AIPCH10 — Compliant by Design

“Policy-as-Code and Governance Controls Enforced”

What AIPCH10 is really asserting​

AIPCH10 is not asserting that:

“The AI Product complies with regulations or policies.”

It is asserting that:

All applicable governance requirements (regulatory, ethical, risk, access, and usage constraints) are explicitly defined, bound to the AI Product at creation time, and automatically enforced at runtime through policy-as-code — without relying on manual processes or human interpretation.

Compliance is not a review outcome.
Compliance is a continuously enforced system property.

The Essence (HDIP + AIPS Interpretation)​

An AI Product is compliant by design if and only if:

1. Governance policies are declared alongside the product intent
2. Policies are bound to the product during compilation
3. Enforcement is automatic, consistent, and runtime-driven

If compliance depends on:

• manual approvals
• audits after deployment
• documentation or sign-offs
• human interpretation of rules

then AIPCH10 is not met, even if the product is “compliant”.

The Governance Model (Federated Computational Governance)​

AIPCH10 operates through:

Federated, hierarchical policy enforcement

Policies are defined at multiple levels:

• Global (enterprise-wide rules)
• Domain (e.g., Risk, Compliance, Legal)
• Product (AIPRO-defined constraints)

These are:

• visible to the AIPRO during design
• compiled into the product
• enforced automatically at runtime

Positive Criteria — When AIPCH10 is met​

AIPCH10 is met when all of the following are true:

1. Policies are explicitly declared and bound at design time​

The AI Product includes:

• risk classification (R0–R4)
• data usage constraints (privacy, residency)
• ethical constraints (fairness, bias thresholds)
• access and entitlement policies
• usage boundaries (allowed/prohibited use)

These are:

• part of the declarative definition
• not external or implicit

2. Policy enforcement is automatic at runtime​

The system enforces:

• access controls (RBAC, ABAC, ReBAC, etc.)
• data filtering (row/column-level controls)
• usage restrictions
• risk-based constraints
• compliance checks (e.g., GDPR, fairness thresholds)

No manual intervention is required.

3. Governance is enforced consistently across all consumers​

The AI Product:

• behaves consistently regardless of who consumes it
• does not rely on consumer-specific controls
• enforces policies at the product level

This ensures:

governance travels with the product

4. Policies are machine-interpretable and executable​

Policies are:

• defined as structured artifacts (policy-as-code)
• interpretable by the platform
• enforceable without human translation

This enables:

• automated compliance
• agent-driven governance

5. Policy violations are detectable and observable​

The system:

• detects violations (e.g., misuse, threshold breaches)
• logs and exposes them as signals (linked to AIPCH07)
• triggers enforcement or escalation actions

Negative Criteria — When AIPCH10 is not met​

AIPCH10 is not met if any of the following are true:

❌ Compliance relies on manual processes​

Examples:

• approval workflows before deployment
• compliance reviews after release
• human sign-offs for policy enforcement

This is governance by process, not by design.

❌ Policies are documented but not enforced​

Examples:

• “Do not use for X” in documentation
• compliance guidelines without system enforcement
• ethical policies without runtime checks

This creates policy illusion.

❌ Enforcement depends on consuming systems​

Examples:

• access control implemented in client applications
• policy checks delegated to downstream systems
• inconsistent enforcement across consumers

This breaks product-level governance.

❌ Policies are not visible to the AIPRO​

Examples:

• hidden governance logic
• unclear constraints during product definition
• policies applied after deployment

This prevents intentional design.

Edge Cases (Important Guidance for Agents)​

Case 1: “Manual compliance approval required before deployment”​

❌ Not met

Rationale:

• compliance is procedural
• not embedded in system behavior

Case 2: “Policy enforcement exists but incomplete coverage”​

⚠️ Partial

Rationale:

• some controls enforced
• gaps remain (e.g., fairness, misuse detection)

Case 3: “Policy-as-code with automatic runtime enforcement”​

✅ Met

Rationale:

• policies declared, bound, and enforced
• no manual dependency

Case 4: “High-risk AI (R3/R4) with human oversight layer”​

✅ Met, if:

• oversight is part of policy enforcement
• not a replacement for system-level controls

Evidence Signals an Agent Should Look For​

Authoritative evidence:​

• policy artifacts linked to AIPROD/AIPDS
• runtime enforcement logs
• entitlement and access control configuration
• risk tier classification

Supporting evidence:​

• violation detection signals (linked to AIPCH07)
• audit trails of enforcement actions
• policy evaluation results

Red flags:​

• reliance on documentation or guidelines
• manual approval workflows
• inconsistent enforcement across consumers
• lack of runtime policy signals

How an AI Agent Should Decide​

Decision rule (simplified):

If governance and compliance depend on manual processes, documentation, or human interpretation rather than automated, runtime policy enforcement, AIPCH10 is not met.

Why AIPCH10 Is Non-Negotiable​

Without AIPCH10:

• governance becomes inconsistent
• risk cannot be controlled at scale
• compliance becomes reactive
• trust (AIPCH07) becomes unreliable

AIPCH10 enables:

• continuous, enforceable governance
• scalable compliance without committees
• safe reuse and composition
• alignment with regulatory expectations

Canonical Statement (for AIPS)​

AIPCH10 is satisfied only when all applicable governance and compliance requirements are explicitly declared, bound to the AI Product during creation, and automatically enforced at runtime through machine-interpretable policy-as-code, without reliance on manual processes or human interpretation.