What Are Policy Artifacts?

Policy Artifacts are the foundational building blocks of SOVR's Responsibility Layer. They're declarative documents that specify:

What actions AI can perform

Under what conditions

With what constraints

Requiring what approvals

Design Principles

1. Immutability

Once a policy version is deployed, it cannot be modified. Changes require creating a new version, ensuring complete audit trails.

2. Composability

Policies can inherit from and extend other policies, enabling organizational hierarchies and role-based customization.

3. Verifiability

Every policy is cryptographically signed, allowing any party to verify its authenticity and integrity.

Anatomy of a Policy Artifact

# policy_artifact_v2.yaml

metadata:

name: "finance-ai-policy"

version: "2.1.0"

author: "security-team"

signature: "0x7f8a...9b2c"

expires: "2026-12-31T23:59:59Z"

inherits:

- "[email protected]"

rules:

- name: "payment-approval"

action: "execute_payment"

effect: REQUIRE_APPROVAL

conditions:

amount_gt: 1000

- name: "data-export-block"

action: "export_data"

resource: "pii_tables"

effect: DENY

unless: "compliance_approved"

Versioning Strategy

We use semantic versioning with additional metadata:

**Major**: Breaking changes to rule structure

**Minor**: New rules or conditions added

**Patch**: Documentation or metadata updates

Version Lifecycle

1. **Draft**: Policy under development, not enforced

2. **Staged**: Ready for deployment, pending approval

3. **Active**: Currently enforced

4. **Deprecated**: Scheduled for retirement

5. **Archived**: Historical reference only

Cryptographic Signing

Every policy artifact includes:

SHA-256 hash of content

Ed25519 signature from authorized key

Timestamp from trusted time source

Chain of custody metadata

This enables:

Tamper detection

Non-repudiation

Compliance proof

Deployment Workflow

1. Author creates policy in YAML/JSON

2. Policy passes schema validation

3. Eval Gate runs simulation tests

4. Authorized signer approves and signs

5. Policy deployed to enforcement layer

6. All agents receive updated rules

Best Practices

Start with restrictive policies, loosen as needed

Use inheritance for organizational consistency

Set expiration dates to force regular review

Test policies in sandbox before production

Try the Policy Editor →

Policy Artifacts: Building Immutable Contracts for AI Behavior