Optimize shadow AI governance portfolio

What it's for

Answers what IT should do after finding shadow AI: what to block, broker, migrate, govern or monitor to reduce correlated exposure without needlessly destroying legitimate business value.

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.

{
  "governance_options": [
    {
      "dependency_option_ids": [],
      "detection_coverage_scenarios": [
        0.6,
        0.6
      ],
      "direct_loss_multiplier_scenarios": [
        1,
        1
      ],
      "evidence_verified": true,
      "exclusion_option_ids": [],
      "exposure_reduction_fraction": 0.4,
      "id": "coding-monitor",
      "implementation_cost": 0,
      "incident_probability_scenarios": [
        0.1,
        0.2
      ],
      "is_current_state": true,
      "operating_cost_scenarios": [
        500,
        500
      ],
      "policy_type": "monitor",
      "resource_demands": {
        "security-review-days": 0
      },
      "satisfied_control_ids": [
        "broker-control"
      ],
      "service_class_id": "coding-assistant",
      "value_retention_fraction": 1
    },
    {
      "dependency_option_ids": [],
      "detection_coverage_scenarios": [
        0.95,
        0.9
      ],
      "direct_loss_multiplier_scenarios": [
        0.2,

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.

{
  "assumptions": [
    "Class counts, retained business value, direct loss, incident probability, detection coverage, costs and control effects are scenario-aligned prospective aggregate evidence.",
    "Residual services within a provider group compound through joint survival and one common incident state, so shared value at risk is priced once rather than per class.",
    "Block, broker, migrate, allow-with-controls and monitor are complete executable options with real dependencies, exclusions, budget and capacity demands."
  ],
  "decision": "execute_governed_shadow_ai_control_portfolio",
  "economics": {
    "expected_loss": 9030.2538,
    "expected_net_value": 2969.7462,
    "expected_net_value_gain_vs_current": 79592.8444,
    "implementation_cost": 5000,
    "loss_conditional_value_at_risk": 35191.269,
    "loss_value_at_risk": 35191.269,
    "risk_adjusted_score": -14625.8883
  },
  "limitations": [
    "Exact mode proves optimality only for the supplied finite model; beam mode is feasible and deterministic but does not prove global optimality.",
    "Selection governs aggregate service classes and does not authorize person-level surveillance, discipline, security accusations or employment decisions."
  ],
  "method": "multiple_choice_shadow_ai_governance_joint_survival_cvar_v1",
  "pareto_frontier": [
    {
      "expected_net_value": 2969.7462,
      "implementation_cost": 5000,
      "loss_cvar": 35191.269,
      "option_ids": [
        "coding-broker"
      ]
    },
    {
      "expected_net_value": -76623.0982,
      "implementation_cost": 0,
      "loss_cvar": 265840.809,
      "option_ids": [
        "coding-monitor"
      ]
    }
  ],
  "residual_service_exposure": {
    "coding-assistant": [
      1,
      1.5
    ]

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Validate scenario-aligned shadow counts, business value, uncontrolled loss and every executable governance option's exposure reduction, retention, detection, incident, cost and controls.
2. 2 Reject portfolios breaching residual exposure, detection, controls, relations, budget or scarce security/privacy capacity.
3. 3 Combine residual provider-group incidents through joint survival plus one common state, optimize expected net value minus CVaR exactly or by disclosed beam and expose the Pareto frontier.

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": "choose block broker migrate allowwithcontrols or" }
  → finds "optimize_shadow_ai_governance_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_shadow_ai_governance_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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