Audit service continuity recovery evidence integrity

Audit whether each critical service has a current, independently reviewed recovery plan whose complete capability/dependency path, backup, restore, failover, communications, RTO and RPO were proven in a recent production-representative exercise.

What it's for

Shows executives whether a recovery plan was actually exercised across the full critical path and achieved its RTO/RPO, instead of treating a runbook or backup checkbox as resilience.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

Field Type Role Required
as_of_ms number ≥ 0 Your calibration Yes
continuity_plans array of objects (12 fields) Evidence Yes
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_evidence_age_days number ≥ 0 Your calibration Optional
minimum_independent_evidence_count_per_type integer ≥ 1, ≤ 100 Your calibration Optional
minimum_verified_evidence_fraction number ≥ 0, ≤ 1 Your calibration Optional
recovery_evidence array of objects (14 fields) ≥ 0 items Evidence Yes
recovery_exercises array of objects (11 fields) Evidence Yes
required_evidence_type_ids array of string ≥ 1 item Evidence Optional
service_dependencies array of objects (7 fields) ≥ 0 items Evidence Yes

Each recovery_evidence record

Field Type Required
achieved_recovery_point_minutes number,null (≥ 0) Yes
achieved_recovery_time_minutes number,null (≥ 0) Yes
artifact_hash string (non-empty) Yes
dependency_id string,null Yes
evidence_type_id string (non-empty) Yes
evidence_verified boolean Yes
exercise_id string (non-empty) Yes
id string (non-empty) Yes
independently_observed boolean Yes
observed_at_ms number (≥ 0) Yes
plan_id string (non-empty) Yes
source_system_id string (non-empty) Yes
successful boolean Yes
valid_until_ms number,null (≥ 0) Yes
Example input
{
  "as_of_ms": 10000,
  "continuity_plans": [
    {
      "critical_capability_ids": [
        "charge",
        "refund"
      ],
      "effective_from_ms": 1000,
      "effective_until_ms": null,
      "evidence_verified": true,
      "id": "plan-payments",
      "independently_reviewed": true,
      "owner_present": true,
      "plan_version": "v3",
      "recovery_point_objective_minutes": 5,
      "recovery_time_objective_minutes": 60,
      "required_dependency_ids": [
        "primary-db"
      ],
      "service_id": "payments"
    }
  ],
  "recovery_evidence": [
    {
      "achieved_recovery_point_minutes": 3,
      "achieved_recovery_time_minutes": 45,
      "artifact_hash": "sha256:backup",
      "dependency_id": null,
      "evidence_type_id": "backup",
      "evidence_verified": true,
      "exercise_id": "exercise-1",
      "id": "evidence-backup",
      "independently_observed": true,
      "observed_at_ms": 9000,
      "plan_id": "plan-payments",
      "source_system_id": "source-backup",
      "successful": true,
      "valid_until_ms": null
    },
    {
      "achieved_recovery_point_minutes": 3,
      "achieved_recovery_time_minutes": 45,
      "artifact_hash": "sha256:restore",

Truncated for display — the full payload is 137 lines.

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

Example output
{
  "assumptions": [
    "The service/capability/dependency perimeter was frozen before review; plans, exercises and artifacts are immutable, tenant-scoped and point-in-time, and production-representative means the declared failure and recovery path was actually exercised.",
    "RTO and RPO are owner-governed objectives; independent source/artifact pairs establish distinct evidence, while required dependencies and customer communications remain inside the exercise perimeter."
  ],
  "decision": "service_continuity_evidence_ready_for_governed_reliance",
  "exercise_diagnostics": [
    {
      "exercise_id": "exercise-1",
      "failed_gates": [],
      "integrity_pass": true,
      "plan_id": "plan-payments"
    }
  ],
  "failed_gates": [],
  "limitations": [
    "Passing establishes submitted recovery-evidence integrity, not guaranteed availability, future recovery, contractual compliance, regulatory assurance, vendor solvency or customer retention.",
    "The function does not trigger failover, alter production, notify customers, waive contractual duties or authorize procurement; accountable continuity owners must rehearse and approve execution."
  ],
  "method": "point_in_time_service_continuity_recovery_evidence_audit_v1",
  "plan_diagnostics": [
    {
      "active_at_as_of": true,
      "failed_gates": [],
      "integrity_pass": true,
      "plan_id": "plan-payments",
      "service_id": "payments"
    }
  ],
  "service_continuity_integrity_ready": true,
  "summary": {
    "dependencies": 1,
    "evidence_rows": 5,
    "exercises": 1,
    "minimum_verified_evidence_fraction": 0.95,
    "plans": 1,
    "plans_passing": 1,
    "services": 1,
    "verified_evidence_fraction": 1
  },
  "truncated_detail_rows": 0
}

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

  1. 1 Freeze effective plan versions, critical capabilities, required dependencies, owners, RTO/RPO and complete exercise/evidence scope at the as-of cutoff.
  2. 2 Select the latest qualifying exercise per active plan, require production-representative capability/dependency coverage plus backup, restore, failover, dependency and customer-communication evidence from distinct verified source/artifact pairs.
  3. 3 Reconcile achieved recovery time and recovery point against governed objectives, dependency failover paths and verified-evidence coverage; return repair gates rather than an uptime claim.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Assumptions & guardrails

  • Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
  • The critical service/capability/dependency perimeter was complete before review and exercise artifacts are immutable, tenant-scoped, current and independently observed.
  • Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
  • Passing establishes submitted recovery-evidence integrity—not guaranteed availability, future recovery, contractual compliance, vendor solvency or authority to fail over production.

Minimum evidence

  • continuity_plans: required and organization-defined
  • service_dependencies: at least 0 rows/items
  • recovery_exercises: required and organization-defined
  • recovery_evidence: at least 0 rows/items
  • as_of_ms: required and organization-defined

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • point-in-time service-to-plan-to-critical-capability/dependency graph joined to the latest qualifying exercise and distinct source/artifact evidence, retaining failed, stale, partial and breached exercises
  • critical service/capability/dependency perimeter, effective plan version, exercise representativeness, required evidence types/independence/freshness, RTO/RPO, customer-communications duty, as-of cutoff and verified-evidence threshold

Calibration workflow

  1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
  2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
  3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
  4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
  5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
  6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "audit whether each critical service has" }
  → finds "audit_service_continuity_recovery_evidence_integrity"

gitrevio_capability_describe
  { "capability_id": "audit_service_continuity_recovery_evidence_integrity" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "audit_service_continuity_recovery_evidence_integrity", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

Related tools

Forecast customer facing service interruption loss

Forecast customer-facing outage frequency, duration, SLA credits, interrupted revenue, churn exposure and total financial VaR/CVaR using local zero-inclusive service history, compound log-normal severity and coherent shared-dependency events.

Forecasting & survival

Optimize service continuity investment portfolio

Choose one production-exercised continuity posture per service-risk unit by maximizing retained business value minus direct/common interruption loss, full cost and CVaR under RTO, RPO, residual-risk, control, dependency, budget and resource constraints.

Constrained optimization

Aggregate risk register copula

Aggregate risk-register occurrence and lognormal severity marginals through a validated Gaussian copula into expected loss, VaR, CVaR, dependence amplification, and tail shares.

Statistical audit & measurement

Allocate restless bandit interventions

Allocate scarce recurring interventions across evolving Markov units with Whittle indices, explicit indexability checks, and paired policy simulation.

Sequential Bayesian & bandits

Attribute commercial dependency tail loss

Calculate expected loss, VaR and CVaR for commercial value concentrated in shared technical components, then allocate every modeled tail-loss dollar exactly once across components with normalized negative-log survival hazard rather than overlapping leave-one-out sensitivities.

Forecasting & survival

Audit commercial resilience claim integrity

Audit resilience ROI claims against a unique commercial-source to technical-component graph: recompute each action's avoided loss under joint failure scenarios, cap support at graph-derived value, detect duplicate effects, probability drift and weak evidence, and prevent overlapping component benefits from being sold twice.

Network & dependency analysis

See every tool in Risk, tails & resilience →

Ready to See Your Engineering work clearly?

Request a free demo