Audit technology risk appetite integrity

What it's for

Lets boards, CTOs and investors distinguish a real technology-risk appetite—with reconciled limits, owners, scenarios and actions—from a slide containing unexecutable risk language.

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.

{
  "appetite": {
    "approved_at": 90,
    "approved_by_roles": [
      "board",
      "cfo",
      "cro"
    ],
    "confidence_level": 0.95,
    "currency": "EUR",
    "cvar_loss_limit": 180,
    "economic_capital_limit": 150,
    "effective_at": 100,
    "evidence_verified": true,
    "expected_loss_limit": 40,
    "horizon_periods": 4,
    "id": "board-risk-2027",
    "minimum_raroc": 0.1,
    "version": "v1"
  },
  "evidence_cutoff": 100,
  "expected_unit_ids": [
    "platform",
    "data"
  ],
  "required_approval_roles": [
    "board",
    "cfo",
    "cro"
  ],
  "risk_limits": [
    {
      "breach_action_id": "board-escalation",
      "cvar_loss_limit": 180,
      "economic_capital_limit": 150,
      "evidence_verified": true,
      "expected_loss_limit": 40,
      "id": "root",
      "node_type": "root",
      "observed_at": 100,
      "owner_role": "cro",
      "parent_limit_id": null,
      "unit_id": null
    },

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.

{
  "appetite_limits": {
    "cvar_loss": 180,
    "economic_capital": 150,
    "expected_loss": 40
  },
  "breaches": [],
  "configuration": {
    "appetite_id": "board-risk-2027",
    "confidence_level": 0.95,
    "currency": "EUR",
    "horizon_periods": 4,
    "maximum_diversification_credit_fraction": 0.5,
    "minimum_raroc": 0.1,
    "version": "v1"
  },
  "decision": "publish",
  "detail_truncated": false,
  "failed_gates": [],
  "finding": "risk_appetite_executable_and_within_limits",
  "findings": [],
  "governance": [
    "The board and finance own appetite, currency, horizon and capital capacity; risk owners own scenarios, limits and escalation actions.",
    "Allocated child limits may exceed a parent only within the explicit diversification-credit ceiling; coherent scenarios must substantiate the diversification.",
    "A breach is an aggregate financial-control event, not evidence about a person, team, intent or employment decision."
  ],
  "hierarchy_diversification": [
    {
      "diversification_credit_fraction": 0,
      "limit_id": "root",
      "metric": "expected_loss"
    },
    {
      "diversification_credit_fraction": 0,
      "limit_id": "root",
      "metric": "cvar_loss"
    },
    {
      "diversification_credit_fraction": 0,
      "limit_id": "root",
      "metric": "economic_capital"
    }
  ],
  "method": "board_appetite_scenario_limit_tree_integrity_v1",

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 appetite version, approval roles and times, evidence cutoff, currency, horizon, confidence, expected-loss/CVaR/economic-capital limits and minimum RAROC before reading current exposures.
2. 2 Reconstruct the complete root/group/unit limit tree, reject cycles, orphans, duplicate or missing unit coverage, future/unverified evidence, incoherent loss limits and any root value that does not equal the approved appetite.
3. 3 Aggregate each node from zero-inclusive coherent unit-loss scenarios, compute expected loss/CVaR/economic capital, quantify implied parent-child diversification credit and require an executable action for every current breach.

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 whether board technologyrisk appetite is" }
  → finds "audit_technology_risk_appetite_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_technology_risk_appetite_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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