Audit workforce identity access evidence integrity

Audit the point-in-time chain from an opaque workforce subject through authorized accounts, independent identity evidence, approved least-privilege grants and MFA/device-backed access events.

What it's for

Turns workforce identity and privileged access assurance into a reproducible evidence-chain audit while preserving human review, lawful basis and correction rights.

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
access_events array of objects (9 fields) ≥ 0 items Evidence Yes
access_grants array of objects (9 fields) ≥ 0 items Evidence Yes
as_of_ms number ≥ 0 Your calibration Yes
identity_accounts array of objects (9 fields) Evidence Yes
identity_evidence array of objects (12 fields) ≥ 0 items Evidence Yes
identity_subjects array of objects (12 fields) Evidence Yes
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
minimum_verified_evidence_fraction number ≥ 0, ≤ 1 Your calibration Optional

Each identity_evidence record

Field Type Required
account_id string,null Yes
artifact_hash string (non-empty) Yes
evidence_type_id string (non-empty) Yes
evidence_verified boolean Yes
id string (non-empty) Yes
independently_verified boolean Yes
legally_obtained boolean Yes
observed_at_ms number (≥ 0) Yes
source_system_id string (non-empty) Yes
subject_id string (non-empty) Yes
valid_from_ms number (≥ 0) Yes
valid_until_ms number,null (≥ 0) Yes
Example input
{
  "access_events": [
    {
      "account_id": "account-1",
      "authorization_grant_id": "grant-1",
      "evidence_verified": true,
      "id": "event-1",
      "mfa_passed": true,
      "occurred_at_ms": 9500,
      "privilege_level": "admin",
      "resource_id": "production",
      "trusted_device_passed": true
    }
  ],
  "access_grants": [
    {
      "account_id": "account-1",
      "approval_id": "approval-1",
      "evidence_verified": true,
      "granted_at_ms": 3000,
      "id": "grant-1",
      "least_privilege_reviewed": true,
      "privilege_level": "admin",
      "resource_id": "production",
      "revoked_at_ms": null
    }
  ],
  "as_of_ms": 10000,
  "identity_accounts": [
    {
      "created_at_ms": 2000,
      "disabled_at_ms": null,
      "evidence_verified": true,
      "id": "account-1",
      "identity_provider_id": "idp-core",
      "mfa_enforced": true,
      "privilege_level": "admin",
      "subject_id": "subject-opaque-1",
      "trusted_device_required": true
    }
  ],
  "identity_evidence": [
    {
      "account_id": null,

Truncated for display — the full payload is 78 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": [
    "Identity subjects are opaque authorized references; engagement, account, grant, evidence and access-event ledgers are complete, immutable, legally obtained and point-in-time.",
    "Concurrent work is relevant only through an explicit applicable disclosure policy and verified conflicting commitments; work hours, geography, language, nationality and protected traits are not identity-risk evidence."
  ],
  "decision": "identity_access_evidence_ready_for_authorized_human_review",
  "event_diagnostics": [
    {
      "account_id": "account-1",
      "event_id": "event-1",
      "failed_gates": [],
      "integrity_pass": true
    }
  ],
  "failed_gates": [],
  "grant_diagnostics": [
    {
      "account_id": "account-1",
      "active_at_as_of": true,
      "failed_gates": [],
      "grant_id": "grant-1",
      "integrity_pass": true,
      "resource_id": "production"
    }
  ],
  "identity_access_integrity_ready": true,
  "limitations": [
    "Passing establishes evidence and authorization-chain integrity, not real-world identity, employee intent, guilt, nationality, espionage, policy breach or suitability for employment.",
    "Failed gates require authorized human security review, correction rights and due process; the function takes no access, investigation, disclosure or employment action."
  ],
  "method": "point_in_time_workforce_identity_access_provenance_audit_v1",
  "subject_diagnostics": [
    {
      "active_accounts": 1,
      "authorized_accounts": 1,
      "engagement_active": true,
      "failed_gates": [],
      "integrity_pass": true,
      "subject_id": "subject-opaque-1"
    }
  ],
  "summary": {
    "access_events": 1,
    "accounts": 1,

Truncated for display — the full payload is 53 lines.

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. 1 Freeze the authorized subject/account perimeter and engagement, account, grant, evidence-validity and access-event intervals at the requested as-of time.
  2. 2 Require each declared evidence type to have enough distinct source/artifact pairs that are current, independently verified, legally obtained and evidence-verified.
  3. 3 Reconcile every access event to the same subject, account, active grant, resource and privilege boundary, then return repair gates without inferring identity or intent.

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.
  • HR/contract, IAM, approval, device and access-event ledgers are complete, immutable, tenant-scoped and point-in-time; an explicit applicable disclosure policy—not behavioral inference—defines any conflict evidence.
  • Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
  • Passing means the submitted authorization chain is internally supportable; it does not establish real-world identity, intent, guilt, nationality, espionage, concurrent employment or employment suitability.

Minimum evidence

  • identity_subjects: required and organization-defined
  • identity_accounts: required and organization-defined
  • access_grants: at least 0 rows/items
  • identity_evidence: at least 0 rows/items
  • access_events: at least 0 rows/items
  • as_of_ms: 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

  • tenant-scoped point-in-time subject-to-account-to-grant-to-resource chain joined to effective engagement, approval, device and evidence validity intervals, preserving disabled/revoked rows and failed security events
  • authorized subject perimeter, evidence types and independent-source count, validity/freshness, privilege vocabulary, account/resource scope, conflict-disclosure applicability, legal basis, correction rights, due process, as-of cutoff and verified-evidence threshold

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": "audit the pointintime chain from an" }
  → finds "audit_workforce_identity_access_evidence_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_workforce_identity_access_evidence_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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