RISK INTELLIGENCE

Risk is not a colour
on a matrix.

Safety management exists to identify hazards, assess and control risk, and confirm that the controls remain effective. In-time intelligence should shorten that learning loop without removing human accountability.

Frame

Define the operation, decision, exposure and risk criteria before analysing the data.

Detect and assess

Combine signals, operating context and uncertainty to identify precursors and changing risk.

Act

Escalate, constrain, reroute or pause through an accountable operational authority.

Assure

Verify that the control worked, watch for unintended effects and feed learning back into the system.

WHERE AI & ML CAN HELP

Find weak signals. Test possible futures.

Link anomalies across vehicle, weather, traffic, infrastructure and human-performance data.
Estimate exposure and detect changes that static reviews may miss.
Forecast how margins could narrow and compare possible interventions.
Monitor model drift and whether a safety control continues to perform.

WHERE THE LIMIT REMAINS

Prediction is not authority.

Models do not define what risk is acceptable.
Confidence scores do not turn incomplete data into truth.
Correlation does not automatically establish a cause.
Authorised automation may execute bounded actions; human and organisational accountability for the design, authority and consequences remains.

PERMANENT RISK WATCH

Questions Sentinel keeps open.

Is exposure changing faster than the risk picture?

Are leading indicators detecting change—or merely creating noise?

What happens when data quality, coverage or latency degrades?

Is the model still valid for today’s operation and environment?

How much time remains between detection and useful intervention?

Did the control reduce risk, transfer it, or create a new dependency?

PRIMARY EVIDENCE

NASA · IASMS for Upper Class E operations NASA · IASMS for Part 139 airports NIST · AI Risk Management Framework

IASMS research describes predictive modelling and reactive, proactive and predictive analytics for detecting hazards and risk precursors. This page interprets that research as an editorial risk lens; it is not an approved risk method.