Optimize cyber control portfolio

Find a budget-, capacity-, availability- and defense-depth-feasible cyber-control portfolio on a nonlinear attack-path graph, recomputing unique-asset expected loss and CVaR under dependencies, exclusions and multiplicative effects.

Network & dependency analysis Optimize CTOs Tech CEOs IT managers Team leads Investors

What it's for

Shows the cyber portfolio leadership should fund after common scenarios, control dependencies, defense in depth, overlapping asset exposure and downside-tail appetite are optimized together.

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.

Field	Type	Role	Required
beam_width	integer ≥ 1, ≤ 10000	Numerical control	Optional
budget	number ≥ 0	Your calibration	Yes
business_assets	array of objects (3 fields) ≥ 1 item	Evidence	Yes
candidate_controls	array of objects (9 fields)	Evidence	Yes
capacity_units	number ≥ 0	Your calibration	Yes
control_path_effects	array of objects (5 fields)	Evidence	Yes
exact_enumeration_limit	integer ≥ 1, ≤ 1000000	Your calibration	Optional
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
maximum_control_unavailability_probability	number ≥ 0, ≤ 1	Your calibration	Optional
maximum_cvar_total_loss	any	Your calibration	Optional
maximum_expected_total_loss	any	Your calibration	Optional
maximum_uncontrolled_critical_path_fraction	number ≥ 0, ≤ 1	Your calibration	Optional
minimum_distinct_control_stages_per_critical_path	integer ≥ 1, ≤ 4	Your calibration	Optional
risk_aversion	number ≥ 0	Your calibration	Optional
scenarios	array of objects (3 fields) ≥ 2 items	Evidence	Yes
tail_probability	number ≥ 0.001, ≤ 0.5	Your calibration	Optional
threat_paths	array of objects (7 fields)	Evidence	Yes

Each `candidate_controls` record

Field	Type	Required
available_scenario_ids	array of string	Yes
capacity_units	number (≥ 0)	Yes
control_stage	one of "prevent", "detect", "respond", "recover"	Yes
dependency_control_ids	array of string	Yes
evidence_verified	boolean	Yes
exclusion_control_ids	array of string	Yes
id	string (non-empty)	Yes
operating_cost_scenarios	array of number (≥ 2 items)	Yes
upfront_cost	number (≥ 0)	Yes

Example input

{
  "budget": 10,
  "business_assets": [
    {
      "evidence_verified": true,
      "id": "payments",
      "value_at_risk": 100
    }
  ],
  "candidate_controls": [
    {
      "available_scenario_ids": [
        "base",
        "campaign"
      ],
      "capacity_units": 1,
      "control_stage": "prevent",
      "dependency_control_ids": [],
      "evidence_verified": true,
      "exclusion_control_ids": [],
      "id": "prevent-control",
      "operating_cost_scenarios": [
        0,
        0
      ],
      "upfront_cost": 5
    },
    {
      "available_scenario_ids": [
        "base",
        "campaign"
      ],
      "capacity_units": 1,
      "control_stage": "detect",
      "dependency_control_ids": [],
      "evidence_verified": true,
      "exclusion_control_ids": [],
      "id": "detect-control",
      "operating_cost_scenarios": [
        0,
        0
      ],
      "upfront_cost": 5
    },

Truncated for display — the full payload is 170 lines.

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.

Example output

{
  "assumptions": [
    "Business assets are unique value sources; threat paths are versioned aggregate models; scenario path probabilities, direct losses, control effects, availability and costs share one horizon and preserve common-cause futures.",
    "Selected control effects multiply residual path probability and unique asset loss unions overlapping paths, preventing additive control credit and repeated asset value. Upfront, operating, direct response and business loss are non-overlapping.",
    "Defense-in-depth requires distinct prevent/detect/respond/recover stages on critical paths, not several controls with the same label. Dependencies, exclusions and scenario availability represent tested feasibility, not nominal product features.",
    "Exact mode certifies only the supplied finite model; beam output is heuristic. Neither establishes causal effectiveness, compliance, legal privilege, breach absence or authority to investigate, disclose, surveil, procure, block or change production."
  ],
  "constraints": {
    "budget": 10,
    "capacity_units": 2,
    "maximum_control_unavailability_probability": 0,
    "maximum_cvar_total_loss": null,
    "maximum_expected_total_loss": null,
    "maximum_uncontrolled_critical_path_fraction": 0,
    "minimum_distinct_control_stages_per_critical_path": 2,
    "risk_aversion": 0,
    "tail_probability": 0.1
  },
  "control_pareto_frontier": [
    {
      "cvar_total_cost_and_loss": 102.56,
      "defense_in_depth_feasible": false,
      "expected_total_cost_and_loss": 71.508,
      "risk_feasible": false,
      "selected_control_ids": [],
      "upfront_cost": 0
    },
    {
      "cvar_total_cost_and_loss": 29.1984,
      "defense_in_depth_feasible": false,
      "expected_total_cost_and_loss": 21.2139,
      "risk_feasible": false,
      "selected_control_ids": [
        "prevent-control"
      ],
      "upfront_cost": 5
    },
    {
      "cvar_total_cost_and_loss": 29.1984,
      "defense_in_depth_feasible": false,
      "expected_total_cost_and_loss": 21.2139,
      "risk_feasible": false,
      "selected_control_ids": [
        "detect-control"

Truncated for display — the full payload is 134 lines.

How it works

Network & dependency analysis — Trace how load, failure and knowledge propagate through a graph of teams, services or components.

1 Freeze unique assets, coherent scenario path probabilities/direct losses, executable controls, stage labels, full costs, availability, relations and prospectively tested path effects.
2 Enumerate dependency-closed subsets when tractable or disclose deterministic beam search; reject relation, budget, capacity, availability, defense-depth, expected-loss and CVaR infeasibility.
3 For every candidate multiply residual path probabilities, union overlapping losses per unique asset, add direct and operating loss, then expose the risk/resource Pareto frontier and certificate boundary.

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.

Assumptions & guardrails

Nodes, edges, direction, time window, missing-link policy, and aggregation boundary represent the coordination or dependency mechanism of interest.
Candidate effects compose multiplicatively, scenario vectors share one order and horizon, costs and loss sources are non-overlapping, and control stage plus availability reflect tested operation rather than nominal features.
Structural centrality, fragility, or clustering describes a modeled graph and must not be interpreted as intent, guilt, or personal value.
Exact mode certifies only the submitted finite model and beam mode is heuristic; output is not a compliance finding, causal guarantee, procurement instruction or authority to investigate, surveil, disclose, block or change production.

Minimum evidence

business_assets: at least 1 rows/items
threat_paths: required and organization-defined
scenarios: at least 2 rows/items
candidate_controls: required and organization-defined
control_path_effects: required and organization-defined
budget: required and organization-defined
capacity_units: required and organization-defined

How to validate it

Validate on held-out periods or aggregate units, perturb edge definitions and missing links, and report sensitivity to graph construction before using structural rankings.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

one immutable optimization projection joining the same asset/path/scenario version to a governed candidate-control registry, preserving dependency closure, effect composition, delivery feasibility, common-mode futures and unique asset identity
candidate executability, causal effect evidence and composition, value/cost/currency/horizon perimeter, scenario probability and dependence, availability, dependencies/exclusions, delivery capacity, defense-depth, expected/CVaR appetite, solver boundary, pseudonymization and security/risk/finance/architecture approval

Calibration workflow

1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
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": "find a budget capacity availability and" }
  → finds "optimize_cyber_control_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_cyber_control_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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