Governance

Model-risk regimes change their names and their numbers. What they ask for barely changes. Strip away the acronyms and every regime — US banking, EU, insurance — wants the same six things from your model inventory. model-ledger is built to produce them as a byproduct of normal use, not as a separate compliance chore.

What every regime actually asks for

The durable need	What an examiner says	The model-ledger primitive
Complete inventory	"Show me every model — including the shadow ones."	Cross-platform discovery & connectors — ML models, rules, and ETL as one graph
Risk tiering	"Which are high-materiality?"	tier on every ModelRef; business systems roll up as composites
Change control + audit trail	"What changed, when, and who did it?"	Immutable, content-addressed Snapshots — append-only, tamper-evident
Dependency & lineage	"How do these components feed each other?"	The dependency graph, built from port matching
Validation records	"Prove this was validated, and find what wasn't."	record_validation() events live in the same immutable log
Point-in-time reconstruction	"Show me the inventory as it stood on December 31."	inventory_at(date) replays the log

That's the whole compliance story: nothing is overwritten, so the answer to "what was true then?" is always reconstructable.

It falls out of normal use

from model_ledger import Ledger

ledger = Ledger.from_sqlite("./inventory.db")

# Identity + risk tier — the minimum a regulator needs
ledger.register(
    name="credit_scorecard", owner="risk-team",
    model_type="ml_model", tier="high",
    purpose="Consumer credit decisioning",
)

# Validation outcomes are just events in the same immutable log
ledger.record("credit_scorecard", event="validated", actor="mrm-team",
              payload={"result": "pass", "validator": "second-line"})

# The full, ordered, tamper-evident history an examiner can replay
for snap in ledger.history("credit_scorecard"):
    print(snap.timestamp, snap.event_type, snap.actor)

Frameworks it maps to

The primitives above satisfy the documentation and inventory expectations of the major model-risk and AI-governance regimes:

• US banking — SR 26‑2 / OCC Bulletin 2026‑13 (the 2026 revision that superseded SR 11‑7): tiered model inventory, materiality classification, lifecycle documentation, and validation status.
• EU AI Act — Annex IV: version-tracked technical documentation, component dependencies, and change history for high-risk systems.
• NIST AI RMF and ISO/IEC 42001: inventory, risk management, and lifecycle governance practices.

model-ledger ships pluggable validation profiles (sr_11_7, eu_ai_act, nist_ai_rmf) that check a model's completeness against a framework, and you can add your own — profiles are a plugin layer, not the core. Run them with model-ledger validate --profile <name> (see the CLI guide).

Framework-agnostic on purpose

model-ledger is a model inventory for any organization with deployed models — not a single-regulation tool. The frameworks above are examples of what the underlying capability is good for; they are a thin, swappable layer over a durable foundation. When a regulator renumbers a rule, you update a profile — not your inventory.