AI can accelerate an investigation. It cannot replace the investigator.
A practical framework for using artificial intelligence to organize, analyze, summarize, and develop investigative information while protecting evidence integrity, constitutional requirements, discovery obligations, and human accountability.
Reviewed and updated August 16, 2026
The value of AI is not that it knows the case.
Criminal investigations generate information: reports, interviews, recordings, photographs, documents, records, timelines, digital evidence, tips, analytical products, and investigative leads. Artificial intelligence can help investigators work through large quantities of that information more efficiently.
But an AI system does not possess personal knowledge of the investigation. It may misunderstand source material, overlook context, combine unrelated facts, generate unsupported conclusions, or state an inaccurate proposition with apparent confidence.
The appropriate model is therefore AI-assisted investigation: technology supports the investigator's work while investigative judgment, factual verification, legal decisions, and accountability remain with trained personnel.
A lead is not evidence.
AI may identify a pattern, inconsistency, relationship, question, or investigative avenue worth examining. That can be valuable. But the usefulness of an investigative suggestion does not establish that the underlying proposition is true.
Ask one question every time:
“What independent source establishes this fact?”
If the answer is merely “the AI said so,” the proposition has not been independently established.
Where AI can assist investigators
The appropriate safeguards depend on the task, information involved, system being used, and consequences of an inaccurate output.
Organize Complex Investigations
Structure large quantities of known information by person, event, location, date, document, issue, or investigative question while retaining links to the original source material.
Develop Preliminary Timelines
Extract dates and events from supplied source material to create a working chronology that investigators can verify against original records.
Summarize Large Record Sets
Create working summaries of documents, reports, transcripts, policies, communications, or other materials while requiring source references and independent verification.
Analyze Interviews & Statements
Identify subjects requiring follow-up, compare accounts, locate potentially inconsistent statements, and develop additional interview questions without treating AI analysis as a credibility determination.
Identify Potential Relationships
Surface names, entities, locations, dates, communications, or events that may warrant comparison across the investigative record.
Generate Investigative Questions
Use AI as a structured brainstorming tool to identify unanswered questions, possible evidentiary gaps, alternative hypotheses, and investigative steps for human evaluation.
Organize Open-Source Research
Assist with categorizing and summarizing lawfully obtained public information while maintaining source attribution and independently verifying consequential information.
Draft & Structure Work Product
Help organize investigative summaries, outlines, chronologies, briefing materials, or draft language when every factual assertion remains subject to investigator verification.
Test the Investigation
Ask the system to identify unresolved questions, conflicting propositions, missing sources, alternative explanations, or areas where the investigative theory depends on assumptions rather than established facts.
Not every AI-assisted task presents the same risk.
Agencies should apply stronger review, documentation, security, and approval requirements as an AI-assisted activity becomes more consequential.
- Brainstorming investigative questions
- Formatting information
- Creating non-evidentiary outlines
- Organizing known topics
- Drafting generic checklists
- Summarizing investigative records
- Constructing timelines
- Comparing witness accounts
- Identifying relationships
- Analyzing large document sets
- Probable-cause related work
- Identification of suspects
- Assessment of guilt or credibility
- Forensic conclusions
- Decisions affecting liberty or enforcement
Keep the investigator between the AI and the conclusion.
A defensible workflow creates separation between original evidence, AI-assisted analysis, investigative verification, and the final investigative conclusion.
Source
Identify the original information being analyzed.
Process
Define exactly what the AI is being asked to do.
Review
Examine the AI output for accuracy and unsupported content.
Verify
Compare consequential propositions against authoritative sources.
Document
Preserve required records of material AI-assisted activity.
Decide
The investigator makes and owns the investigative judgment.
The prompt should constrain the system.
Investigative prompting should define the task, identify the source boundaries, prohibit unsupported assumptions, require source attribution where possible, and tell the system how to handle uncertainty.
A well-structured prompt does not make an unreliable system reliable. It does, however, make the investigator's request clearer and makes unsupported output easier to identify.
Investigative Prompt Framework
“Using only the materials provided, create a chronological list of events. For each event identify the source supporting it. Do not add facts not contained in the source material. If two sources conflict, identify the conflict rather than resolving it. List unanswered questions separately.”
Never lose the path back to the original.
AI-assisted processing should not make it difficult to determine what the original evidence contained, what the technology did to it, and what conclusions were made by a human investigator.
Preserve Original Material
- Retain the original evidentiary source.
- Avoid overwriting original files with AI-processed versions.
- Maintain normal chain-of-custody procedures.
- Distinguish original evidence from derivative analytical products.
Verify Quotations
- Check quoted language against the original recording or document.
- Do not assume an AI-generated quotation is verbatim.
- Confirm speaker attribution independently.
- Resolve transcription uncertainty before relying on exact wording.
Protect Context
- Review enough of the original source to understand context.
- Do not rely solely on extracted excerpts or automated summaries.
- Identify omissions that could materially affect interpretation.
- Distinguish fact, inference, allegation, and opinion.
Maintain Provenance
- Know which system performed the processing.
- Know what source information was supplied.
- Know whether the output was edited or transformed.
- Retain records required by agency policy, law, or discovery obligations.
AI may help organize the writing. It cannot supply probable cause.
An AI system may be useful for organizing established facts, identifying gaps in a draft, improving structure, or testing whether an affidavit clearly explains the investigative theory.
But factual assertions supporting probable cause must come from independently established investigative information—not from an AI model's prediction, assumption, reconstruction, or invented detail.
Before using AI-assisted language in formal investigative work:
- Trace every material factual assertion to its source.
- Verify names, dates, quotations, locations, and numbers.
- Remove unsupported characterization or inference.
- Confirm that omissions have not changed the meaning of the evidence.
- Ensure the investigator personally understands and adopts the final work product.
- Follow applicable agency, prosecutorial, preservation, and disclosure requirements.
What goes into the system matters as much as what comes out.
Investigative information can include criminal justice information, personally identifiable information, medical information, intelligence, sealed material, confidential-source information, victim information, and other sensitive data. Agencies should determine what information may be submitted to each approved system before personnel use it.
Approved Systems
Personnel should know which AI systems are authorized for investigative use and whether particular categories of agency data may be processed by those systems.
Vendor Data Practices
Agencies should understand storage, retention, access, secondary use, model-training practices, deletion, security controls, and contractual obligations associated with submitted information.
Criminal Justice Information
Criminal justice information requires appropriate protection. Agencies should integrate AI use with their existing information security, access-control, and CJIS compliance processes where applicable.
Preservation
Agencies should decide whether prompts, outputs, draft products, audit logs, source files, and other AI-related records must be retained for evidentiary, records-management, litigation, or discovery purposes.
Disclosure
Material AI use may create disclosure questions. Agencies should establish procedures with counsel and prosecutors rather than leaving those decisions to individual investigators.
Auditability
For consequential investigative uses, the agency should be able to reconstruct what information was supplied, what the system produced, what the investigator verified, and what ultimately entered the case file.
Five red lines worth considering
These are governance recommendations rather than a statement that every use below is categorically prohibited by law. Agencies should establish their own use-specific rules in consultation with counsel, prosecutors, technology personnel, investigators, and command staff.
Do not treat generative AI output alone as proof that a fact exists.
Do not allow AI to make the final determination of guilt, credibility, probable cause, or investigative disposition.
Do not place sensitive investigative information into an unapproved AI system merely because the system is convenient.
Do not use AI-generated quotations, summaries, identities, or factual assertions without appropriate verification.
Do not permit AI processing to obscure or replace the original evidentiary source.
Before using AI — and before relying on it
Before Using the System
- Is the system approved for this use?
- May this information be entered into the system?
- What exactly am I asking the system to do?
- What are the consequences if the output is wrong?
- What records of the AI interaction must be preserved?
- How will I verify the resulting output?
Before Relying on the Output
- What original source supports each material proposition?
- Did the system add a fact that was never supplied?
- Did it omit information that changes the context?
- Are quotations and speaker attributions accurate?
- Have alternative explanations been considered?
- Can another investigator reconstruct how this conclusion was reached?
Investigators increasingly encounter AI-generated evidence and AI-enabled crime.
AI is not only an investigative tool. It may also be part of the criminal conduct, communication method, concealment strategy, or evidence investigators encounter.
Investigators should be prepared to evaluate authenticity, provenance, platform records, metadata, account attribution, and corroborating evidence rather than assuming digital content is authentic merely because it appears realistic.
Build AI-assisted investigative workflows without compromising the case.
Shield Public Safety Training provides training and consultation addressing AI governance, criminal investigations, police reports, digital evidence, emerging technology, and defensible agency implementation.