Optimize AI data rights remediation portfolio

What it's for

Answers the executive question after a data-rights review: what should we license, replace, delete, disable or retrain first to preserve the most AI value while reducing correlated downside within real legal and engineering capacity?

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.

{
  "maximum_budget": 100000,
  "remediation_actions": [
    {
      "action_type": "license",
      "cost": 50000,
      "dependency_action_ids": [],
      "execution_verified": true,
      "group_loss_reduction_fraction": 0.9,
      "id": "license-support-corpus",
      "legal_approved": true,
      "loss_reduction_fraction": 0.9,
      "resource_demands": {
        "legal-review-days": 5
      },
      "retained_value_fraction": 1,
      "risk_item_id": "support-corpus-use"
    },
    {
      "action_type": "replace",
      "cost": 80000,
      "dependency_action_ids": [],
      "execution_verified": true,
      "group_loss_reduction_fraction": 0.98,
      "id": "replace-support-corpus",
      "legal_approved": true,
      "loss_reduction_fraction": 0.98,
      "resource_demands": {
        "legal-review-days": 2
      },
      "retained_value_fraction": 0.9,
      "risk_item_id": "support-corpus-use"
    }
  ],
  "resource_capacities": [
    {
      "capacity": 5,
      "id": "legal-review-days"
    }
  ],
  "risk_aversion": 0.5,
  "risk_items": [
    {
      "asset_group_id": "support-corpus",

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": [
    "Scenario losses and supported values share one frozen horizon, currency, discount basis and rights interpretation; probabilities are mutually exclusive and exhaustive.",
    "Direct loss reduction, value retention, execution evidence and shared-lineage reduction are measured action effects, not vendor claims or LLM judgments.",
    "Shared asset-group loss is counted once and survives at the weakest remediated member, preventing duplicated benefits from correlated lineage actions."
  ],
  "decision": "execute_governed_data_rights_remediation_portfolio",
  "economics": {
    "conditional_value_at_risk": 80000,
    "expected_loss": 28000,
    "expected_net_value": 412000,
    "expected_net_value_gain_vs_feasible_baseline": null,
    "expected_supported_value": 490000,
    "remediation_cost": 50000,
    "risk_adjusted_score": 372000,
    "value_at_risk": 80000
  },
  "limitations": [
    "Exact mode proves optimality only for the supplied finite action model; beam mode is deterministic and feasible but does not prove global optimality.",
    "Selection is internal decision support, not legal advice, rights clearance, deletion certification, deployment authority or a judgment about any person or rights holder."
  ],
  "method": "scenario_cvar_ai_data_rights_remediation_portfolio_v1",
  "pareto_frontier": [
    {
      "action_ids": [
        "license-support-corpus"
      ],
      "conditional_value_at_risk": 80000,
      "cost": 50000,
      "expected_net_value": 412000
    },
    {
      "action_ids": [
        "replace-support-corpus"
      ],
      "conditional_value_at_risk": 16000,
      "cost": 80000,
      "expected_net_value": 355400
    }
  ],
  "resource_usage": {
    "legal-review-days": {
      "capacity": 5,
      "used": 5

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 Enumerate one optional governed action per risk item, enforcing mandatory remediation, cross-action dependencies, budget, legal/execution evidence and resource capacity.
2. 2 Recompute direct residual loss, retained supported value and shared asset-group loss in every coherent scenario; shared contamination survives at the weakest remediated member.
3. 3 Maximize expected net value minus CVaR appetite exactly when tractable or by disclosed deterministic economic beam, and expose the non-dominated value-tail-cost 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 license replace delete disable or" }
  → finds "optimize_ai_data_rights_remediation_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_ai_data_rights_remediation_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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