AI Integration for Cority Process Safety

AI integration connects directly to Process Hazard Analysis (PHA), Layer of Protection Analysis (LOPA), and Safety Instrumented System (SIS) data objects within Cority. The primary surface areas are the PHA study records, hazard and operability (HAZOP) worksheets, LOPA scenarios, and SIF (Safety Instrumented Function) performance data. AI agents can be triggered via Cority's API or scheduled workflows to analyze these records, cross-reference them with live operational data (via integrations), and historical incident logs to identify gaps, validate assumptions, and recommend updates.

Implementation typically involves a sidecar architecture where an AI service layer, hosted in your cloud, subscribes to Cority webhooks for new or updated PHA studies. This service uses retrieval-augmented generation (RAG) over your internal engineering standards, past PHA reports, and equipment failure databases to ground its analysis. For example, when a PHA for a reactor system is initiated, an AI agent can automatically suggest relevant deviation guidewords (e.g., "more pressure," "reverse flow") based on similar historical studies, draft potential causes and consequences, and even recommend initial safeguards and their required SIL (Safety Integrity Level) based on the consequence severity. This reduces the manual literature review and data entry that often bottlenecks process safety engineers, shifting their focus to validation and expert judgment.

Rollout should be phased, starting with assistive copilots for PHA scribes to accelerate study setup, then expanding to automated LOPA scenario validation that checks for consistent risk tolerance criteria. Governance is critical: all AI-generated recommendations must be attributed, versioned, and require human approval before being committed to the official Cority record. An audit trail within Cority should log every AI interaction, the prompts used, and the data sources retrieved. This controlled, human-in-the-loop approach ensures the AI augments—rather than replaces—the rigorous, accountable engineering judgment required for functional safety.

The integration architecture is built around Cority's core Process Safety Management (PSM) data objects—Process Hazard Analysis (PHA) studies, Layer of Protection Analysis (LOPA) worksheets, Safety Instrumented System (SIS) test records, and associated deviations or management of change (MOC) logs. A secure middleware layer, typically deployed as a containerized service in your cloud or on-premises environment, establishes a real-time sync via Cority's REST API or listens for webhook events on key record updates. This service normalizes the data, extracting critical fields like process parameters, identified hazards, safeguard descriptions, and IPL (Independent Protection Layer) details, then routes it to a vector database for semantic indexing.

For a typical workflow, an engineer updating a PHA study in Cority triggers the system. The AI agent retrieves the study's context and performs a cross-reference analysis against historical incident data, recent audit findings from the Cority audit module, and similar processes documented in other PHAs. Using a fine-tuned LLM, it can then generate a risk narrative summary, flag potential gaps in safeguards where the LOPA may be under-specified, or suggest updates to SIS testing frequencies based on failure data patterns. These insights are written back to designated fields in Cority as draft recommendations, or pushed to a dedicated AI Insights dashboard for engineer review, maintaining a full audit trail of AI-suggested changes.

Governance is critical. The architecture includes a human-in-the-loop approval step before any AI-generated content modifies a master PHA or LOPA record. All AI interactions are logged with prompts, source data references, and model versions for compliance (e.g., OSHA PSM 1910.119). Rollout is phased, starting with a single high-risk process unit to validate insights and user trust before scaling. This approach transforms Cority from a system of record into an active partner in process risk management, helping teams move from periodic, manual review cycles to continuous, data-driven hazard analysis. For related architectural patterns on incident data, see our guide on AI Integration for Cority Incident Management.

A production-ready integration for Cority Process Safety is built on a secure, event-driven architecture. AI agents are triggered by changes to key objects like Process Hazard Analysis (PHA) studies, Layer of Protection Analysis (LOPA) worksheets, or Safety Instrumented System (SIS) records. Using Cority's APIs, the system extracts relevant context—such as hazard descriptions, consequence analyses, and existing safeguards—and sends a secure payload to a private inference endpoint. Generated outputs—like suggested initiating events, independent protection layer (IPL) validations, or SIF (Safety Instrumented Function) verification notes—are written back to designated fields or linked documents, with a full audit trail logging the AI's contribution, the source data, and the prompting logic used.

Governance is designed into the workflow. Before any AI-generated content is committed to the primary record, it can be routed through a human-in-the-loop review step within Cority's existing approval workflows. For example, a suggested PHA node generated from a similar study would require review and sign-off by the lead facilitator before being added to the live worksheet. This ensures the process safety engineer retains ultimate responsibility and control. Furthermore, all AI interactions are scoped to operate within a strict data security boundary, ensuring sensitive process design information, proprietary chemical data, and risk matrices are never exposed to unauthorized models or retained for external training.

We recommend a phased rollout, starting with a single, high-value use case in a non-critical environment. A typical Phase 1 might focus on AI-assisted PHA revalidation, where the agent analyzes previous studies and operational changes to suggest new nodes or updated safeguards for facilitator review. Success is measured by the reduction in manual data collation time and the consistency of hazard identification. Subsequent phases can expand to automated LOPA cross-checks or SIS requirement specification drafting, each introduced only after validating performance, user acceptance, and data integrity in the prior phase. This crawl-walk-run approach builds confidence, allows for tailored prompt engineering, and integrates AI as a governed tool within the certified process safety management system, not a disruptive replacement.