Audit AI output IP provenance integrity

Audit aggregate AI outputs against the exact model and provider terms effective at generation, pre-commercialization similarity evidence, counsel-owned ownership/use rules, human review and any claimed indemnity coverage.

Statistical audit & measurement Audit CTOs Tech CEOs IT managers Team leads Investors

What it's for

Makes AI-output commercialization auditable: leaders can see which artifacts have exact provenance, complete similarity evidence and real—not assumed—risk transfer.

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
generation_policies	array of objects (14 fields)	Evidence	Yes
indemnity_contracts	array of objects (11 fields)	Evidence	Yes
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
minimum_evidence_coverage	number ≥ 0, ≤ 1	Your calibration	Optional
output_artifacts	array of objects (11 fields)	Evidence	Yes
similarity_scans	array of objects (9 fields)	Evidence	Yes

Each `generation_policies` record

Field	Type	Required
commercial_use_allowed	boolean	Yes
evidence_verified	boolean	Yes
id	string (non-empty)	Yes
indemnity_contract_id	string,null (non-empty)	Yes
maximum_similarity_score	number (≥ 0, ≤ 1)	Yes
model_version_hash	string (non-empty)	Yes
output_ownership_asserted	boolean	Yes
provider_id	string (non-empty)	Yes
required_human_review	boolean	Yes
required_similarity_corpus_ids	array of string	Yes
system_id	string (non-empty)	Yes
terms_hash	string (non-empty)	Yes
valid_from_ms	number (≥ 0)	Yes
valid_until_ms	number,null (≥ 0)	Yes

Example input

{
  "generation_policies": [
    {
      "commercial_use_allowed": true,
      "evidence_verified": true,
      "id": "output-policy-v1",
      "indemnity_contract_id": "indemnity-v1",
      "maximum_similarity_score": 0.8,
      "model_version_hash": "sha256:model-v1",
      "output_ownership_asserted": true,
      "provider_id": "provider-a",
      "required_human_review": true,
      "required_similarity_corpus_ids": [
        "source-code",
        "licensed-assets"
      ],
      "system_id": "code-assistant",
      "terms_hash": "sha256:terms-v1",
      "valid_from_ms": 1000,
      "valid_until_ms": 10000
    }
  ],
  "indemnity_contracts": [
    {
      "claim_limit": 100000,
      "commercial_output_covered": true,
      "covered_jurisdiction_ids": [
        "eu"
      ],
      "deductible": 1000,
      "defense_duty": true,
      "evidence_verified": true,
      "exclusions_reviewed": true,
      "id": "indemnity-v1",
      "provider_id": "provider-a",
      "valid_from_ms": 1000,
      "valid_until_ms": 10000
    }
  ],
  "output_artifacts": [
    {
      "aggregate_product_id": "product-a",
      "commercialized_at_ms": 4000,
      "evidence_verified": true,

Truncated for display — the full payload is 71 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

{
  "artifact_diagnostics": [
    {
      "aggregate_product_id": "product-a",
      "artifact_id": "output-artifact-1",
      "failed_gates": [],
      "indemnity_claim_limit": 100000,
      "indemnity_contract_id": "indemnity-v1",
      "indemnity_deductible": 1000,
      "maximum_similarity_score": 0.2,
      "policy_id": "output-policy-v1",
      "similarity_scan_id": "similarity-scan-1"
    }
  ],
  "assumptions": [
    "Policies, provider terms, model versions, output hashes, scans, corpus manifests and indemnity contracts are immutable point-in-time records at aggregate artifact/product grain.",
    "Similarity thresholds, reference-license compatibility, output ownership, commercial-use permission and indemnity interpretation are governed by local counsel; the audit only applies submitted rules consistently.",
    "No indemnity is treated as zero risk reduction rather than an integrity failure, while a claimed indemnity must reconcile to provider, time, jurisdiction, coverage, limit, defense duty and reviewed exclusions."
  ],
  "counts": {
    "generation_policies": 1,
    "indemnity_contracts": 1,
    "output_artifacts": 1,
    "similarity_scans": 1,
    "valid_output_artifacts": 1
  },
  "decision": "ai_output_ip_evidence_ready_for_counsel_review",
  "evidence": {
    "gate_pass": true,
    "minimum_required_fraction": 0.95,
    "verified_fraction": 1
  },
  "failure_counts": {},
  "integrity_pass": true,
  "limitations": [
    "Passing is not proof of copyrightability, ownership, non-infringement, enforceability, originality, legal compliance or permission to commercialize.",
    "A similarity flag is an evidence-review trigger, never a finding about infringement, intent, a claimant or any contributor; the function cannot discover missing reference corpora or fraudulent records."
  ],
  "method": "point_in_time_ai_output_ip_provenance_similarity_indemnity_audit_v1",
  "truncated_detail_rows": 0
}

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 Reconcile every canonical output hash to its point-in-time generation policy, observed model-version and terms hashes, aggregate product, jurisdiction and commercialization time.
2 Require a complete versioned similarity scan over every mandated corpus before commercialization, escalating unresolved above-threshold matches while accepting counsel-approved compatible licenses.
3 Apply human-review and commercial-use gates, and independently reconcile any claimed indemnity to provider, effective dates, jurisdiction, commercial coverage, limit, defense duty, exclusions and evidence.

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

Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
Output, policy, model, terms, corpus, scan and contract records are immutable and complete; counsel—not the model—owns ownership, originality, similarity, license and coverage interpretations.
Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
Passing is readiness for counsel review, not proof of copyrightability, ownership, originality, non-infringement, enforceability or permission to commercialize; a similarity flag is never a legal finding.

Minimum evidence

generation_policies: required and organization-defined
output_artifacts: required and organization-defined
similarity_scans: required and organization-defined
indemnity_contracts: required and organization-defined

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

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

point-in-time output-policy-scan-contract reconciliation that never treats a model-generated similarity score as a legal finding
artifact/product perimeter, model/terms identity, counsel ownership/use/license interpretation, corpus completeness, similarity threshold, review timing, jurisdictions, indemnity interpretation, evidence gates and escalation owner

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": "audit aggregate ai outputs against the" }
  → finds "audit_ai_output_ip_provenance_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_ai_output_ip_provenance_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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