AI Integration for Cority Risk Assessment

AI connects directly to Cority's Risk Register and Assessment objects, typically via its REST API or scheduled data syncs. The integration targets two primary workflows: qualitative risk analysis (where AI parses free-text descriptions of hazards, consequences, and existing controls from JSA forms or incident reports) and quantitative risk scoring (where AI validates and enriches numerical data like probability, severity, and exposure to calculate a more consistent risk priority number). This allows the system to auto-populate risk matrices and generate initial assessment narratives, reducing manual data entry by safety engineers and risk analysts.

In production, the architecture involves a secure middleware layer that pulls new or updated risk records from Cority, processes them through an LLM orchestration service with a specialized risk assessment prompt library, and posts back enriched data—including a generated risk description, a refined score, and a list of recommended control measures mapped to Cority's Control Library. High-impact use cases include:

• Automated JSA Enrichment: AI reviews job safety analyses to identify unlisted hazards and suggest additional controls.
• Dynamic Risk Re-scoring: AI periodically re-evaluates risk scores based on new incident data, audit findings, or control effectiveness records from linked modules.
• Cross-Module Correlation: AI identifies related risks across different Cority modules (e.g., linking a chemical exposure risk from the Chemical Management module to a process safety risk in the Operations module).

Rollout should be phased, starting with a pilot for new risk assessments to build trust in AI-generated content before applying it to the entire historical register. Governance is critical: all AI-suggested scores and controls should be flagged for reviewer approval within Cority's workflow engine, maintaining a clear audit trail. The final output is not a black-box score, but a co-pilot for risk professionals—providing a consistent, data-informed starting point that accelerates assessment cycles from days to hours while improving the quality and defensibility of risk records. For related architectural patterns, see our guide on AI Integration for Cority Incident Management.

The integration architecture is built on Cority's REST API and webhook capabilities. The primary data flow begins when a new risk assessment is initiated in Cority's Risk Management module or when an existing assessment is flagged for review. A webhook payload containing the assessment ID, associated objects (like location, process, or equipment), and any initial qualitative data is sent to a secure orchestration service. This service fetches the full assessment record via the GET /api/assessments/{id} endpoint, along with related data from Cority's incidents, audits, and observations APIs to provide historical context for the AI model.

The orchestration layer then constructs a context-rich prompt for the LLM, which includes the assessment's scope, control descriptions, and the fetched historical data. The AI performs two key functions: 1) generating a narrative summary of the risk based on the provided data, and 2) suggesting a quantitative risk score (e.g., a 5x5 matrix score) by evaluating described consequences and likelihood against historical patterns. The results, along with a confidence score and suggested control measures, are packaged and posted back to Cority via the PATCH /api/assessments/{id} endpoint to populate the relevant fields, triggering any configured approval workflows. All prompts, responses, and data lineage are logged to a separate audit store for governance.

For rollout, we implement a phased approval workflow. Initial integrations often run in a "copilot" mode, where AI-generated narratives and scores are presented as drafts in a custom Cority dashboard or a side-panel UI extension for the risk analyst to review, modify, and approve. This human-in-the-loop step is critical for validation, building trust, and refining the model. Governance is managed through strict RBAC synced from Cority, ensuring only authorized users can trigger or approve AI-generated content, and all modifications are tracked in Cority's native audit trail. The entire data flow is designed to keep sensitive EHS data within your controlled environment, using private cloud or VPC-hosted AI models where required.

Implementation begins by mapping AI inputs to specific Cority data objects and APIs. The AI service typically ingests data from the Risk Register, JSA (Job Safety Analysis) modules, incident reports, and audit findings via Cority's REST API or a scheduled data sync. A secure middleware layer handles authentication, data transformation, and prompt context assembly before calling the LLM (e.g., OpenAI, Anthropic, or a private model). Generated outputs—such as risk narratives, control recommendations, or updated quantitative scores—are written back to designated custom fields or linked records via API, with a full audit trail logging the AI's source data, prompt, and generated content for every transaction.

A phased rollout mitigates risk and builds user trust. Phase 1 (Pilot) targets a single, high-volume risk assessment type, like new equipment JSA reviews, with AI running in a 'draft assistant' mode. Assessors review and approve all AI suggestions before any system-of-record updates. Phase 2 (Expansion) enables auto-scoring for low-severity, routine assessments based on historical data patterns, with human-in-the-loop approval still required for high-severity risks. Phase 3 (Integrated Workflow) embeds AI as a background service that pre-populates assessments and flags anomalies in real-time, shifting the human role to validation and exception handling. This approach moves from 'AI as a tool' to 'AI as a co-pilot' within 6-12 months.

Governance is enforced through technical and procedural controls. Role-based access in Cority determines who can trigger AI actions and approve outputs. All AI-generated content is watermarked and stored with its source data lineage. A regular review cycle—often tied to the Management of Change (MOC) or management review process—evaluates AI performance, checks for model drift against historical risk outcomes, and updates the risk assessment logic prompts. This ensures the AI remains aligned with the organization's risk tolerance and Cority's evolving data model, maintaining compliance with records management policies and regulatory expectations for demonstrable risk control.