Audit technology loss scenario integrity

What it's for

Gives a CTO, finance partner or investor a defensible answer to the question behind every tail-risk number: did we include every technology exposure exactly once on one point-in-time financial basis?

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.

{
  "currency": "USD",
  "evidence_cutoff": 100,
  "expected_exposure_ids": [
    "exposure-1",
    "exposure-2",
    "exposure-3",
    "exposure-4",
    "exposure-5"
  ],
  "expected_source_ids": [
    "risk-register",
    "finance-plan"
  ],
  "exposure_snapshots": [
    {
      "currency": "USD",
      "economic_loss_source_ids": [
        "loss-source-1"
      ],
      "evidence_verified": true,
      "id": "exposure-1",
      "loss_by_scenario": {
        "adverse": 10,
        "ordinary": 1,
        "severe": 100
      },
      "observed_at": 90,
      "price_basis_id": "real-2026",
      "scenario_set_version": "technology-loss-v1",
      "scope_size": 10,
      "shared_loss_group_ids": [],
      "source_ids": [
        "risk-register",
        "finance-plan"
      ]
    },
    {
      "currency": "USD",
      "economic_loss_source_ids": [
        "loss-source-2"
      ],
      "evidence_verified": true,
      "id": "exposure-2",

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.

{
  "configuration": {
    "currency": "USD",
    "evidence_cutoff": 100,
    "minimum_scope_size": 5,
    "price_basis_id": "real-2026",
    "scenario_set_version": "technology-loss-v1"
  },
  "decision": "publish",
  "detail_truncated": false,
  "failed_gates": [],
  "finding": "loss_scenario_ledger_integrity_verified",
  "findings": [],
  "governance": [
    "Loss, currency, price basis and scenario probabilities require finance or risk ownership; Git activity cannot supply them.",
    "Repeated economic lineage must be represented as an explicit shared-loss group and counted once.",
    "The audit covers aggregate technology exposures and cannot establish individual performance, intent or misconduct."
  ],
  "method": "point_in_time_zero_inclusive_loss_scenario_lineage_audit_v1",
  "summary": {
    "expected_exposure_count": 5,
    "expected_source_cell_count": 10,
    "maximum_aggregate_direct_loss": 1500,
    "missing_source_cell_count": 0,
    "observed_exposure_count": 5,
    "parsed_expected_exposure_count": 5,
    "privacy_suppressed_count": 0,
    "scenario_count": 3,
    "undeclared_lineage_reuse_fraction": 0,
    "unexpected_source_cell_count": 0,
    "unverified_fraction": 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 the expected aggregate exposure and source perimeter, scenario set/version, finance cutoff, currency, real/nominal price basis and privacy threshold before inspecting observations.
2. 2 Reconcile one zero-inclusive loss vector per expected exposure to every declared scenario, reject future or version-mismatched evidence, and preserve missing, unexpected and unverified cells rather than silently dropping them.
3. 3 Trace each economic-loss source across exposures; accept repeated lineage only when every claimant declares a common shared-loss group, otherwise block publication before portfolio arithmetic can double count it.

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.

Calibrating it to your org

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 a technology lossscenario ledger as" }
  → finds "audit_technology_loss_scenario_integrity"

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

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

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

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