Artificial intelligence has made its way to the squad room, the mobile command center, and the evidence lab. Fire investigators across the U.S. are beginning to explore how tools like ChatGPT and generative AI can support their work, from scene documentation to report generation.
But there are also a few pitfalls. One careless prompt could compromise a case. A single oversight could violate CJIS compliance and expose protected data.
So, how do you use AI tools without risking compromising your case or violating compliance?
In this AI fire investigation guide, we’ll take a look at 20 ways to use it safely to boost your productivity.
Table of Contents
20 Safe Ways to Use AI in Fire Investigation
AI can help make the fire investigation process more efficient if you use it well. Here are 20 ways you can incorporate it into your workflow.
- Redact and Preprocess All Case Data
Before feeding anything into an AI tool, even inside a CJIS-compliant system, you must scrub your data clean. This means removing or anonymizing names, case numbers, geographic coordinates, license plates, photos of identifiable individuals, and anything else that could constitute personally identifiable information (PII) or criminal justice information (CJI).
Redaction isn’t just about protecting the innocent. It’s also about limiting your legal exposure. Fire investigators may handle sensitive witness identities, juvenile records, or protected health information. Uploading unredacted content even into a “private” instance could still result in unauthorized data exposure, depending on how the AI model handles inputs internally.
Use data preprocessing scripts or secure redaction tools before uploading anything. Some agencies are even building in-house utilities to batch-anonymize PDFs or JSON records before any AI model sees them.
- Use On-Prem or CJIS-Compliant AI Environments
To stay on the right side of the law, your AI model must operate within a CJIS-compliant environment. This can include private data centers maintained by your agency or vendors that meet the rigorous standards defined by the FBI’s CJIS Security Policy.
Think FIPS 140-2 encryption, secure identity and access controls, real-time audit logging, and U.S.-based data residency.
Examples of CJIS-compliant cloud infrastructure include:
- Microsoft Azure Government Cloud
- AWS GovCloud
- Oracle Government Cloud
If you're building internal AI capabilities, consider deploying open-source models like Mistral, LLaMA, or Falcon in a secure, on-prem container. This way, you maintain complete data sovereignty and never risk prompt logs leaking to a third party.
- Draft Reports But Don’t Finalize Them
AI can make your documentation process dramatically faster. It can reword rough scene descriptions, structure narratives, and identify which parts of NFPA 921 you've already cited. For overworked agencies juggling dozens of open cases, using ChatGPT or a local LLM to “clean up” text can save hours.
But AI should never write conclusions. Cause and origin determination requires professional judgment, experience, and context awareness that AI doesn’t have. Allowing a model to make definitive statements, even if it sounds right, can create evidentiary issues in court.
Treat AI-generated reports as rough drafts. Use them to speed up paperwork, but it can’t substitute for investigative reasoning.
- Use AI for Training Scenarios and Simulations
Training new investigators? Use AI to simulate a wide range of mock fire scenarios, each with varying levels of complexity, witness cooperation, and scene damage.
For example, you could prompt an internal LLM with:
“Generate a detailed residential fire investigation involving an unattended candle, one fatality, a second-floor origin, and misleading witness testimony.”
This kind of dynamic scenario creation is great for tabletop exercises, interview training, and hypothesis development. It allows trainers to tailor simulations on demand and expose recruits to edge cases not easily available in archives.
Pair this with visual scene diagrams (generated via AI image models) for a full-spectrum learning experience.
- Summarize Transcripts and Scene Notes
Fire scenes often involve dozens of interviews, handwritten scene logs, insurance documents, inspection histories, and lab reports. Manually processing all of this data can be overwhelming.
You can use AI to:
- Identify major themes across interviews
- Extract timelines from freeform scene notes
- Detect repetition or contradictions in witness statements
When fed through a secure LLM pipeline, AI can help distill hundreds of pages into digestible summaries. But you must compare outputs to the originals to make sure they’re reliable, especially if you'll use them in reports or trial prep.
- Build Timelines and Sequence Diagrams
Accurate sequencing is important for cause and origin determination. When did smoke alarms activate? When was the first 911 call placed? When was the back door opened?
AI can help convert these time stamps into structured timelines. Feed it input like:
“3:05 p.m. – Occupant hears loud pop.
3:08 p.m. – 911 call placed.
3:12 p.m. – First unit arrives.”
The model can return a polished, formatted timeline ready for review. You can also ask it to identify possible inconsistencies or logical gaps. You’ll still need to do timeline analysis yourself, but AI can make the process faster.
- Tag and Categorize Photo/Video Evidence
If you're dealing with hundreds of photos or drone footage from a large scene, AI vision models can help you sort them. Tools like AWS Rekognition, YOLOv8, or custom
OpenCV pipelines can:
- Flag potential burn patterns
- Label objects like fire alarms, outlets, or extension cords
- Detect people or vehicles present at different times
These models work best when used with metadata like GPS, timestamp, and camera orientation. Always review outputs manually and never use AI vision analysis as sole evidence.
- Create Hypothesis Trees from Scene Data
NFPA 921 encourages the development of multiple working hypotheses and testing each against the evidence. AI can help you create detailed decision trees that map:
- Possible causes
- Supporting and contradicting evidence
- Gaps in the investigation
For example, you could prompt:
“Based on this evidence set, list three possible origins and construct a logic tree for each.”
This is particularly useful for large-scale incidents or fatal fires with multiple ignition scenarios. AI helps you visualize your reasoning and share it clearly with others, especially during peer review or legal scrutiny.
- Auto-Organize Large Case Files
Got 500+ pages of combined files for a case? You can use AI to help you group them by category, like interviews, lab reports, insurance documents, transcripts, exhibits, and create an indexed table of contents.
You can also use GPT-based document classification within a secure PDF environment to generate smart folders, which can help you save hours of scrolling. This improves case efficiency and is a lifesaver during courtroom testimony prep.
- Translate Technical Jargon
Fire investigation is thick with terminology like arc mapping, spalling, vent-limited fires, and flashover. Attorneys, insurance agents, and jurors don’t always speak your language.
AI can help you translate complex concepts into plain English or reframe your findings for different audiences. Prompt examples:
- “Explain arc mapping to a 10th-grade student.”
- “Write a layperson summary of this burn pattern analysis.”
This makes sure your findings are understood and your credibility is reinforced.
- Run Debriefs with Simulated AI Witnesses
AI can simulate hostile, evasive, or emotional witnesses if you want to practice interviews. Use it for team drills or to train new investigators on real-world resistance.
Prompt example:
“Pretend you're a 17-year-old witness who’s nervous and reluctant to speak. You're hiding something but won’t say what unless directly asked.”
This kind of dynamic roleplay helps investigators improve tone, pacing, and empathy, which is important to gather accurate information.
- QA Reports for Internal Consistency
Human error creeps into even the most polished report. AI can act as a second set of eyes. Ask it to scan your document for:
- Timeline inconsistencies
- Contradictions between summary and detail
- Uncited claims or incomplete logic
Think of it as a junior editor, but not the final authority. It won’t know the case better than you do, but it will catch things you’ve glossed over after the 12th re-read.
- Draft Interview Questions
One of the most overlooked but important areas in any fire investigation is the quality of your interview. Poorly phrased questions can muddy timelines or tip off a suspect.
AI can help generate well-structured, unbiased, and context-appropriate interview questions. For example, you can prompt a secure LLM with:
“Generate 10 open-ended interview questions for a neighbor who reported smelling gasoline near the garage before a residential fire.”
The AI can help you vary question structure, shift tone for rapport-building, or create follow-up questions based on prior statements. It can dramatically improve prep and thoroughness.
AI can also rephrase potentially leading questions into neutral versions to protect the admissibility of your evidence.
- Generate Checklists from NFPA 921
Every fire investigator knows the importance of following the NFPA 921 methodology. But flipping through the text mid-case isn’t always efficient.
AI can extract protocol-aligned checklists based on your scenario. Prompt example:
“Create a step-by-step checklist for investigating suspected electrical fires using NFPA 921 principles.”
The model can return a procedural outline that includes:
- Evaluating circuit integrity
- Inspecting wiring and device connections
- Identifying signs of arcing
- Checking for overcurrent protection device failure
This can improve both your investigative rigor and courtroom defensibility.
- Detect Contradictions in Witness Testimony
Humans struggle to keep multiple, long interviews in mind, especially over weeks or months. AI doesn’t.
You can feed witness statements into your model, assuming redaction and secure deployment, and ask it to flag:
- Conflicting timelines
- Inconsistent scene descriptions
- Gaps between statements and physical evidence
For example, if a witness claims they entered the house after firefighters arrived, but the 911 transcript places them at the scene before, AI can help highlight that discrepancy for you to investigate further.
It’s not foolproof, but it’s a major advantage when combing through large volumes of narrative content.
- Document Your Use of AI in Every Case
If you’re using AI, no matter how securely, you must disclose it in your investigative file. This doesn’t mean you’re undermining yourself; it means you’re establishing transparency and a chain of logic.
Your case file should include:
- What AI tool was used
- Where it was hosted
- What task it supported (example: draft generation, summarization)
- What safeguards were in place
- A disclaimer that all final determinations were made by the investigator
Think of it like documenting a third-party lab test. If it influences your workflow, it deserves a mention.
- Avoid Predictive Fire-Cause Modeling
Do not use AI to guess cause or origin. Use it to describe the area of origin instead. No matter how advanced the model is, LLMs like ChatGPT or Claude are not trained on validated fire science. They operate on language patterns, not heat transfer, fuel loads, or ventilation dynamics.
If you prompt an AI to “determine the cause of a garage fire based on this report,” it will produce something that sounds correct. But it may be dangerously wrong, and worse, impossible to audit.
Stick to evidence-based models and validated investigative techniques. AI supports the process. It should never be your substitute for scientific reasoning. You can, however, use AI to describe the area of origin.
- Never Use Consumer-Grade ChatGPT for Sensitive Data
Public ChatGPT is not secure. The free version logs your prompts, stores them on OpenAI’s servers, and may be used to train future models. That means your sensitive case content could leak, if not directly, then through model behaviors.
Even ChatGPT Pro is not CJIS-compliant unless it’s hosted via Microsoft Azure OpenAI Service within a restricted environment.
No matter how harmless the input seems, avoid:
- Scene summaries
- Witness names
- Report excerpts
- Interview snippets
- Red Team the Output
Always assume that the first draft an AI gives you is flawed. That’s because LLMs are great at sounding right, even when they’re wrong.
Red teaming means interrogating the output:
- Does this claim cite real sources?
- Is this logic sound or circular?
- Is it hallucinating procedures that don’t exist?
AI is helpful but not trustworthy without oversight. If the stakes are high, and they always are in a fire investigation, you must vet everything before it enters the record.
- Collaborate with IT and Legal Before Implementation
Using AI tools without coordination from your IT and legal departments is asking for trouble.
IT ensures:
- Secure storage
- Identity controls
- API governance
- Encryption and audit trails
Legal can ensure:
- CJIS compliance
- Data handling policies
- FOIA and subpoena-readiness
- Internal policy alignment
Work with these departments from the start. You’ll have a safety net if anything goes sideways.
Understand the Risks and Rewards
There’s not much room for error in fire investigations. Every report, every timeline, every determination needs to be legally bulletproof. The idea of using ChatGPT or Claude to help build reports or summarize interviews might feel reckless.
On the other hand, AI can help you process 300 pages of witness statements in 30 minutes. It can help identify a theme you didn’t notice or format a draft in seconds. It can be incredibly helpful if handled correctly.
CJIS Compliance and AI
It’s important to understand CJIS compliance before you even think about dropping a name or a case number into ChatGPT.
The FBI’s Criminal Justice Information Services Security Policy (CJIS) dictates how all law enforcement agencies store, process, and transmit Criminal Justice Information (CJI). Any tool or platform that touches this data must:
- Use encryption at rest and in transit (FIPS 140-2)
- Log and audit every user action
- Restrict access via role-based controls
- Be hosted in approved U.S. jurisdictions
- Require personnel background checks if third parties are involved
OpenAI’s consumer-facing ChatGPT is not CJIS-compliant. Neither is Anthropic Claude, Google Gemini, nor iz any free online AI chatbot.
While the standard ChatGPT is not CJIS-compliant, OpenAI has introduced "ChatGPT Gov," which is designed for U.S. government agencies. This version can be self-hosted within Microsoft Azure's Government Cloud to help agencies manage security, privacy, and compliance requirements, including CJIS.
If you want to use AI legally and safely, it must either:
- Run on an on-premise server your agency controls,
- Be deployed within a CJIS-partnered cloud (like Azure GovCloud or AWS GovCloud),
- Or use a vetted API with no data retention and zero prompt logging.
AI Providers for CJIS & Security
Here’s a quick comparison for AI providers that can be used for CJIS and security. Always check with your agency’s security officer or legal department before deploying.
| Provider |
CJIS-Eligible? |
Prompt Retention? |
Enterprise Options? |
Notes |
| OpenAI (API) |
No |
Optional retention |
Yes (Azure hosted) |
Azure + GovCloud required |
| Microsoft Copilot |
Yes (via Azure) |
No |
Yes |
Strong candidate |
| Amazon Bedrock |
Yes |
No |
Yes |
LLM-agnostic |
| Anthropic Claude |
No |
Yes |
Limited |
Prompt data stored |
| Google Gemini |
No |
Yes |
Limited |
Prompt storage common |
| Private LLaMA Models |
Yes (if self-hosted) |
No |
Yes |
Open-source flexibility |
Fire investigation will always be a boots-on-the-ground profession. You analyze scenes, notice human nuance, and weigh witness credibility. No algorithm can replicate that.
But AI can help you work faster, train smarter, and stay organized in ways we couldn't imagine a decade ago. So, use it, but do it safely.
The most important intelligence in your investigation is still human, but AI can be a tool that supports it and makes it more efficient.
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