Hazard analysis: Enhanced with AI within existing processes

1| The Complexity of Hazard Analysis in High-Speed Rail

Hazard analysis within high-speed rail requires a methodology that is simultaneously precise, time-critical, and resilient. The core challenge lies in the combination of explosive growth in project data, strict safety standards (such as EN 50126), complex system interactions, and hazards that often only manifest late as emergent behavior.

The sheer volume of project data makes every safety issue multi-layered. Teams must sift through thousands of documents, specifications, changes, and design notes to understand interdependencies. The necessity of complying with rigorous safety standards ensures that every step must be provable, consistent, and traceable. At the same time, the intensity of system interactions makes it difficult to predict how a minor change in one subsystem will ripple through other components. Hazards often become visible only when multiple factors converge—a challenge that puts traditional methods under immense pressure.

This interconnectivity leads to a workload where engineers spend more time gathering, organizing, and verifying information than on substantive risk assessment. This slows down lead times and increases the likelihood of overlooked risks. This is not due to a lack of engineering competence, but because the human capacity to oversee all context, historical decisions, and interfaces simultaneously has reached its limit.

Industry Case Studies:

• The Interface Ripple Effect: A project team implements an interface change in a braking system. Only after weeks of manual cross-referencing does it emerge that this change unexpectedly impacts the logic in a different, seemingly unrelated subsystem, leading to costly rework.
• Late-Stage Emergent Behavior: During a late integration test, a critical hazard is discovered. It turns out to be the result of two separate design choices that individually met all standards, but collectively posed an unacceptable risk—a classic case of emergent behavior.
• The Audit & Traceability Gap: During an audit, it appears that different teams are working with conflicting document versions. It takes days to retrieve the correct documentation because project data is scattered across numerous streams and versions, jeopardizing both traceability and compliance.

2| How AI Connects Project Data and Historical Information into a 'Digital Thread'

AI functions within existing processes without replacing your established workflows. In the Basewise approach, AI supports document analysis, requirements management, verification, and evidence—all within a secure environment where client data is never used for model training. The role of AI is advisory and reinforcing, not decision-making.

By connecting project data with historical documents, information becomes accessible in a consistent manner. AI creates a 'Digital Thread' that reveals relationships between documents that would otherwise remain hidden. This includes automatic links between specific requirements (according to EN 50128/129) and verification activities, or between design changes and relevant prior decisions.

Early hazard identification is strengthened as AI proactively flags anomalies and inconsistencies while engineers work. As a result, insights emerge faster, manual search effort is eliminated, and efficiency is significantly improved.

AI monitors design changes, specifications, and verification documents as they are developed, signaling relevant relationships or deviations before inconsistencies become embedded in the project.

Practical Examples of AI Support:

• Real-time Requirement Guarding: An engineer updates the specification for a door mechanism. AI immediately flags that a passage conflicts with an older requirement regarding emergency manual overrides. The team can instantly assess whether this triggers a potential hazard and take corrective action.
• Consistency Across Subsystems: AI notices that two separate documents use different assumptions for the same operational condition. This is flagged before it leads to costly inconsistencies in design or risk assessments later in the project.
• Proactive Audit Readiness: While preparing for an audit, AI identifies a discrepancy in a test report compared to a previously established verification plan. Engineers can correct this in time, ensuring compliance and preventing project delays.

→ AI Suggests, Humans Decide
AI presents insights and potential points of attention, but the classification of hazards, risk acceptance, and final decision-making remain explicitly the responsibility of the engineer and the designated safety role.

3| Strengthening Engineering Decisions Through Evidence-Based Insights

The primary benefit lies not just in speed, but in demonstrably more complete and consistently substantiated decisions. Time savings follow naturally from this foundation.

Engineers are provided with relevant passages, relationships, variants, and dependencies, while they always retain final authority over substantive decisions. This enables them to focus on high-value risk assessment and interpretation, rather than time-consuming search tasks.

Because AI can operate entirely within existing workflows, the methodology remains familiar and the learning curve is minimal. Engineers experience a significant reduction in administrative burden, while the reliability of the analysis increases.

The result is that mitigations can be assessed earlier and more consistently—which is essential in environments where safety is paramount. This leads to time savings, cost reduction, and, above all, an elevated level of safety.

Practical Examples of Faster and Better Decision-Making:

• Validating Mitigations for Complex Software: A team is assessing a mitigation for a complex software modification. AI instantly retrieves all documents where the related requirement appears, including relevant design notes and test reports. This accelerates the answer to the crucial question: Is the mitigation both complete and adequate?
• Context-Rich Hazard Analysis: Faced with a potential hazard caused by voltage fluctuations, AI immediately surfaces relevant historical incident analyses and previous test results. This provides a more comprehensive and better-substantiated overview, leading to more effective safety measures.
• Proactive Conflict Detection: During a design change, AI signals that a proposed mitigation appears to contradict an older verification report. The team can immediately determine whether additional measures are necessary to ensure compliance and safety.

4| The Tangible Impact on High-Speed Rail Organizations

When AI is deployed in a secure, integrated, and process-supportive manner, it strengthens existing systems without replacing them. Teams gain access to information that is more complete, consistent, and rapidly available, leading to higher-quality output and reduced costs.

For organizations, this means workflows become more robust, valuable institutional knowledge is better utilized, and risks are identified and addressed significantly earlier. Adoption can be phased: starting with document analysis and later expanding to requirements, verification, and evidence.

Because the Basewise approach never uses client data for model training and operates entirely within a secure environment, organizations maintain full control and confidentiality—an absolute necessity in the sensitive rail sector.

Practical Examples of Successful Implementation:

• Scalable Pilot Success: An organization begins with AI support in a single project team. After a few months, this is scaled to multiple teams as it becomes evident that risk dependencies are found demonstrably faster and the quality of analyses noticeably improves.
• Step-by-Step Compliance Integration: A program organization starts using AI for structuring and managing requirements. Once this leads to greater consistency and improved demonstrable compliance, the same methodology is incrementally expanded to verification and evidence.
• High Adoption through Trust: Experience shows that acceptance is high when engineers feel that AI enhances their thought process rather than replacing it. By deliberately avoiding autonomous decision-making, trust is maintained throughout the organization.