From Guesswork to Ground Truth: The AI Transformation of Travel Risk Management

Travel security risk management is a highly dynamic, data-dependent function that demands real-time awareness, rapid decision-making, and precise personnel accountability. When critical incidents such as terrorist attacks, natural disasters, civil unrest, or disease outbreaks occur, one of the immediate challenges for security teams is determining exactly which employees are in the affected area, what their risk exposure is, and how best to manage the company’s duty of care responsibilities. Artificial Intelligence (AI), if carefully and responsibly integrated into corporate security processes, offers practical solutions to these long-standing challenges by enhancing speed, accuracy, and operational efficiency in ways that manual or semi-automated systems cannot match.

Moving Beyond Static Manifests

A core operational problem for travel security risk teams is that traveler manifests and itineraries are rarely a reliable indicator of where employees actually are at the moment of a crisis. Employees rebook flights, extend stays, cancel meetings, or change plans without necessarily updating central travel management systems. What AI can bring to this problem is the ability to continuously synthesize a range of disparate data sources to create a far more accurate, real-time location picture of personnel movements and presence.

For example, AI models can pull and correlate structured and unstructured data such as corporate travel booking feeds, airline and hotel system APIs, mobile device roaming data (subject to privacy controls and consent), corporate credit card transactions, voluntary location check-ins via secure corporate apps, and even meeting and calendar data from enterprise platforms like Microsoft 365 or Google Workspace. By cross-referencing these sources, an AI system can infer where a person actually is versus where their original booking suggested they would be. This allows the security team to produce live, prioritized lists of employees whose locations place them within, or near, an emerging threat zone.

Real-Time Incident Matching

In the context of a crisis event – such as an explosion at a transport hub or a political riot in a city district – the system could automatically match the geolocation of the event (sourced from verified intelligence feeds, breaking news NLP monitoring, or local security partners) against the inferred or confirmed location of all traveling personnel. The output could be an immediate, auto-generated list of potentially exposed staff, categorized by certainty of their presence: “Confirmed in Zone,” “Likely in Zone,” or “Possibly in Zone,” based on the strength and freshness of the correlated data. This eliminates hours of manual effort typically spent cross-checking spreadsheets, contacting line managers, or chasing travel booking data that may already be out of date.

Faster Risk Exposure Reporting

One of the most valuable operational applications of this AI-driven approach is the acceleration of initial incident reports and corporate exposure assessments. Security managers are often required to provide senior leadership with a situation summary within minutes of a crisis unfolding: How many people do we have in that location? Are they safe? What’s our risk exposure? With AI-fueled location correlation, such questions can be answered with immediate clarity, including the provision of automated summary dashboards and pre-formatted reports suitable for executive decision-making and legal or insurance notification requirements.

Automated Communication and Accountability

Crisis communication workflows also benefit from AI enhancement. Natural Language Processing (NLP) powered chatbots, integrated into corporate communication channels or mobile security apps, can automatically initiate welfare check-ins with potentially impacted employees. The system can detect both structured and free-text replies, interpret expressions of distress or urgent need, and escalate these to human operators as required. This kind of automation ensures no employee is accidentally omitted from contact efforts during the chaos of a fast-moving incident, and it allows human crisis teams to focus attention where it is most urgently needed—on unresponsive or at-risk individuals.

Audit Trails and Post-Incident Analysis

Once the immediate threat has passed, AI can assist with the post-incident phase by producing precise movement and exposure logs, showing which travelers were closest to the incident and how quickly they were accounted for. Such records are invaluable for internal investigations, board-level risk reviews, and insurance or legal claim defenses. Additionally, machine learning algorithms can detect patterns in incident data and traveler behavior over time, helping security teams to adjust travel policies, destination risk ratings, and pre-trip advisories based on evidence rather than assumption.

Predictive Risk Planning

Beyond real-time crisis response, predictive analytics powered by AI can offer strategic risk planning value. By analyzing historical data from previous travel disruptions – such as the frequency of protests in specific cities, flight disruption rates at certain airports, or the emergence of disease outbreaks – AI systems can generate forward-looking risk models. These models can guide pre-trip risk assessments, suggesting routing alternatives or travel postponements for destinations showing a rising risk profile, or recommending additional mitigation measures for vulnerable traveler profiles such as solo travelers or executives with public profiles.

Risks and Governance Considerations

However, the introduction of AI into travel security workflows is not without its challenges. The privacy implications of analyzing and inferring employee location data are significant and must be managed carefully, with transparent policies, informed employee consent, and strict data governance frameworks. There is also the risk of over-reliance on AI-derived outputs, where false positives or negatives could lead to incorrect assumptions about personnel safety or risk exposure. As such, human oversight remains a critical requirement; AI is an assistive tool, not a replacement for human judgment in high-stakes security decisions.

Conclusion

AI offers the potential to radically improve the operational effectiveness and speed of travel security risk management. By automating the collection, correlation, and interpretation of fragmented data sources, AI enables security teams to achieve faster crisis detection, clearer risk exposure reporting, and more efficient traveler communication than manual processes allow. For firms operating globally, with hundreds or thousands of employees on the move at any given time, this capability may become not merely a competitive advantage but a fundamental requirement for responsible corporate duty of care in the years ahead.

Marshal is a powerful digital ecosystem for Security & Resilience capability. [As at June 2025] We are currently developing a dynamic AI-enabled risk intelligence infrastructure that adapts to real-world security needs as they emerge, providing the ability to request, discover and assess “ground truth” whilst sourcing mitigation solutions and support in real time.