AV Observability Framework

Intent is the foundational layer everything else depends on. Before selecting tools, designing dashboards, or writing collection logic, the institution must articulate what observability is expected to protect under its risk tolerance.

• Define institutional expectations, risks, and non-negotiables.
• Specify what must not fail — experience, safety, continuity, trust.
• Set scope boundaries and ownership.

Outcomes convert intent into measurable criteria. They define success in concrete terms that can be validated across phases.

• Translate intent into measurable service expectations.
• Identify decision points — who needs signals, when, and for what action.
• Establish success criteria for pilot, expansion, and steady-state.

Health modeling defines healthy states, thresholds, and required evidence.

• Model health as states, thresholds, and evidence.
• Define dependencies across device state, control logic, network, and service layers.
• Enumerate failure modes and observations that prove or disprove them.

Signal architecture translates the health model into concrete data requirements.

• Define required data points, sources, and collection cadence.
• Design data pipelines: acquire, parse, normalize, route, and retain.
• Establish topology and integration surfaces.
• Set lifecycle expectations for retention, access, backup, and restore.

Activation is where architecture meets operation.

• Start small, validate outcomes, and expand deliberately.
• Train practitioners and document operational runbooks.
• Measure drift over time and refine the model continuously.