How AI Transforms Safeguard Analysis
In complex process facilities, safeguarding systems are the backbone of risk reduction. But when your Process Hazard Analysis (PHA) contains hundreds, or even thousands of safeguards, identifying what truly matters becomes a challenge.
In this scenario, we demonstrate how AI-powered analysis can rapidly prioritize, contextualize, and communicate safeguard performance—turning static PHA data into actionable engineering insight.
Prioritizing What Matters Most
Industry investigations consistently show that safeguards must be actively managed. Being identified in the PHA can only be the first step in the management process. The U.S. Chemical Safety Board’s Williams Geismar report identified procedural changes made without management of change as a contributing factor, along with broader deficiencies in process safety systems. In the BP-Husky Toledo refinery report, the CSB concluded that the refinery did not have effective safeguards to prevent naphtha overflow into a vapor bypass line during a process upset, underscoring the risk of bypass conditions and weak abnormal-situation management.
The process of prioritization begins by filtering safeguards based on maximum criticality. In a facility with over 1,000 safeguards, this step immediately highlights high-value safety critical elements that demand attention.
For example, in this video clip you’ll see a safeguard labelled PSL 101 rises to the top of the list. Rather than manually searching through multiple scenarios, the system enables a targeted deep dive into where and how this safeguard is applied across the operation.
Connecting Safeguards to Real Process Context
Safeguards need to be understood in context, engineers need to see how it interacts with the process.
By linking directly to the P&ID, users can:
Visualize the safeguard within the engineering design
Identify upstream and downstream dependencies
Understand how it interacts with other protective layers
This contextual view is essential for assessing real-world effectiveness, especially during design reviews or field modifications.
Aggregating Scenario-Level Intelligence
The platform demonstrates one method of using AI to extract all scenarios where a safeguard is active. This includes:
Accessing raw PHA data through a data viewer
Filtering relevant use cases
Exporting structured datasets for reporting
Additionally, safeguards can be analyzed within bow tie diagrams, providing a clear view of:
Threats and initiating events
Consequences
Preventive and mitigative barriers
This makes it easier to identify whether other safeguards can act as contingencies, particularly during bypass or maintenance conditions.
AI-Generated Bow Ties and Reports
One of the most powerful capabilities is the use of AI to generate full bow tie visualizations and safeguard reports.
By entering a simple prompt, the system produces:
A complete safeguard description and functional overview
Risk reduction analysis and effectiveness
Credibility considerations and potential failure modes
Alignment with industry standards and best practices
The system references real-world incidents from industry such as the 2016 Enterprise Products gas plant event to highlight failure scenarios and lessons learned.
Enabling Better Engineering Decisions
Beyond analysis, the platform supports decision-making by generating:
Targeted questions for operations and maintenance teams
Insights into safeguard reliability and testing requirements
Considerations for management of change (MOC), bypass planning, and maintenance prioritization
This ensures that critical safeguards are not only identified but actively managed throughout their lifecycle.
At RskLess, our goal is to help you bridge PSM program gaps, ensuring you manage risk effectively and stay safer than yesterday.
At RskLess, our goal is to help you uncover hidden hazards, ensuring that your risk assessments are thorough and well understood to help you be safer than yesterday.
