AI Acceptable Use Template: Data Handling

Large Language Model (LLM) Data Handling Policy

Policy Owner: [Role/Department]
Last Updated: [Date]
Version: [X.X]

1. Purpose and Scope

1.1 Purpose
This policy establishes mandatory requirements for handling, processing, and protecting data in connection with Large Language Model (LLM) usage at [Organization Name]. It defines specific controls and procedures for data protection throughout the LLM interaction lifecycle.

1.2 Scope
This policy applies to:
a) All data processed through or submitted to LLM services
b) All employees, contractors, and third parties with LLM access
c) All LLM platforms and services, whether enterprise or public
d) All associated data storage and transmission systems

2. Data Classification Framework

2.1 Prohibited Data Categories
The following data categories must never be submitted to LLMs:

2.1.1 Personal Information
a) Social Security numbers or national identifiers
b) Driver’s license numbers
c) Passport numbers
d) Biometric data
e) Personal financial account information

2.1.2 Healthcare Information
a) Patient records
b) Medical histories
c) Treatment information
d) Insurance information
e) Provider identification numbers

2.1.3 Financial Data
a) Credit card numbers
b) Bank account details
c) Wire transfer information
d) Investment account numbers
e) Tax identification numbers

2.1.4 Access Credentials
a) Passwords
b) Security tokens
c) Encryption keys
d) Authentication credentials
e) Access codes

2.2 Restricted Data Categories
The following data requires explicit approval and masking:

2.2.1 Business Information
a) Internal performance metrics
b) Project specifications
c) Product roadmaps
d) Strategic planning documents
e) Pricing information

2.2.2 Customer Information
a) Contract details
b) Service level agreements
c) Account information
d) Usage patterns
e) Support history

3. Data Handling Procedures

3.1 Pre-Processing Requirements
Before submitting data to LLMs:
a) Scan all content through Repacket’s monitoring proxy
b) Apply required data masking patterns
c) Validate compliance with classification rules
d) Document any approved exceptions
e) Log preprocessing activities

3.2 Data Masking Standards
When masking is required:
a) Replace identifiers with standardized tokens
b) Maintain consistent masking patterns
c) Document masking rules applied
d) Preserve data utility while removing sensitivity
e) Validate masking effectiveness

3.3 Data Validation Procedures
All data must undergo:
a) Automated pattern matching via Repacket
b) Classification verification
c) Sensitivity level assessment
d) Compliance validation
e) Authorization checking

4. Data Transmission Controls

4.1 Secure Transport Requirements
All LLM data transmission must:
a) Use encrypted connections (TLS 1.2 minimum)
b) Route through Repacket’s proxy
c) Pass through content filters
d) Generate audit logs
e) Maintain chain of custody

4.2 Access Control Requirements
Data access must be:
a) Role-based and documented
b) Limited to authorized personnel
c) Regularly reviewed and validated
d) Monitored and logged
e) Immediately revocable

5. Data Storage and Retention

5.1 Storage Requirements
LLM interaction logs must:
a) Reside in approved storage systems
b) Maintain encryption at rest
c) Follow retention schedules
d) Support audit requirements
e) Enable rapid access revocation

5.2 Retention Schedules
Data shall be retained as follows:
a) Transaction logs: [timeframe]
b) Access records: [timeframe]
c) Security incidents: [timeframe]
d) Audit trails: [timeframe]
e) Exception records: [timeframe]

6. Monitoring and Auditing

6.1 Real-Time Monitoring
Repacket’s system shall be used to:
a) Monitor all LLM data transmissions
b) Flag potential policy violations
c) Block prohibited data
d) Alert security personnel
e) Log all monitoring activities

6.2 Audit Requirements
Regular audits must:
a) Review all data handling procedures
b) Validate classification compliance
c) Assess control effectiveness
d) Document findings
e) Track remediation

7. Incident Response

7.1 Data Incident Classification
Incidents shall be classified as:
a) Level 1: Confirmed data exposure
b) Level 2: Attempted policy violation
c) Level 3: Control failure
d) Level 4: Process deviation

7.2 Response Procedures
For all data incidents:
a) Immediate containment actions
b) Incident documentation
c) Impact assessment
d) Corrective measures
e) Post-incident review

8. Training Requirements

8.1 Data Handling Training
All users must complete:
a) Initial data classification training
b) Tool-specific training
c) Annual refresher courses
d) Incident response training
e) Policy update training

8.2 Competency Validation
Training program must include:
a) Skills assessment
b) Practical exercises
c) Policy comprehension testing
d) Documentation review
e) Performance evaluation

9. Compliance Verification

9.1 Verification Methods
Compliance shall be verified through:
a) Automated monitoring
b) Manual audits
c) Regular testing
d) User assessments
e) Process reviews

9.2 Documentation Requirements
Maintain records of:
a) Classification decisions
b) Processing activities
c) Security incidents
d) Audit results
e) Corrective actions

10. Policy Administration

10.1 Review Schedule
This policy shall be:
a) Reviewed quarterly
b) Updated as needed
c) Distributed to stakeholders
d) Validated for effectiveness
e) Approved by [authority]

10.2 Exception Management
Exceptions require:
a) Written business justification
b) Risk assessment
c) Approval documentation
d) Regular review
e) Expiration date

[Organization Name] reserves the right to modify this policy at any time. Questions about this policy should be directed to [contact information].

Last reviewed: [Date]
Next review due: [Date]

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