One of the topics I’ve been working on a bit is using AI to make Mermaid chart diagrams for threat modeling and visualization. I asked OpenAi’s deep research to write me a blog post on the subject and here are the results!
Introduction
Intelligence professionals – from OSINT investigators to military and law enforcement analysts – often need to turn complex information into clear visuals. Charts and diagrams can reveal patterns and connections in data that might be missed in text form (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations). For example, link analysis charts map relationships between people, places, and things, making hidden networks visible (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations). Traditionally, creating such visuals required specialized software or painstaking manual drawing. But recent advances in AI and tools like Mermaid have made it much easier. Now, you can simply describe the scenario you want to visualize, and let an AI assistant (like ChatGPT or Anthropic’s Claude) generate a diagram for you in Mermaid’s syntax. This blog post will explain what Mermaid is, how it works, and how AI-powered tools can help intelligence analysts quickly create diagrams – from link charts and flowcharts to timelines and decision trees – using just text prompts. We’ll walk through examples in open-source intelligence, cyber threat intel, law enforcement/military, and competitive intelligence, all in plain, non-technical language.
What is Mermaid?
Mermaid is an open-source JavaScript-based diagramming and charting tool that uses text to generate diagrams. In other words, you write out a description of a chart in a simple, markdown-like text syntax, and Mermaid renders it as a visualization (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). Because it’s text-based, Mermaid lets you create flowcharts, sequence diagrams, graphs of linked nodes, timelines, and more by typing, rather than drawing shapes by hand. It “renders Markdown-inspired text definitions to create and modify diagrams dynamically” (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). This means the diagrams are easy to update or version-control – you can edit the text and instantly get an updated chart. Mermaid supports a wide range of diagram types (flowcharts, organizational charts, sequence diagrams, state machines, Gantt charts, pie charts, etc.) with a relatively simple syntax (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). Each element in the diagram (like a box, circle, or arrow) is represented by a line of text following Mermaid’s rules. For example, a basic flowchart in Mermaid might be written as:
flowchart LR
A[Start] --> B[Investigation]
B --> C[Conclusion]
This text would produce a simple left-to-right flowchart with three boxes connected by arrows. You don’t need to use a mouse or graphics editor – just writing the description is enough. Because Mermaid diagrams are stored as plain text, they are lightweight and portable. The output is usually a vector graphic (SVG), so it scales without losing clarity (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). Mermaid’s openness and portability are big advantages: you’re not locked into a proprietary tool, and you can easily embed diagrams in reports, wikis, or presentations that support the format. In short, Mermaid turns your written instructions into visuals, which is perfect for analysts who might prefer working in a text environment or need to generate visuals on the fly.
How AI Tools Can Generate Diagrams from Description
Writing Mermaid syntax is simpler than drawing a diagram manually, but you still have to learn the right keywords and format. This is where AI assistants like ChatGPT (by OpenAI) or Claude (by Anthropic) come in. These AI tools can understand a natural language prompt and produce the Mermaid code for the diagram you described. In other words, you can tell the AI what you want to see – using everyday language – and it will write the text diagram for you. In one example, a LinkedIn author simply described an OAuth architecture diagram, and ChatGPT generated the Mermaid code to create it (Crafting Diagrams with ChatGPT 3.5 and Mermaid) (Crafting Diagrams with ChatGPT 3.5 and Mermaid). With the help of such AI, you can “quickly generate Mermaid code for any diagram you need” (Crafting Diagrams with ChatGPT 3.5 and Mermaid).
How does this work in practice? Here’s the typical workflow:
- Describe your diagram in plain language. For instance, an investigator might say to the AI: “Draw a network chart with Alice and Bob both connected to the same address 123 Maple Street.”
- The AI produces Mermaid syntax. The assistant (ChatGPT or Claude) will output text like a Mermaid code block that corresponds to the described chart. For the above request, the AI might output something like:
flowchart LR Alice[Alice] -- "Lives at" --> Address[123 Maple Street] Bob[Bob] -- "Lives at" --> AddressThis is Mermaid code representing two person nodes (Alice and Bob) both pointing to an “Address” node labeled 123 Maple Street. Don’t worry if you’re not familiar with the syntax – the AI writes it for you. - Render or view the diagram. You can take the Mermaid code that the AI provided and paste it into a Mermaid viewer (such as the Mermaid Live Editor or a compatible platform like Obsidian, VS Code, etc.) to see the actual diagram. Some AI integration plugins can even render the image directly. In our example, the code above would show a simple link chart where Alice and Bob have arrows to the same address node, visually confirming they share that location.
- Refine if necessary. If the diagram isn’t exactly what you envisioned, you can tweak the text or ask the AI to adjust it. For example, you might ask for a different layout direction or add another entity. The AI can quickly iterate, saving you the effort of adjusting dozens of shapes manually. As one user described using Claude to build Mermaid timelines and then refining them, “It’s like magic.” (How do I make a timeline like this in Obsidian? : r/ObsidianMD) With AI doing the heavy lifting of syntax, the hardest part – getting started – is no longer a barrier ( Mermaid Chart – Create complex, visual diagrams with text. A smarter way of creating diagrams. ).
This AI-assisted approach offers a few big benefits. Speed is one: what might have taken hours to draw by hand can be done in minutes (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations). You spend far less time clicking and coding, and more time making sure the diagram communicates the right information ( Mermaid Chart – Create complex, visual diagrams with text. A smarter way of creating diagrams. ). Another benefit is accessibility – you don’t have to be a developer or graphic designer to create useful diagrams. Even if you’ve never used Mermaid before, an AI can bridge that gap. The AI essentially acts as a translator, converting your analyst’s thinking into a visual format. And because the output is text-based, it’s easy to adjust or update when new information comes in. Next, let’s explore specific examples of how this can enhance different intelligence workflows.
Examples in Open-Source Intelligence (OSINT)
Open-Source Intelligence analysts often map out relationships between entities – people, organizations, phone numbers, addresses, social media accounts, and so on. These link analysis charts are a core OSINT technique, used to visualize connections and uncover hidden networks (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations). A link analysis chart consists of nodes (representing entities like a person or an object) and links (lines or arrows representing relationships). The visual format is crucial because it allows investigators to “see” relationships that would be missed in raw data (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations). Traditionally, tools like i2 Analyst’s Notebook or Maltego are used for this purpose, but those can be expensive or have a steep learning curve. Mermaid offers a lightweight alternative to sketch out such networks in text form, and AI can help create those sketches instantly from your descriptions.
(Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations) Example of a simple link analysis chart connecting a person to various related entities (phone number, social media accounts, license plate, etc.). Link charts use nodes and connecting lines to show how different data points relate to a central subject (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations).
Imagine an OSINT investigator is looking at two individuals who might be connected through a shared piece of information. In a manual scenario, the analyst would list out all known data points and then draw lines between matching items. Instead, they could simply ask an AI to generate a Mermaid link chart. For example, the prompt might be: “Show a diagram of Alice and Bob, indicating if they share any addresses or contacts. Alice lives at 123 Maple Street. Bob also lives at 123 Maple Street.” From this description, the AI can produce Mermaid code for a network graph. We saw an example earlier, but here’s how that Mermaid code might look and what it represents:
flowchart LR
Alice[Alice] -- "Lives at" --> Address[123 Maple Street]
Bob[Bob] -- "Lives at" --> Address
In this Mermaid diagram code, we have defined two person nodes (“Alice” and “Bob”) and one node for the shared address. The -- "Lives at" --> notation means an arrow labeled “Lives at” connects the person to the address. If we render this, it shows Alice and Bob each pointing to the same address node. This simple visual immediately highlights the connection (a shared residence) between the two individuals, which could be a crucial link in an investigation. An OSINT analyst could obtain this code from ChatGPT and paste it into a report or tool that supports Mermaid, getting a clean link chart without manually drawing a thing.
This is a basic example, but you could ask the AI to include many more nodes and links – perhaps adding a phone number node that one of them uses, an employer node that they both have ties to, etc. The AI can scale the diagram as your description provides more entities. The result is an automated link analysis sketch. You still control the narrative (by describing which connections to visualize), but you offload the tedious part of arranging it into a chart. Because it’s text, you can easily edit names or add a new node by editing the Mermaid code, either by hand or by asking the AI to update it. This dramatically speeds up the cycle of hypothesizing connections and visualizing them. What used to be a painstaking manual process can become almost conversational – “let’s see how X connects to Y” – and the AI delivers the diagram. It’s important to double-check the output for accuracy, of course, but even if minor tweaks are needed, the heavy lifting is done. By leveraging AI-generated Mermaid diagrams, OSINT investigators can quickly produce link charts to include in their intelligence reports, briefing others with clarity and visual impact.
Examples in Cyber Threat Intelligence
Threat intelligence and cybersecurity teams also rely on diagrams to understand and communicate complex scenarios. For example, analysts might draw an attack chain or kill chain diagram to depict the stages of a cyber attack, or use an attack tree to break down all the possible ways an attacker could exploit a system. (Attack trees are a modeling technique used to represent the potential sources, methods, and outcomes of an attack (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D).) Mermaid is quite capable of representing these structures with flowchart syntax or specialized diagrams, and AI assistance can make generating them much faster.
(Developing Attack Trees with Mermaid – Ryan Straight, Ph.D) An example of an attack tree diagram created with Mermaid, illustrating the branches of possible attack methods in a cybersecurity scenario (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). Each node is an attack step or condition, and the tree shows how an attacker could proceed through different paths.
Consider the classic Lockheed Martin Kill Chain – a sequence of stages that a cyber intrusion might go through (Reconnaissance, Weaponization, Delivery, Exploitation, Installation, Command & Control, Actions on Objectives). An analyst might want to include a simplified kill chain diagram in a threat briefing to illustrate where an attack was detected or stopped. Instead of manually crafting this diagram, the analyst can prompt an AI to “Generate a flowchart of the cyber kill chain stages.” The AI, having knowledge of the kill chain model, can output Mermaid code for the sequence. For instance, it might produce something like:
flowchart TD
Recon[Reconnaissance] --> Weaponize[Weaponization] --> Deliver[Delivery] --> Exploit[Exploitation] --> Install[Installation] --> C2["Command & Control"] --> Actions["Actions on Objectives"]
This Mermaid snippet defines a top-down flowchart (as indicated by TD) that flows through each phase of an attack in order. If you render it, you would see a vertical chain of boxes: Reconnaissance → Weaponization → Delivery and so on, each arrow pointing to the next stage. Such a diagram helps convey the progression of an attack at a glance. With AI generating the code, the threat intel analyst doesn’t need to recall the exact Mermaid syntax (--> for an arrow, handling the spaces in “Command & Control”, etc.); the AI handles those details. The analyst can then quickly mark, say, the stage at which the threat was detected, or color a particular node (Mermaid allows styling via class definitions) if needed, by adding a line or two – again something the AI could assist with if asked.
Beyond linear flows, threat intelligence work often involves more complex branching logic. Attack trees, like the one shown in the image above, are essentially decision trees of how an attack can unfold. Creating those by hand can be time-consuming, but an AI can produce the Mermaid structure if you describe the tree. For example, you might feed the AI a structured description of an attack tree (e.g., “Node A (Attack goal); from A it can happen via B (Method1) or C (Method2); B can lead to D or E…”) and it can output the corresponding Mermaid code with the proper branching. One cybersecurity blogger demonstrated exactly this, using Mermaid to build attack trees for different scenarios (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D) (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). AI can accelerate that by handling the syntax and even suggesting labels.
Threat intel analysts also use diagrams like network infrastructure maps (which could be drawn with Mermaid’s graph syntax), process flowcharts for incident response procedures, or timeline of breach events (more on timelines shortly). All of these can be generated or at least first-drafted by an AI assistant. The benefit is that an analyst can translate their analytical insights directly into a visualization without getting bogged down by drawing tools. For example, if investigating a phishing campaign, one could ask for a sequence diagram showing “User -> clicks link -> triggers malware -> alerts SOC”, and get a Mermaid sequence diagram outline from the AI, then refine it. This way, threat intelligence findings can be communicated not just in lengthy text, but accompanied by neat diagrams that the AI helped produce.
Examples in Law Enforcement and Military Intelligence
Professionals in law enforcement, the military, and defense intelligence communities also regularly use visual aids to track cases, understand events, or plan operations. Two very useful types of diagrams for these domains are timelines and organizational or hierarchy charts. Mermaid supports both (with a timeline syntax for chronological sequences and flowcharts that can depict hierarchies or processes), and AI can assist in generating them.
A timeline diagram is invaluable for investigative work – it lays out events in the order they occurred, helping analysts and stakeholders see the sequence and timing. For instance, a police detective might compile a timeline of a suspect’s activities on the days around a crime, or an intelligence officer might create a timeline of significant events leading up to a geopolitical incident. If the information is gathered (dates and descriptions of events), an AI can turn it into a Mermaid timeline chart with a simple prompt. The user might say: “Create a timeline of a case: 2023 – case opened; 2024 – suspect identified and evidence collected; 2025 – trial starts.” The AI could generate something like:
timeline
title Case Investigation Timeline
2023 : Case opened
2024 : Suspect identified : Evidence collected
2025 : Trial starts
This Mermaid code describes a timeline with a title and events. The year 2024 in this example has two events (Mermaid allows multiple events in the same time period separated by a colon). When rendered, it would show a timeline bar with markers at 2023, 2024, 2025, and text for the events at each point. Such a diagram makes it easy to grasp the flow of the investigation – e.g., a long gap between case opening and suspect identification might raise questions, or the timeline might show a flurry of activity in 2024. An intelligence analyst who isn’t familiar with Mermaid’s timeline syntax doesn’t have to worry; by giving an AI the list of events, they get back the code ready to visualize. In fact, enthusiasts in the community have noted that they use AI (like Claude) to iterate on Mermaid timelines and then import them into their analysis notes, finding the process incredibly smooth (How do I make a timeline like this in Obsidian? : r/ObsidianMD).
Beyond timelines, law enforcement and military analysts often map hierarchies or chains of command. For example, mapping a criminal organization’s hierarchy (who reports to whom), or a military unit’s organizational structure, or even a flowchart of decision authority in a command-and-control process. Mermaid doesn’t have a dedicated “org chart” format, but you can easily use a flowchart in a top-down orientation to depict a hierarchy. For instance, one could ask: “Show a command hierarchy: General -> Colonel -> Captain -> Soldiers.” The AI might produce a Mermaid flowchart like:
flowchart TD
General[General] --> Colonel[Colonel]
Colonel --> Captain[Captain]
Captain --> Trooper[Soldier]
Rendered, that would show a pyramid-like chart with an arrow from General to Colonel, Colonel to Captain, and Captain to Soldier – effectively an org chart. Similarly, for a criminal network, one could have the boss at top and underlings branching below. AI can help format these and even apply styling (perhaps making the top node a different shape, etc., as Mermaid allows different node shapes using syntax like [] for boxes or () for circles). The key is that an analyst just describes the relationships, and the AI writes the diagram text.
Another use case is process flowcharts for standard operating procedures or decision-making protocols. For example, law enforcement might have a decision tree for how to proceed with an investigation depending on evidence: “If evidence is strong, proceed to arrest; if not, continue surveillance or drop case”. This can be visualized as a decision flowchart. Instead of drawing the decision tree manually, an AI can create it. A prompt might be: “Draw a decision tree: start with ‘Evidence sufficient?’, if yes -> make arrest, if no -> gather more evidence.” The AI could return:
flowchart TD
Q{Evidence sufficient?} -->|Yes| A[Make Arrest]
Q -->|No| B[Gather More Evidence]
Here, the {} in Mermaid denotes a decision (usually drawn as a diamond shape), and the |Yes| and |No| on the arrows label the branches. This yields a simple decision chart that an investigator can follow. Military planners could do similar things for go/no-go decisions in operations or rules of engagement flowcharts. Again, the advantage is speed and clarity – the analyst focuses on the logic and content, while the AI handles the correct diagram syntax and layout.
In all these cases, whether timelines or flowcharts, AI-generated Mermaid diagrams help law enforcement and DoD analysts turn information into a visual story quickly. They can embed these diagrams in reports to superiors, briefing decks, or case files. Because the diagrams are text-defined, they’re also easy to adjust as new intel comes in (e.g., add a new event to the timeline, change a relationship in the hierarchy) – which the AI can do just by a new prompt or the analyst can edit manually. This flexibility and agility in creating visuals can enhance understanding and communication in time-sensitive situations typical of military and law enforcement operations.
Examples in Competitive and Business Intelligence
Competitive intelligence analysts and business intelligence professionals also benefit from visualizing information. They might not be mapping criminal networks or cyber attacks, but they do map competitive landscapes, organizational relationships, timelines of market events, and strategic decision trees. Mermaid diagrams, assisted by AI, can be a quick way to generate these business-focused visuals as well.
For instance, a competitive intelligence analyst might create a timeline of competitor moves – product launches, mergers, marketing campaigns – to see trends over time. Using AI, they can list those events (with dates) and get a Mermaid timeline to illustrate, say, how Competitor X’s major actions were spaced out. Or they might want to depict a market ecosystem, linking a company to its suppliers, partners, and customers in a network diagram (similar to an OSINT link chart, but for businesses). By describing the companies and their relationships (“A partners with B; B and C are suppliers to D”), the AI can generate a Mermaid flowchart that maps out the ecosystem.
Another common need is visualizing decision processes or strategy options. Business analysts often use decision trees to evaluate possible actions and outcomes. With AI and Mermaid, they can quickly sketch these out. Suppose a business strategist wants to diagram the decision logic for responding to a competitor’s pricing change. They could prompt: “Our competitor might cut prices. Diagram the decision tree where if competitor cuts price, we either reduce our price or add value; if competitor doesn’t cut price, we continue as is.” The AI can respond with Mermaid code for that scenario:
flowchart TD
Q{Competitor cuts prices?} -->|Yes| R[Consider price drop or added value]
Q -->|No| S[Maintain current pricing strategy]
This produces a simple yes/no decision chart. The diamond (decision node) asks if the competitor cuts prices. On the “Yes” branch, it leads to a step to consider a counter-strategy (dropping prices or adding value), and on the “No” branch, it says to stay the course. Such a diagram makes it easier to discuss strategy in meetings because everyone can see the fork in the road and the planned responses. An AI-generated diagram like this saves an analyst from having to fiddle with shapes in PowerPoint or Visio – they get a clean, consistently formatted chart from just a description.
Competitive intelligence often overlaps with organizational analysis too – e.g. charting a competitor’s org structure or key leadership (which, as mentioned, can be done with Mermaid flowcharts), or mapping relationships between industry entities (a kind of business link analysis). All these can be articulated in natural language and converted to Mermaid visuals by an AI.
One more example: mind maps or concept maps for brainstorming industry factors. Mermaid has a mindmap syntax as well. If an analyst wants to brainstorm factors affecting their market (like a central node with branches for “Regulations”, “Technologies”, “Competitors”, each of which branch further), they could list those hierarchically and have the AI format it as a Mermaid mindmap. This could aid in presentations or reports to show the analyst’s thinking structure.
The bottom line for business and competitive intelligence is that AI + Mermaid offers a quick way to produce polished diagrams that can clarify strategic information. Instead of relying solely on slide decks with bullet points, an analyst can include a machine-generated graphic – for example, a timeline of competitor activities with minimal effort, or a decision flow that can be easily adjusted as new scenarios arise. This not only saves time but can lead to better strategic communication, as visuals often convey scenarios and choices more effectively than text alone.
Conclusion
The ability to generate visual diagrams from simple text prompts is a game-changer for intelligence professionals. AI tools like ChatGPT and Claude serve as smart assistants that can take an analyst’s description and output a Mermaid diagram script, essentially acting as a “diagrammer on demand.” We’ve seen how this can apply across domains: OSINT analysts can quickly produce link charts to connect the dots in investigations, threat intelligence teams can sketch attack flows or threat models in moments, law enforcement and military personnel can lay out timelines or decision trees during fast-moving operations, and business intelligence analysts can map out competitive landscapes and strategies with ease. By leveraging AI to handle the Mermaid syntax, analysts free themselves to focus on what needs to be shown rather than how to draw it.
Mermaid’s text-based approach pairs naturally with AI because both speak the language of plain text. As a result, even those who have “never heard of Mermaid” can start using it with virtually no learning curve – the AI will guide the way by generating correct syntax. The diagrams produced are clean, consistent, and easily editable. Another big advantage is that everything remains transparent and reproducible. Since Mermaid diagrams are essentially code, they can be versioned, shared, or even merged like any other text. An intelligence report can include the Mermaid source, ensuring that others can regenerate or modify the graphic later if needed. There’s no black box – the AI’s output is human-readable and can be fine-tuned.
Of course, it’s wise to review any AI-generated output. In some cases, the AI might need a nudge or a correction to get the diagram just right (for example, fixing a label or adjusting an orientation). The good news is that these adjustments are usually minor. As one guide noted, if the first attempt isn’t perfect, you can treat it as a rough starting point and refine it by hand or with another AI prompt (The ChatGPT prompt list for Software Engineers: Prompts to generate software diagrams in Mermaid | Medium). Even with occasional tweaks, you’ve saved significant effort compared to drawing from scratch.
In summary, by combining generative AI with Mermaid’s versatile chart syntax, intelligence professionals have a powerful new way to think visually. Complex webs of data can be translated into diagrams with a simple conversation with an AI, accelerating analysis and improving how findings are communicated. Whether you’re mapping a criminal network, outlining a cyber incident, charting a timeline of events, or weighing business decisions, AI-generated Mermaid diagrams can become an everyday part of your workflow. They allow you to see the story in the data without the traditional hassle of manual diagramming. This synergy of AI and Mermaid is democratizing the creation of analytical visuals – making it faster, smarter, and accessible to all, no matter your technical background. So next time you’re struggling to picture the connections in your data or explain a process, remember: you can ask the AI to draw it for you, and watch a Mermaid diagram bring your intelligence insights to life. ( Mermaid Chart – Create complex, visual diagrams with text. A smarter way of creating diagrams. ) (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D)
Sources: The information and examples in this post are based on a combination of the author’s experience and various references. Mermaid is described in its official docs and related tutorials (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D) (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). The benefits of using Mermaid (open-source, text-based) are highlighted by Ryan Straight (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). OSINT link analysis concepts are explained by Skopenow (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations) (Uncovering Connections: How Link Analysis Can Streamline OSINT Investigations). The use of ChatGPT to generate Mermaid code was demonstrated by Vinay Abhyankar (Crafting Diagrams with ChatGPT 3.5 and Mermaid) (Crafting Diagrams with ChatGPT 3.5 and Mermaid), and Mermaid’s creator emphasized how AI accelerates diagram creation ( Mermaid Chart – Create complex, visual diagrams with text. A smarter way of creating diagrams. ) ( Mermaid Chart – Create complex, visual diagrams with text. A smarter way of creating diagrams. ). A Reddit user also shared their success using Claude to make Mermaid timelines (How do I make a timeline like this in Obsidian? : r/ObsidianMD). Cybersecurity attack trees and their Mermaid implementation were discussed by Ryan Straight (Developing Attack Trees with Mermaid – Ryan Straight, Ph.D). These sources collectively show the potential of AI-assisted Mermaid diagramming across intelligence fields.
