GOVERN-1.3 / MAP-5.1 / MEASURE-2.13 NIST AI Risk Management Framework

Output filters for memorised / sensitive content

Demonstrate that output filtering mechanisms effectively prevent the disclosure of memorized sensitive content, credentials, PII, and confidential training data through automated system responses or AI-generated outputs.

Description

What this control does

Output filters for memorized or sensitive content prevent systems—particularly AI models, chatbots, and automated response platforms—from inadvertently disclosing confidential data, training material, credentials, or personally identifiable information through generated responses. These filters employ pattern matching, redaction logic, keyword blocking, and contextual analysis to intercept and sanitize outputs before delivery to end users or external systems. The control is critical for organizations deploying large language models, customer service automation, or any system that generates dynamic content from internal knowledge bases where training data or system prompts may contain sensitive information.

Control objective

What auditing this proves

Associated risks

Risks this control addresses

Unauthorized disclosure of confidential training data or proprietary knowledge embedded in AI model weights through prompt injection or adversarial queries
Leakage of credentials, API keys, or authentication tokens memorized during model training or ingested from system prompts
Exposure of personally identifiable information (PII) from customer records, employee data, or other sensitive datasets used in training or fine-tuning
Inadvertent reproduction of copyrighted material, proprietary source code, or licensed content verbatim in generated responses
Disclosure of internal system architecture, security configurations, or operational details through verbose error messages or diagnostic outputs
Social engineering exploitation where attackers craft prompts to elicit protected information by bypassing insufficient output controls
Regulatory non-compliance (GDPR, CCPA, HIPAA) due to unfiltered generation of regulated data categories in user-facing outputs

Testing procedure

How an auditor verifies this control

Inventory all systems that generate dynamic or automated outputs, including AI models, chatbots, auto-response platforms, and customer service tools, documenting model versions and data sources.
Review output filtering configurations, including redaction rules, deny-lists, pattern matching specifications, and contextual analysis logic applied to generated content before user delivery.
Obtain documentation of sensitive data categories defined for filtering, including PII schemas, credential formats, confidential data classifications, and proprietary content indicators.
Execute a sample of adversarial test prompts designed to elicit memorized training data, PII, credentials, or system details, recording raw and filtered outputs for comparison.
Analyze logs of filtering events over a representative period, identifying trigger patterns, false positive rates, and instances where sensitive content was detected and redacted.
Interview personnel responsible for filter maintenance to confirm update procedures, threshold tuning, and integration with data classification policies.
Test edge cases including multi-turn conversations, indirect queries, encoding variations (Base64, hex), and prompt injection techniques to validate filter robustness.
Verify integration between output filters and incident response workflows, confirming that filter breaches or bypass attempts trigger security alerts and investigation procedures.

Evidence required Configuration exports of output filtering rules, redaction logic, and deny-list databases from AI platforms or response automation systems; logs showing filter activation events, blocked outputs, and redaction actions over a 30-90 day period; test results from adversarial prompt campaigns with side-by-side raw and sanitized outputs; incident response records documenting filter bypass investigations; policy documentation defining sensitive content categories and filtering thresholds; change management records for filter rule updates correlated with data classification changes.

Pass criteria All tested adversarial prompts and edge cases produce outputs where sensitive content categories defined in organizational policy are consistently redacted or blocked, filtering logs demonstrate active enforcement with documented review of exceptions, and filter configurations align with current data classification standards.

Where this control is tested

Audit programs including this control

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