Threat hypothesis
Define the failure mechanism, affected asset population, decision objective, and evidence needed to establish a causal relationship.
- Threat and sub-threat definition
- Unit of analysis
- Decision and success measures
Model-expansion blueprint
Adding Threats
A complete prompt and sample engineering readiness checklist for a new risk threat
Sample output ready
This presentation was generated from the prompt below using Navigator's synthetic demonstration portfolio.
This presentation was generated from the prompt below using Navigator's synthetic demonstration portfolio.
Demo prompt
Use Navigator through the connected MCP server as the primary source of evidence. You are a senior gas distribution integrity engineer. Assess whether we are ready to run a credible first-pass probability-times-consequence screen for customer-meter leakage. Check the minimum required inputs for probability, consequence, and validation against the data available in Navigator. For each input, show: * Checklist status * Priority: P1, P2, or P3 * System of record * Rough completeness % * Measured, inferred, or defaulted Priority meaning: * P1 = critical to ranking credibility * P2 = materially improves the ranking * P3 = useful refinement Return a one-page PDF that looks like an engineering readiness checklist. Include only: 1. Verdict: ready for a first-pass screen, or not yet 2. The single missing field that most changes the ranking, and why 3. The three fastest moves to get to a run, in priority order Use Navigator data rather than general industry assumptions wherever Navigator provides relevant information. Where data is thin, use reasonable defaults, flag the uncertainty, and do not wait for a perfect inventory.
Data and integration plan
Identify the information required, where it commonly resides, and how governed MCP capabilities could retrieve it.
- Required fields and source systems
- Identifiers, topology, and time alignment
- Privacy and access controls
Validation and deployment
Establish the tests and approvals required before a candidate signal changes a production risk result.
- Data-quality and bias assessment
- Back-test and shadow-model comparison
- Governance, versioning, and monitoring