Introduction
A recent online discussion examining the gap between AI marketing claims and investigative reality prompted practitioners to share how they are currently using AI tools in their work, along with the practical challenges they continue to encounter.
The response highlighted a clear demand for candid, experience-based discussion about the role of AI in open source intelligence (OSINT) investigations. As adoption increases, a more grounded examination is needed: what is working in practice, where do risks remain, and how is AI being integrated into real investigative workflows?
This article explores the current state of AI in OSINT investigations, focusing on practical implementation rather than promise, and examining how investigators are balancing experimentation with professional standards and methodological rigour.
Current AI Usage: What’s Actually Happening
Most investigators are using AI for contained tasks where errors won’t compromise entire investigations. For example, document summarisation for initial review, timeline organisation from fragmented notes and search string generation when initial queries aren’t productive.
This cautious approach makes sense. You don’t overhaul proven methodologies based on new technology until you understand its limitations. The experimental phase builds knowledge about where AI adds value and where it introduces risk, and that institutional knowledge matters more than rushing to adopt sophisticated platforms.
AI’s Role: Processing, Not Investigating
AI operates on information you provide. Large Language Models (LLMs) can generate plausible-sounding answers that are completely wrong and confidently describe processes that don’t exist. They fabricate details that seem reasonable but aren’t factual.
This is why the distinction between processing and investigating matters. AI can reorganise your verified data, reformat it, or present it from different angles, but it cannot make investigative decisions: which database to check, which line of enquiry is promising, whether a source is credible.
You still need core OSINT skills such as:
Source evaluation.
Understanding search operators and database structures.
Recognising when information is missing versus when it doesn’t exist.
Knowing how to pivot when initial searches fail.
Understanding metadata and digital footprints.
Assessing credibility and cross-referencing information across multiple sources.
What Actually Works: Practical Applications
Document Review and Summarisation. Initial AI summarisation of lengthy documents, followed by manual review of critical sections. This accelerates the review phase without eliminating careful reading.
Timeline Organisation. AI organises fragmented information from multiple sources chronologically. This creates structure from scattered data. However, verification against source material remains essential. Organisational errors occur frequently enough that blind trust would be negligent.
Search Query Development. AI suggests alternative search terms or different phrasings when initial searches aren’t productive. These suggestions require evaluation, but they can prompt lines of enquiry you hadn’t considered.
Translation Support. Quick AI translation of foreign language material provides initial understanding. Critical material still requires human translator verification.
The consistent pattern is that AI can provide acceleration for specific tasks, but human oversight and verification remain essential.
Practical Integration Framework
Establish Clear Boundaries. AI operates on verified information you provide, it doesn’t conduct searches independently. You collect information, feed it to AI for organisation or analysis, then verify outputs against source material.
Improve Your Prompting. Specific, contextualised prompts yield useful outputs. Vague requests produce vague results.
Poor prompt: “Analyse this information.”
Better prompt: “Organise these interview notes chronologically, note contradictions between sources, and highlight timeline gaps between 2pm and 6pm.”
Context, specified output format, and focused analytical questions improve utility significantly.
Know When Speed Doesn’t Matter. Critical findings need thorough manual verification regardless of time pressure. Information influencing significant decisions requires traditional verification methods. AI might help organise your thinking, but it doesn’t replace due diligence.
Be Transparent About Methodology. Document AI’s role clearly. If it organised a timeline, note that. If it summarised documents, state which ones and that critical sections were manually reviewed. This transparency maintains professional standards and protects you if methodology questions arise, otherwise, you are delivering assessments based on invisible foundations.
The Hype-Reality Gap
Conference presentations often highlight autonomous AI investigative capabilities. Marketing materials promise dramatic efficiency gains, while industry publications refer to “OSINT 3.0” as though it were already standard practice.
In reality, most practitioners are experimenting with far more contained uses such as document summarisation, note organisation, and limited analytical support within existing workflows.
This disconnect creates confusion. New investigators may assume that advanced, semi-autonomous capabilities are typical. More experienced professionals may feel pressure to adopt technologies they neither fully trust nor consider operationally ready.
In practice, the norm is far more measured: cautious experimentation, clearly defined task boundaries, rigorous verification of outputs, and gradual integration designed to enhance, not replace, established investigative methods.
What is often missing from industry discussions is an honest assessment of where AI currently performs well, where it struggles, and how those limitations shape responsible use in real-world investigations.
Moving Forward
The technology will continue to evolve. However, the central requirement of effective investigation remains unchanged: sound judgement developed through experience.
Investigators benefiting most are those who:
Understand both capabilities and limitations
Integrate AI into established workflows rather than replacing proven methods
Maintain strong foundational investigation skills
Treat AI as one tool among many, not a solution
Used appropriately, AI accelerates specific tasks and helps manage information overload. Used carelessly, it introduces errors and undermines investigation quality.
The difference lies in understanding what it can and cannot do, maintaining verification standards, and being honest about its role in your methodology.
Please contact IMSL for more information about our AI and OSINT training or explore IMSL’s upcoming course calendar.
Alternatively, IMSL’s workshops offer quick upskilling sessions as well as free lunchtime webinars across a variety of specialist subjects.
