Unauthorized AI Data Processing

Unauthorized AI Data Processing occurs when sensitive, regulated, confidential, proprietary, or otherwise restricted information is submitted to or processed by an AI system that is not authorized to handle that specific data classification, regulatory requirement, contractual restriction, or business use.

The AI application itself may be legitimate or enterprise-approved, but the particular data, processing purpose, configuration, or AI environment falls outside the organization's permitted security and governance boundary.

1. Authorized Data Access

An employee, researcher, clinician, developer, contractor, or other user legitimately accesses organizational information.

2. AI System Selection

The user accesses an AI system that may itself be approved or legitimately available for organizational use.

3. Legitimate Business Objective

The user attempts to perform a valid business task such as summarization, analysis, research, coding, drafting, classification, document review, transcription, or automation.

4. Restricted Data Introduction

Information subject to security, privacy, contractual, regulatory, or organizational restrictions is provided to the AI system.

5. Authorization Mismatch

The AI environment is not approved or appropriately configured to process that particular data classification, even though the user, project, or AI application may otherwise be authorized.

6. AI Processing

The AI provider or system receives, analyzes, transforms, stores, logs, transmits, embeds, summarizes, or otherwise processes the restricted information.

7. Governance Boundary Violation

The organization’s required protections for retention, access, geographic processing, contractual handling, deletion, monitoring, training use, privacy, or regulatory compliance may no longer be assured.

8. Discovery

The organization identifies the unauthorized processing through monitoring, investigation, audit, employee reporting, privacy review, regulatory review, or another security event.

9. Business Impact

The organization may face privacy notification, regulatory reporting, contractual obligations, remediation costs, legal exposure, compliance consequences, or loss of control over sensitive information.

Attack Path:

Authorized Data Access → Approved or Available AI System → Restricted Data Introduction → Data-to-System Authorization Mismatch → AI Processing → Governance Boundary Violation → Sensitive Data Exposure → Regulatory, Legal, or Business Impact

• Enterprise generative AI platforms
• External AI services
• AI research platforms
• AI document-analysis systems
• AI coding assistants
• AI transcription platforms
• AI meeting assistants
• AI analytics tools
• LLM APIs
• AI agents
• RAG systems
• Model inference environments
• Enterprise copilots
• AI SaaS applications
• Healthcare AI systems
• Financial-services AI systems
• Research environments
• Legal AI platforms
• Customer-support AI systems
• Internal AI applications
• Cloud AI services
• Data repositories integrated with AI applications

Define the organization’s AI data-processing matrix before testing.

For each AI application or environment, identify which data classifications are explicitly permitted and prohibited.

Test whether controls enforce those restrictions.

Use controlled synthetic information representing:

• PII
• PHI
• Financial records
• Payment information
• Customer information
• Confidential documents
• Source code
• API credentials
• Legal information
• Research information
• Intellectual property

Attempt to provide restricted test data through:

• Prompts
• File uploads
• Document attachments
• APIs
• Browser extensions
• Agent tools
• RAG ingestion
• Clipboard operations
• Coding assistants
• Meeting assistants
• Transcription services
• Automated integrations

Verify whether the AI application recognizes and blocks prohibited data classifications.

Test whether data loss prevention controls distinguish between approved and prohibited AI processing environments.

Determine whether users can switch from approved enterprise accounts to consumer or personal instances of the same AI provider.

Review whether different model deployments or workspaces have different data-processing permissions.

Verify contractual requirements associated with regulated data.

For healthcare environments, determine whether systems permitted to process PHI have the necessary organizational approval and contractual protections.

Test whether de-identification requirements are technically or procedurally enforced before AI processing.

Review retention, deletion, training, logging, geographic processing, and administrative-access settings.

Verify that AI integrations inherit appropriate data classification and authorization policies.

Test whether users can circumvent restrictions through APIs, mobile applications, alternate models, browser extensions, personal accounts, or file-sharing mechanisms.

The objective is to determine whether authorization follows the data rather than merely the application.

Monitor for:

• Restricted data entering AI systems not approved for that classification
• PHI transmitted to non-approved AI environments
• PII uploaded to AI applications outside approved processing controls
• Financial records transmitted to unauthorized AI workspaces
• Source code submitted to AI systems prohibited from processing proprietary code
• Credentials or secrets appearing in AI prompts
• Regulated information uploaded through AI file interfaces
• Sensitive documents processed through consumer AI accounts
• Users switching between enterprise and personal AI accounts
• Restricted data sent to external model APIs
• AI applications processing information outside approved geographic regions
• AI environments with incompatible retention policies
• AI integrations accessing higher-sensitivity data than their approved classification
• Research datasets containing identifiers submitted to external AI systems
• Data-classification labels being removed before AI submission
• Sensitive-data access immediately followed by AI service interaction
• High-volume document transfers to AI platforms
• Newly introduced AI applications processing regulated information
• AI processing inconsistent with contractual or privacy requirements

Detection should correlate three elements:

1. Data classification
2. User authorization
3. AI processing-environment authorization

The presence of an approved user and approved AI application should not automatically make the transaction permissible.

Organizations should alert when the classification of the information exceeds the approved processing level of the destination AI environment.

T1567 — Exfiltration Over Web Service Contextual mapping. MITRE ATT&CK T1567 describes information being transferred through legitimate external web services. Unauthorized AI Data Processing may create a similar technical data path when restricted organizational information is transmitted to an external AI service. However, RB-060 does not require malicious intent or adversarial exfiltration. The user may be authorized to access the information and may be performing legitimate work. The security failure is the mismatch between the data classification and the AI environment authorized to process it. Relevant Mitigation: M1057 — Data Loss Prevention Data loss prevention controls can identify or prevent restricted information from being transmitted to AI environments that are not authorized for that data. Mapping Note: MITRE ATT&CK does not precisely model accidental or policy-violating AI processing by legitimate users. T1567 should therefore be treated as a contextual data-flow relationship rather than an exact behavioral equivalence.

PRIMARY LLM02:2025 — Sensitive Information Disclosure Unauthorized AI Data Processing aligns strongly with OWASP's Sensitive Information Disclosure risk because restricted information may include personally identifiable information, financial information, health records, confidential business data, credentials, legal documents, and other sensitive information. OWASP recommends data sanitization and controls designed to prevent sensitive information from entering LLM systems when that information should not be processed. SECONDARY LLM07:2025 — System Prompt Leakage Contextual only where sensitive organizational information, secrets, credentials, or protected configuration becomes incorporated into AI context or system-level instructions. Mapping Note: RB-060 specifically focuses on authorization of the AI processing event. The security question is not merely whether information is sensitive. The question is whether this particular AI environment is authorized to receive and process that information under the organization's security, privacy, regulatory, contractual, and business requirements.