0. Foundational Interview
Capture the operational and business context that determines BCDR risk appetite.
Readiness score
-
High gaps
-
Medium gaps
-
RTO vs MTD
-
Reputation exposure
-
Interactive Companion
Select critical services, model composite availability, assess operational readiness, and validate commitment feasibility.
Source baseline: Microsoft Azure (Microsoft Online Services SLA (WW), May 2026 (verified 2026-05-15))
Use the five-pass model from Microsoft Learn to interpret definitions, measurement, exclusions, and claim process. Local snapshot: src/data/sla-snapshots/azure-sla-guide.html.
Coverage status: all SLA-indexed services are listed in the selector. Composite calculations include selected modeled options.
Quick Azure cross-check: AzureCharts SLA board (community mirror). Official commitment source remains Microsoft published SLA terms.
Autosave is browser-local only. Export or print for durable records.
Capture the operational and business context that determines BCDR risk appetite.
Readiness score
-
High gaps
-
Medium gaps
-
RTO vs MTD
-
Reputation exposure
-
Add only services in the transaction path. Each service field is searchable inline.
Catalog coverage: 689 services total (Azure 128, AWS 321, GCP 165, OCI 75).
Modeled availability upper bound
-
Monthly downtime budget
-
Yearly downtime budget
-
Internal reliability target
-
Maximum supportable commitment
-
Feasibility check
-
Commitment risk index
-
Gap over monthly budget
-
Commitment risk statement
-
Previous readiness
N/A
Current readiness
-
Readiness trend
-
SLA contractual coverage
-
Contract confidence
-
Coverage interpretation
-
Recommendations are filtered from APRL using your currently selected resources.
Checks are provider-specific for single-cloud scope. Multi-cloud scope evaluates each provider separately.
Prioritized findings and actions based on interview inputs, commitment feasibility, and selected resources.
Dependency failure scenario
-
Regional outage scenario
-
Data corruption scenario
-
Dependency budget burn
-
Regional budget burn
-
Data budget burn
-
Real incident duration
-
Modeled worst-case duration
-
Outage probability context
-
Worst-case outage impact
-
SLA penalty exposure
-
Estimated churn exposure
-
Visualize monthly SLA budget against real and modeled outage durations.
Translate risk findings into an explicit leadership decision.
Each high or medium finding needs an owner, due date, and risk reduction action.
This prototype uses local state only; no server-side data persistence is enabled.
No export yet in this browser session.