Optimize risk mitigation portfolio

What it's for

Shows boards which combination of reliability, security, resilience, or compliance controls buys the most risk reduction without hiding dependencies or residual tail loss.

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.

{
  "baseline_loss_scenarios": [
    1000,
    300,
    50
  ],
  "budget": 150,
  "mitigations": [
    {
      "cost": 40,
      "id": "foundation",
      "scenario_loss_reductions": [
        100,
        80,
        20
      ]
    },
    {
      "cost": 80,
      "dependency_ids": [
        "foundation"
      ],
      "id": "tail-control",
      "scenario_loss_reductions": [
        600,
        120,
        0
      ]
    },
    {
      "cost": 100,
      "exclusion_ids": [
        "tail-control"
      ],
      "id": "alternative",
      "scenario_loss_reductions": [
        350,
        150,
        20
      ]
    }
  ],
  "scenario_probabilities": [
    0.1,

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.

{
  "assumptions": [
    "Loss reductions are incremental, scenario-aligned, finance-approved, and capped at baseline loss; dependencies and exclusions represent technical and contractual feasibility.",
    "Mitigation cost and avoided loss share one currency, horizon, discount/tax boundary, and counterfactual; implementation failure and risk compensation are encoded in scenarios.",
    "The greedy fallback is deterministic but does not prove optimality; material decisions above the exact boundary require an approved integer optimizer or shortlisted exact rerun."
  ],
  "configuration": {
    "budget": 150,
    "maximum_residual_cvar_loss": null,
    "tail_probability": 0.1
  },
  "decision": "risk_mitigation_portfolio_supported",
  "method": "dependency_aware_expected_loss_residual_cvar_mitigation_portfolio_v1",
  "mitigation_diagnostics": [
    {
      "cost": 40,
      "dependency_ids": [],
      "exclusion_ids": [],
      "mitigation_id": "foundation",
      "selected": true,
      "standalone_expected_loss_reduction": 46
    },
    {
      "cost": 80,
      "dependency_ids": [
        "foundation"
      ],
      "exclusion_ids": [],
      "mitigation_id": "tail-control",
      "selected": true,
      "standalone_expected_loss_reduction": 96
    },
    {
      "cost": 100,
      "dependency_ids": [],
      "exclusion_ids": [
        "tail-control"
      ],
      "mitigation_id": "alternative",
      "selected": false,
      "standalone_expected_loss_reduction": 92
    }
  ],
  "solver_diagnostics": {

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze one baseline joint loss distribution, incremental mitigation reductions, costs, dependencies, exclusions, budget, and tail tolerance over a common risk horizon.
2. 2 Cap combined reductions at baseline loss, enumerate all feasible portfolios through the exact boundary, and calculate spend, expected net loss avoided, and residual CVaR.
3. 3 Above the exact boundary use the disclosed dependency-closure greedy fallback, require an approved integer solver or shortlisted exact rerun for material commitments, and stress implementation failure and risk compensation.

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": "select a dependency and exclusionfeasible mitigation" }
  → finds "optimize_risk_mitigation_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_risk_mitigation_portfolio", "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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