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§ I · Domain INP · Control 4 of 10
Control
INP-04
Edition
v.1.3
Status
Public review

INP · Input & Prompt Security

INP-04Sensitive content handling

PII and other sensitive content in inputs is detected and handled per policy (redact, block, or log).

Personally identifiable information and other sensitive content in inputs is detected at the input layer and handled per the organization's data classification policy: redacted, blocked, or logged depending on the context and the agent's authorization level. This control works in concert with DAT-06 and DAT-07 to ensure sensitive data does not propagate where it should not.

Table INP-04.1 · Maturity criteria · v.1.3

L1 to L5 · the lowest level in scope caps the agent
L1Ad hoc

Controls exist informally; documentation is incomplete; no consistent review.

L2Documented

Controls are written down; ownership is assigned; review cadence is defined.

L3Operated

Controls are followed in practice; evidence is collected; deviations are tracked.

Detection is applied to all input paths; handling rules are documented per data class; detection accuracy is measured; false negatives are tracked and the detection pipeline is tuned.

L4Measured

Control effectiveness is measured against documented thresholds; trends are reported.

L5Continuously Improved

Controls evolve based on incident learning; maturity gains are evidenced over time.

The v1.3 edition publishes a control-specific criterion at L3 (Operated). The other levels apply the general ladder to this control; see the maturity ladder.

Evidence an assessor asks for

  1. E.1

    Detection is applied to all input paths.

  2. E.2

    Handling rules are documented per data class.

  3. E.3

    Detection accuracy is measured.

  4. E.4

    False negatives are tracked and the detection pipeline is tuned.

Table INP-04.2 · Framework mappings

6 of 7 frameworks carry a control-level reference
NIST AI RMFMEASURE 2.8NIST AI Risk Management Framework 1.0
ISO/IEC 42001A.7.2ISO/IEC 42001:2023 · Artificial intelligence management system
OWASP LLM Top 10LLM03OWASP Top 10 for Large Language Model Applications
SOC 2 (TSC)CC7.1AICPA Trust Services Criteria for SOC 2 Type II
EU AI ActArt. 15(4)EU AI Act · Regulation (EU) 2024/1689
MITRE ATLAS·MITRE ATLAS · Adversarial Threat Landscape for AI Systems
SingaporeMAIGF Security, AI Verify SecuritySingapore Model AI Governance Framework / AI Verify / PDPA

Mappings are directional: meeting this control evidences but does not by itself satisfy the external requirement. A middle dot means no control-level reference is published at this edition; see the crosswalk.

Related controls and terms

Cite this control

Human Intelligence. HI-AAF v1.3, INP-04 Sensitive content handling. Public review edition, July 2026. https://humanintel.net/standard/inp/inp-04

Edition v.1.3 · July 2026 · Public review · Markdown · PDF

https://humanintel.net/standard/inp/inp-04

Notice

HI-AAF v1.3 is an independent framework published by Human Intelligence. It is not affiliated with NIST, ISO, AICPA, or any government or accreditation body. The framework is offered openly for review during the current draft cycle.