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AIPCH19 — Safe & Policy-Bound Usage

“Prohibited Use Policies and Safety Controls Enforced”

What AIPCH19 is really asserting​

AIPCH19 is not asserting that:

“The AI Product has safety guidelines or acceptable use policies.”

It is asserting that:

The AI Product explicitly defines prohibited and unsafe usage boundaries and enforces them at runtime through automated detection, prevention, and control mechanisms — ensuring that misuse, harmful behavior, or out-of-scope usage is actively prevented, not just discouraged.

Safety is not guidance.
Safety is enforced behavior.

The Essence (HDIP + AIPS Interpretation)​

An AI Product is safe and policy-bound in usage if and only if:

  1. Unsafe and prohibited usage is explicitly defined
  2. The system can detect misuse or boundary violations
  3. Enforcement mechanisms actively prevent or control such usage

If safety depends on:

  • user compliance
  • documentation
  • training or awareness

then AIPCH19 is not met, even if policies exist.

What Safe Usage Covers​

1. Prohibited Use Cases​

  • clearly defined disallowed scenarios
  • misuse conditions (intentional or accidental)

2. Contextual Boundaries​

  • valid usage contexts
  • conditions under which outputs are safe or unsafe

3. Harm Prevention​

  • prevention of:
    • unsafe decisions
    • harmful recommendations
    • misuse of outputs

4. Abuse and Misuse Detection​

  • adversarial inputs
  • prompt injection (for LLM-based systems)
  • unexpected usage patterns

👉 This ensures:

the AI Product operates within safe and intended boundaries

Positive Criteria — When AIPCH19 is met​

AIPCH19 is met when all of the following are true:

1. Prohibited usage is explicitly defined​

The AI Product defines:

  • disallowed use cases
  • unsafe scenarios
  • misuse conditions

These are:

  • structured
  • part of product definition (AIPROD / policy layer)

2. Safety controls are enforced at runtime​

The system:

  • detects unsafe inputs or usage
  • blocks, modifies, or escalates responses
  • prevents execution of unsafe actions

This is:

  • automatic
  • consistent
  • not dependent on human intervention

3. Misuse detection mechanisms exist​

The AI Product can detect:

  • abnormal usage patterns
  • adversarial behavior
  • out-of-scope inputs

Detection is:

  • real-time or near real-time
  • integrated with observability (AIPCH09)

4. Safety enforcement is integrated with governance​

Safety controls:

  • are part of policy-as-code (AIPCH10)
  • align with:
    • risk tier (R0–R4)
    • regulatory constraints
    • domain-specific policies

5. Safety events are observable and auditable​

The system:

  • logs safety violations
  • exposes signals (AIPCH07)
  • supports audit and review

Negative Criteria — When AIPCH19 is not met​

AIPCH19 is not met if any of the following are true:

❌ Safety is defined only in documentation​

Examples:

  • acceptable use policies without enforcement
  • guidelines for users without system controls

This is passive safety.

❌ System does not detect misuse​

Examples:

  • no monitoring of input patterns
  • no detection of adversarial behavior
  • no validation of context

This creates exposure.

❌ Unsafe outputs are not controlled​

Examples:

  • harmful recommendations allowed
  • no filtering or correction mechanisms
  • no escalation for risky decisions

This is unsafe operation.

❌ Safety depends on user behavior​

Examples:

  • users expected to follow rules
  • no enforcement at system level

This is unreliable.

❌ Safety is inconsistent across contexts​

Examples:

  • controls vary per consumer
  • enforcement depends on integration layer

This breaks product-level safety.

Edge Cases (Important Guidance for Agents)​

Case 1: “Safety guidelines exist but no enforcement”​

❌ Not met

Rationale:

  • policy without control
  • unsafe in practice

Case 2: “Basic filtering but no comprehensive safety model”​

⚠️ Partial

Rationale:

  • some protection exists
  • not complete or robust

Case 3: “Full runtime safety detection + enforcement”​

✅ Met

Rationale:

  • misuse detected and prevented
  • aligned with governance

Case 4: “High-risk AI (R3/R4) with strict safety controls”​

✅ Required

Rationale:

  • stricter enforcement needed
  • safety must be comprehensive

Evidence Signals an Agent Should Look For​

Authoritative evidence:​

  • defined prohibited-use policies
  • runtime safety enforcement mechanisms
  • logs of blocked or modified actions

Supporting evidence:​

  • misuse detection metrics
  • anomaly detection signals
  • audit trails of safety events

Red flags:​

  • reliance on user compliance
  • lack of enforcement mechanisms
  • absence of misuse detection
  • unsafe outputs allowed

How an Agent Should Decide​

Decision rule (simplified):

If the AI Product cannot detect and actively prevent unsafe, prohibited, or out-of-scope usage through automated controls, AIPCH19 is not met.

Why AIPCH19 Is Non-Negotiable​

Without AIPCH19:

  • AI Products can be misused
  • harmful outcomes may occur
  • governance (AIPCH10) is incomplete
  • trust (AIPCH07) is undermined

AIPCH19 enables:

  • safe operation in real-world environments
  • protection against misuse and harm
  • alignment with ethical and regulatory expectations
  • confidence in AI Product deployment

Canonical Statement (for AIPS)​

AIPCH19 is satisfied only when an AI Product explicitly defines prohibited and unsafe usage boundaries and enforces them at runtime through automated detection, prevention, and control mechanisms, ensuring that misuse and harmful behavior are actively prevented rather than merely discouraged.