts to create a cloud storage bucket with public read access, a scanner can catch it.

In our graph ontology, we could map this by adding nodes like:

Node: Terraform Script (Component)
Node: Public S3 Bucket (Asset)
Edge: Terraform Script [PROVISIONS] Public S3 Bucket
Edge: Public S3 Bucket [HAS_VULNERABILITY] Misconfiguration: Public Access

This allows a business to query the graph and stop a deployment before the vulnerable environment is ever built.

Would you like to see how we could add an IaC node and relationship to our pyvis Python script to visualize this pre-deployment risk?

Could this knowledge graph plus a knowledge graph of the business IT framework be used together to help identify and fight cyber attacks?

Yes, absolutely. In fact, merging a Cybersecurity Knowledge Graph with a Business IT/Enterprise Architecture Graph is essentially the "holy grail" of modern proactive defense.

When you fuse these two domains together, you move from just collecting data to creating a highly intelligent Security Digital Twin of the organization.

Here is exactly how they interlock and the capabilities they unlock when combined.

The Pivot Point: Where the Graphs Merge

The two graphs naturally overlap at the Asset and Identity layers.

The IT Architecture Graph (The "What" and "Why"): Maps the business side. It contains nodes like Business Capability (e.g., Payroll), Employee, Application, Server, and Data Flow.
The Cybersecurity Graph (The "How" and "Who"): Maps the threat landscape. It contains nodes like Threat Actor (e.g., APT29), Vulnerability (CVEs), Exploit Technique, and Security Control.

The integration happens because a node like AuthService_Server exists in both worlds. It is simultaneously an IT asset supporting a business function and a potential target harboring a vulnerability.

The Superpowers of the Merged Graph

When these graphs are combined, security teams gain three distinct advantages that are impossible to achieve with flat databases or isolated security tools.

1. True "Blast Radius" and Context-Aware Prioritization A standard vulnerability scanner might output an alert: "Critical CVSS 9.8 Vulnerability found on Server XYZ." If an enterprise has 5,000 of these alerts, patching them all immediately is impossible.

The merged knowledge graph provides immediate, automated triage. A query can ask: "Show me the path from this vulnerability to any critical business function." If the graph reveals that Server XYZ is a sandbox environment with no connection to production data, the priority drops. If it reveals that Server XYZ supports the core payment processing application and is connected to a highly privileged identity, it becomes priority number one.

2. Algorithmic Attack Path Prediction Because the data is structured as a network, you can run mathematical graph algorithms (like Dijkstra's Shortest Path or PageRank) to think like an attacker.

Centrality Analysis: The graph can automatically highlight the "choke points"—servers, routers, or user accounts that have the highest number of paths routing through them. Securing these highly connected nodes disproportionately disrupts an attacker's ability to move laterally.
Simulated Breaches: You can pick a node (e.g., an intern's laptop) and programmatically map out every single possible path an attacker could take to reach the Crown Jewel Database node based on network architecture and known exploits.

3. Powering Agentic AI and GraphRAG If you are developing or applying agentic AI to handle security operations, the merged graph acts as the perfect structural brain. Large Language Models often hallucinate or lack deep organizational context.

By using GraphRAG (Retrieval-Augmented Generation using Graphs), when a security alert fires, an AI agent can query the merged graph to pull exactly what happened, who owns the affected system, what business process is impacted, and what the threat actor is known to do next. The agent can then automatically draft a highly accurate incident report or even execute a quarantine script on the exact firewall protecting that specific business unit.

By treating the business structure and the threat landscape as one massive, interconnected ecosystem, you stop fighting isolated alerts and start defending the actual flow of the business.

Would you like to see what a merged Cypher query (the language used to search graph databases) looks like to find a path from a known threat actor to a critical business capability?

Provide a business and project plan for implementing this combined cybersecurity knowledge graph framework for a business

Here is a structured business and project plan for building and launching a combined IT/Cybersecurity Knowledge Graph framework.

This plan is tailored for a highly experienced software engineer operating as a solopreneur, focusing on creating a low-overhead, high-value AI-centric service specifically designed to protect small-to-medium businesses (SMBs).

Executive Summary

The Concept: "Security Digital Twin as a Service" (SDTaaS). SMBs lack the budget for massive Security Operations Centers (SOCs). This service uses an automated knowledge graph combined with agentic AI to map their business assets, identify vulnerabilities, and calculate true business risk—offering enterprise-grade contextual security at a fraction of the cost. The Edge: Leveraging deep software process engineering and prompt engineering to automate the data ingestion and analysis, keeping operational costs near zero once deployed.

Part 1: The Business Strategy (Go-to-Market)

1. Target Audience & Problem

Target: Small businesses (e.g., law firms, boutique financial services, specialized manufacturing) with 10-100 employees.
Problem: They use fragmented cloud services (Microsoft 365, AWS, local Wi-Fi) and lack visibility into how a compromised email could lead to a breach of client data.
Solution: A lightweight, non-intrusive mapping service that visually and programmatically shows them their risk and prioritizes the exact three things they need to fix today.

2. The Service Tiers

Tier 1: The One-Time Assessment (The "Hook"): A flat-fee service where you ingest their current IT architecture and vulnerability scan data to generate a static Knowledge Graph. You deliver a prioritized "Attack Path Report" highlighting their most critical business risks.
Tier 2: Continuous Monitoring (MRR - Monthly Recurring Revenue): A subscription model. You set up automated pipelines (via APIs) to continuously update their graph. Agentic AI monitors the graph and alerts them only when a vulnerability connects to a critical business asset.

3. Cost Structure (Low CAPEX)

Infrastructure: Utilize open-source graph databases (like Neo4j Community Edition or Apache TinkerPop) and standard cloud hosting.
AI Engine: API costs for LLMs to power the GraphRAG and agentic analysis.
Primary Investment: Your time in coding the data ingestion pipelines, writing robust system requirements, and engineering the AI prompts.

Part 2: The Project Implementation Plan

This execution plan focuses on rapid prototyping and iterative development.

Phase 1: Core Engine Development (Weeks 1-4)

Goal: Build the foundational Graph and AI reasoning loop.

Define the Unified Ontology: Map out the exact nodes and edges needed (combining the Application/IT nodes with CVE/Threat Actor nodes).
Database Setup: Spin up a graph database instance.
Build the "Toy" Network: Create a synthetic, heavily detailed SMB dataset (e.g., a fake accounting firm with laptops, a router, an unpatched server, and customer data) to populate the graph for testing.
Develop the GraphRAG Layer: Write the Python scripts that allow an LLM to translate natural language questions (e.g., "What happens if the receptionist's laptop gets ransomware?") into graph queries (Cypher/SPARQL), execute them, and interpret the results.

Phase 2: Data Ingestion Automation (Weeks 5-8)

Goal: Automate the flow of data into the graph so it requires minimal human intervention.

IT Asset Ingestion: Build API connectors for common SMB tools (e.g., pulling user directories from Google Workspace or Microsoft Entra ID).
Vulnerability Ingestion: Build parsers to ingest standard vulnerability data (e.g., importing automated scan results from tools like Nessus or OpenVAS) and map them directly to the IT assets in the graph.
Data Normalization: Write the processing logic to deduplicate assets and ensure clean, reliable data edges.

Phase 3: Agentic AI Integration & Testing (Weeks 9-12)

Goal: Apply rigorous testing to the AI agents to ensure they don't hallucinate risk.

Agent Deployment: Build autonomous agents tasked with specific routines:
- The Hunter: Continuously queries the graph looking for newly formed shortest paths between internet-facing assets and internal databases.
- The Reporter: Drafts human-readable mitigation steps when a critical path is found.
Process Verification: Apply strict software testing methodologies to the agents. Introduce known vulnerabilities into the test graph and verify the agents catch them and prioritize them correctly against business assets.

Phase 4: Pilot & Launch (Weeks 13-16)

Goal: Validate the service with a real-world client.

Friendly Pilot: Offer the Phase 1 Assessment tier for free to one or two friendly local businesses in exchange for case study data and system stress-testing.
Refine the Output: Ensure the final reports generated by the AI are actionable for non-technical business owners. They shouldn't see Cypher queries; they should see: "Fixing this one router setting protects your entire payroll system."
Launch: Begin marketing the automated assessment to local and niche SMBs.

This architecture leans heavily on upfront software engineering to build a highly scalable, automated backend, allowing you to service multiple clients simultaneously without scaling headcount.

Would you like to drill down into the technical requirements for building the Python data ingestion pipeline, or focus on how to structure the LLM prompts for the GraphRAG component?

Describe how this setup would be used to constantly audit the business infrastructure for cybersecurity vulnerabilities

To transition this framework from a static, one-time snapshot into a continuous auditing engine, you have to treat the business infrastructure as a living system. This shifts the cybersecurity paradigm from periodic compliance checklists to continuous safety and mission assurance.

Instead of waiting for a monthly vulnerability scan, the Knowledge Graph becomes a dynamic state machine. Here is how you architect the setup to constantly audit the environment for emerging risks.

1. The Ingestion Layer: Continuous Telemetry

To maintain an accurate "Security Digital Twin," the graph cannot rely on manual data entry. It must be fed by automated, event-driven pipelines or high-frequency polling.

Cloud & Infrastructure APIs: Scripts continuously poll cloud provider APIs (like AWS Config or Azure Resource Graph) and Identity Providers (like Active Directory/Entra ID). If a new user is created or a new server is spun up, the pipeline instantly generates a new Node and the corresponding Edges (e.g., [OWNS], [HOSTS]).
Agent-Based Telemetry: Endpoint Detection and Response (EDR) tools on employee laptops feed data into the graph. If an employee connects from a new, untrusted IP address or installs unapproved software, the graph updates the state of that specific Laptop node.
Vulnerability Feeds: The system ingests the National Vulnerability Database (NVD) or threat intelligence feeds via API. When a new CVE is published, it drops into the graph as a Vulnerability node, automatically drawing [EXPLOITS] edges to any matching Component nodes already in the system.

2. The Logic Layer: Delta Detection and Graph Triggers

The core auditing mechanism relies on detecting state changes (deltas) within the graph. You do not need to rescan the entire business every minute; you only analyze what changed.

Edge Creation Triggers: The moment a new edge is formed—for instance, when a firewall rule change creates an [EXPOSES_TO_INTERNET] edge connected to an internal Database node—a trigger fires.
Semantic Sanity Checks: You can write automated Cypher or SPARQL queries that run constantly in the background, acting as executable system requirements. For example, a query continuously looks for: (User)-[HAS_ROLE]->(Admin) AND (User)-[LACKS]->(MFA). If that pattern ever exists in the graph, the audit fails in real-time.

3. The Analysis Layer: Agentic AI "Watchdogs"

This is where agentic AI replaces the traditional Tier 1 SOC analyst. You deploy specialized AI agents whose entire job is to monitor specific sub-graphs.

The Attack Path Simulator: Whenever the graph updates (e.g., a new CVE is mapped to a server), this agent automatically runs a pathfinding algorithm (like Dijkstra's) to see if a valid route now exists from the public internet to a critical Data Asset.
The Blast Radius Assessor: If a user account is compromised, this agent immediately queries the graph for every single system that user has an [ACCESS] edge to. It evaluates the sensitivity of those downstream systems to determine the true severity of the compromise.
Contextual RAG Pipelines: When the graph detects an anomaly, it feeds the specific, localized graph context into an LLM using Retrieval-Augmented Generation. The LLM evaluates the technical delta against the business context and decides if it warrants a critical alert or if compensating controls (like a WAF blocking that specific traffic) are already in place.

4. The Response Layer: Automated Remediation

Because the graph understands the relationships, the auditing system can safely initiate automated responses without accidentally taking down critical business functions.

Targeted Isolation: If the graph confirms an attack path from a compromised laptop to the payroll server, an agent can automatically trigger a script to sever the network connection ([COMMUNICATES_WITH]) between those specific nodes at the switch or firewall level, isolating the threat while leaving the rest of the business operational.
Dynamic Ticketing: Instead of sending an alert saying "Patch Server A," the system generates a ticket with the full graph context: "Server A has a new critical CVE. It is currently hosting the Auth Service, which controls access to Customer Data. Patch immediately; attack path is open."

By wiring the business's actual telemetry into a continuously updating graph structure, you build a system that audits itself by design, constantly verifying that the operational reality matches the required security posture.

Would you like to draft a specific set of automated Cypher queries (the "executable system requirements") that a watchdog agent would run continuously against an SMB's network graph?

From <https://gemini.google.com/app/> Google Gemini 3.1 (Pro)

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