Below is a practical, enterprise-wide policy template you can adapt for your organization.

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Enterprise Policy for Evaluation and Deployment of AI Tools

Policy Owner: [Chief Information Officer / Chief Digital Officer / AI Governance Committee]
Effective Date: [Insert Date]
Version: 1.0
Applies To: All departments, business units, subsidiaries, contractors, and third parties acting on behalf of [Organization Name]

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1. Purpose

This policy establishes a standardized framework for evaluating, approving, deploying, monitoring, and retiring artificial intelligence (AI) tools across the enterprise. Its purpose is to ensure that AI tools:

• Align with business strategy and operational needs
• Protect company, customer, employee, and partner data
• Meet legal, regulatory, and contractual obligations
• Integrate effectively with existing workflows and systems
• Deliver measurable business value and acceptable return on investment (ROI)
• Are governed in a consistent, transparent, and accountable manner

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2. Scope

This policy applies to:

• Generative AI tools
• Machine learning platforms
• Predictive analytics tools
• Natural language processing tools
• AI-enabled SaaS applications
• Embedded AI features in enterprise software
• Internally developed AI systems
• Third-party AI services accessed via API, browser, plug-in, or standalone application

This policy covers all stages of the AI lifecycle:

• Business case development
• Vendor selection
• Security and compliance review
• Pilot testing
• Deployment approval
• Ongoing monitoring
• Renewal, retraining, or decommissioning

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3. Guiding Principles

All enterprise AI deployments must adhere to the following principles:

1. Business Value First
   AI must address a defined business problem or opportunity with measurable outcomes.

2. Security by Design
   Data security, privacy, identity management, and access controls must be evaluated before use.

3. Compliance by Default
   AI tools must comply with all applicable laws, regulations, industry standards, and internal policies.

4. Human Accountability
   Humans remain accountable for decisions informed by AI, especially in high-impact use cases.

5. Transparency and Traceability
   AI use cases, data sources, outputs, decisions, and approvals must be documented.

6. Risk-Based Governance
   Higher-risk AI use cases require deeper scrutiny, stronger controls, and more frequent review.

7. Operational Fit
   AI tools must integrate with existing workflows, systems, and governance structures.

8. Measured Performance
   AI tools must be monitored for effectiveness, adoption, reliability, and financial return.

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4. Governance Structure

4.1 AI Governance Committee

The organization shall maintain an AI Governance Committee responsible for oversight of AI tool evaluation and deployment. Membership should include representatives from:

• IT
• Information Security
• Legal
• Compliance/Privacy
• Procurement
• Risk Management
• HR
• Finance
• Internal Audit
• Business Unit leadership
• Data Governance
• Enterprise Architecture

4.2 Roles and Responsibilities

Business Sponsor

• Defines use case, goals, users, and expected benefits
• Owns budget and business outcomes
• Ensures departmental adoption and change management

IT / Enterprise Architecture

• Assesses technical fit, interoperability, scalability, and supportability
• Reviews integration requirements and system dependencies

Information Security

• Conducts security review
• Defines required controls, access restrictions, and monitoring standards

Legal / Privacy / Compliance

• Reviews contractual, regulatory, privacy, intellectual property, and records obligations

Procurement / Vendor Management

• Conducts vendor due diligence
• Negotiates contract terms, SLAs, data protection terms, and exit provisions

Finance

• Reviews cost assumptions, total cost of ownership, and ROI methodology

Risk Management / Internal Audit

• Evaluates operational, reputational, and control risks
• Verifies compliance with policy and review procedures

Tool Owner / Product Owner

• Manages implementation, user access, training, monitoring, and ongoing performance

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5. AI Tool Classification

All AI tools and use cases must be classified before approval.

5.1 Risk Tiers

Tier 1 – Low Risk

• Administrative productivity tools
• Internal drafting or summarization tools
• Limited or no sensitive data exposure
• Human review required before external use

Tier 2 – Moderate Risk

• Departmental workflow automation
• Customer support assistance
• Internal decision support
• Integration with enterprise data sources
• Use of confidential business data

Tier 3 – High Risk

• Tools affecting customer, employee, financial, legal, healthcare, or safety-related decisions
• Processing regulated or highly sensitive data
• Automated recommendations with material business impact
• Public-facing AI agents or externally visible outputs
• Use cases with elevated legal, ethical, or reputational risk

5.2 Approval Thresholds

• Tier 1: Department approval + IT/security review
• Tier 2: AI Governance Committee approval required
• Tier 3: Executive approval plus AI Governance Committee, Legal, Security, and Risk sign-off required

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6. Evaluation and Approval Process

No AI tool may be purchased, connected to enterprise systems, or used with company data until the following process is completed.

6.1 Stage 1: Business Justification

Requesting department must submit:

• Business problem statement
• Proposed use case
• User population and stakeholders
• Expected benefits
• Alternatives considered
• Data required
• Estimated costs
• ROI hypothesis
• Risk tier recommendation

6.2 Stage 2: Preliminary Risk Screening

The AI Governance Committee or designated review team will assess:

• Data sensitivity
• Intended users
• Customer or employee impact
• Degree of automation
• External exposure
• Regulatory implications
• Reputational risks

6.3 Stage 3: Vendor and Technical Assessment

All third-party AI vendors must undergo due diligence, including:

• Security review
• Privacy review
• Compliance review
• Architecture review
• Procurement review
• Financial and operational stability assessment

6.4 Stage 4: Pilot / Proof of Concept

Approved tools must generally begin with a controlled pilot unless waived by the AI Governance Committee. Pilot requirements include:

• Defined success criteria
• Limited user scope
• Approved data set
• Monitoring plan
• Human review procedures
• Incident response path
• Exit criteria

6.5 Stage 5: Deployment Approval

Production deployment requires documented sign-off that:

• Security controls are implemented
• Contractual terms are finalized
• Required training is completed
• Performance and ROI baselines are established
• Support model is defined
• Monitoring and review cadence is assigned

6.6 Stage 6: Ongoing Review

All deployed AI tools must be reviewed periodically for:

• Security posture
• Vendor compliance status
• Actual business outcomes
• User adoption
• Model performance and reliability
• Emerging legal or regulatory changes
• Continued business need

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7. Data Security Requirements

AI tools must meet the organization’s information security standards before approval.

7.1 Data Classification and Handling

Departments may only use AI tools with data appropriate to the approved risk and data classification level. The following rules apply:

• Public data may be used with approved low-risk tools
• Internal data may only be used with approved enterprise-authorized tools
• Confidential, regulated, customer, financial, employee, legal, or intellectual property data may only be used where explicitly approved and contractually protected
• Highly sensitive or restricted data requires enhanced review and may be prohibited in some external AI systems

7.2 Minimum Security Criteria

All AI tools must be evaluated for:

• Encryption in transit and at rest
• Identity and access management integration
• Single sign-on (SSO) and multi-factor authentication (MFA)
• Role-based access controls
• Audit logging and retention
• Data segregation in multi-tenant environments
• Secure API access
• Vulnerability management
• Incident detection and response capabilities
• Backup, resilience, and disaster recovery controls

7.3 Data Usage Restrictions

Unless explicitly approved in writing:

• Company data must not be used to train third-party foundation models
• Sensitive data must not be pasted into consumer-grade AI tools
• AI outputs containing confidential information must be handled according to data classification policy
• AI tools must not retain enterprise prompts or outputs beyond approved retention periods
• Cross-border data transfer must comply with applicable laws and internal requirements

7.4 Security Review Requirements

The security team shall assess:

• Vendor security certifications and reports
• Penetration testing practices
• Secure development lifecycle maturity
• Data residency options
• Access logging capabilities
• Administrative controls
• Subprocessor relationships
• Incident notification commitments

7.5 Incident Management

Any suspected AI-related security incident must be reported immediately through standard incident response channels. This includes:

• Unauthorized disclosure of prompts, outputs, or training data
• Model manipulation or prompt injection attacks
• Malicious or harmful outputs
• Unauthorized system access or privilege escalation
• Third-party vendor compromise

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8. Vendor Compliance and Third-Party Risk Requirements

All third-party AI vendors must satisfy enterprise third-party risk standards.

8.1 Required Vendor Due Diligence

Vendors must provide, as applicable:

• SOC 2 Type II, ISO 27001, or equivalent certifications
• Privacy and data processing documentation
• Security architecture documentation
• Subprocessor list
• Disaster recovery and business continuity information
• Model governance documentation
• Service level commitments
• Regulatory compliance attestations
• Financial stability information
• References and customer case studies

8.2 Contractual Requirements

Contracts for AI tools must include, where applicable:

• Data ownership and usage terms
• Restrictions on vendor training use of enterprise data
• Confidentiality obligations
• Security requirements and audit rights
• Breach notification timelines
• Data retention and deletion obligations
• Regulatory cooperation commitments
• IP indemnification where appropriate
• Performance and availability SLAs
• Termination assistance and data portability provisions

8.3 Legal and Regulatory Review

Legal and Compliance must assess whether the tool implicates:

• Privacy regulations
• Employment laws
• Industry-specific regulations
• Consumer protection laws
• Accessibility requirements
• Export controls
• Records retention requirements
• AI-specific regulations or disclosure obligations

8.4 Prohibited Vendor Conditions

Vendors shall not be approved if they:

• Refuse reasonable security review
• Claim broad rights to use enterprise data for model training without consent
• Lack adequate security controls
• Cannot meet required contractual obligations
• Do not provide sufficient transparency into hosting, subprocessors, or data handling
• Pose unacceptable financial, reputational, or operational risk

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9. Integration with Existing Workflows and Systems

AI tools must support operational effectiveness rather than create fragmented or uncontrolled processes.

9.1 Integration Criteria

All AI tools must be evaluated for:

• Compatibility with existing enterprise applications
• API availability and maturity
• Identity and access management integration
• Logging and monitoring support
• Workflow orchestration compatibility
• Data source connectivity
• Change management requirements
• Impact on existing controls and approvals
• Scalability across departments
• Support requirements and ownership

9.2 Workflow Design Requirements

Before deployment, departments must document:

• Current-state workflow
• Target-state workflow with AI augmentation
• Human review points
• Exception handling procedures
• Escalation paths
• Required approvals and segregation of duties
• Operational dependencies
• Business continuity fallback process if AI tool is unavailable

9.3 Human-in-the-Loop Controls

For moderate- and high-risk use cases, the organization shall require appropriate human oversight, including:

• Review of AI-generated outputs before external distribution or decision execution
• Approval checkpoints for high-impact recommendations
• Mechanisms for correction, override, and escalation
• Documentation of final human decision-maker

9.4 Change Management and Training

No department may deploy AI tools without a documented plan for:

• User training
• Acceptable use guidance
• Role-based instructions
• Risk awareness
• Communication to impacted teams
• Support procedures
• Success measurement

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10. ROI and Value Measurement

Each AI tool must have a documented value realization plan before approval and measurable outcomes after deployment.

10.1 Required ROI Business Case

The sponsoring department must define:

• Baseline process metrics
• Expected efficiency gains
• Quality improvements
• Revenue enhancement opportunities
• Risk reduction benefits
• Cost avoidance or cost savings
• Implementation and operating costs
• Time to value
• Assumptions and dependencies

10.2 Standard ROI Metrics

As applicable, departments should measure:

Financial Metrics

• Total cost of ownership
• Net savings
• Revenue uplift
• Margin improvement
• Payback period
• ROI percentage

Operational Metrics

• Time saved per task
• Cycle time reduction
• Throughput increase
• Error rate reduction
• SLA improvement
• Productivity per employee/team

Adoption Metrics

• Active users
• Frequency of use
• Completion rates
• User satisfaction
• Training completion

Risk and Quality Metrics

• Output accuracy
• Hallucination or error rate
• Compliance exceptions
• Security incidents
• Escalation rates
• Rework rates

10.3 Measurement Cadence

ROI must be reviewed at minimum:

• At pilot completion
• 90 days after production launch
• At 6 months
• Annually thereafter, or more frequently for high-risk tools

10.4 Remediation and Exit Triggers

AI tools may be restricted, redesigned, or decommissioned if they:

• Fail to achieve minimum value thresholds
• Create excessive risk or control failures
• Show poor adoption
• Introduce workflow inefficiencies
• Produce unacceptable error rates
• Cause repeated compliance or security incidents
• Become cost-ineffective relative to alternatives

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11. Acceptable Use Requirements

Employees and contractors using approved AI tools must:

• Use only enterprise-approved tools for business purposes
• Follow data classification and handling rules
• Review outputs for accuracy, bias, confidentiality, and appropriateness
• Avoid relying solely on AI outputs for high-impact decisions unless specifically approved
• Disclose AI assistance where required by law, contract, or policy
• Report suspicious outputs, incidents, or performance issues promptly

Employees and contractors must not:

• Input restricted or sensitive data into non-approved AI tools
• Circumvent approval, logging, or access controls
• Use AI tools for unlawful, discriminatory, harassing, or unethical purposes
• Represent AI-generated output as verified fact without review
• Deploy AI-enabled automations directly into production without approval

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12. Documentation and Recordkeeping

The following records must be maintained for each approved AI tool:

• Business case and approval documents
• Risk classification
• Security and compliance assessments
• Vendor due diligence records
• Contractual documents
• Pilot results
• Training completion records
• Deployment approvals
• Monitoring reports
• ROI assessments
• Incident logs
• Renewal or decommissioning decisions

Records shall be retained in accordance with the organization’s records retention schedule.

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13. Monitoring, Audit, and Review

The organization reserves the right to audit any AI deployment for compliance with this policy. Monitoring activities may include:

• Access and usage reviews
• Data handling audits
• Vendor compliance verification
• Output quality assessments
• ROI validation
• User adoption analysis
• Security logging review
• Policy adherence checks

High-risk AI deployments shall be reviewed more frequently and may require formal periodic reapproval.

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14. Exceptions

Exceptions to this policy must be:

• Submitted in writing
• Supported by a business justification
• Reviewed by Security, Legal, Compliance, and the AI Governance Committee
• Approved by the designated executive authority
• Time-bound and documented with compensating controls

No exception may override legal or regulatory obligations.

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15. Enforcement

Violations of this policy may result in:

• Removal of tool access
• Suspension of deployment
• Mandatory retraining
• Disciplinary action up to and including termination
• Contractual remedies for vendors or contractors
• Escalation to Legal, Compliance, HR, or executive leadership as appropriate

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16. Policy Review

This policy shall be reviewed at least annually, or sooner if required by:

• Regulatory changes
• Material security events
• Major technology changes
• Significant vendor incidents
• New enterprise risk findings
• Expansion of AI use cases

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Appendix A: AI Tool Evaluation Checklist

A. Business Need

• Is there a clearly defined use case?
• Are expected outcomes measurable?
• Have non-AI alternatives been considered?
• Is there an executive or departmental sponsor?

B. Data Security

• What data types will be used?
• Is sensitive or regulated data involved?
• Is data encrypted at rest and in transit?
• Does the tool support SSO/MFA?
• Are logs and audit trails available?
• Will enterprise data be used for vendor model training?
• Are retention and deletion controls documented?

C. Vendor Compliance

• Has the vendor passed security review?
• Are compliance certifications current?
• Are subprocessors disclosed?
• Are contract terms acceptable?
• Is data residency acceptable?
• Is breach notification contractually defined?
• Are exit and portability terms sufficient?

D. Integration and Workflow

• Does the tool integrate with existing systems?
• Is API access available and secure?
• Are workflow changes documented?
• Is human oversight built into the process?
• Are fallback procedures defined?
• Have user training and support plans been created?

E. ROI

• What is the baseline?
• What metrics will be tracked?
• What are one-time and recurring costs?
• What is the expected payback period?
• Who owns measurement and reporting?
• What are the stop/go criteria after pilot?

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Appendix B: Sample Approval Matrix

Risk Tier	Business Sponsor	IT	Security	Legal/Privacy	Procurement	AI Governance Committee	Executive Approval
Tier 1	Required	Required	Required	As needed	As needed	Not required unless flagged	Not required
Tier 2	Required	Required	Required	Required	Required	Required	As needed
Tier 3	Required	Required	Required	Required	Required	Required	Required

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Appendix C: Sample ROI Scorecard

Metric	Baseline	Target	Actual at 90 Days	Actual at 6 Months	Owner
Time per task	30 min	20 min	22 min	18 min	Ops Lead
Error rate	8%	4%	5%	3%	QA Manager
User adoption	0%	75%	62%	81%	Department Head
Monthly cost	$0	$25,000	$24,500	$24,500	Finance
Net savings	$0	$40,000	$28,000	$52,000	Business Sponsor

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If you want, I can also turn this into one of these formats:

1. a more formal board-ready policy,
2. a concise 2-page executive policy,
3. a procedure document with workflow steps and approval gates, or
4. a policy plus RACI matrix and intake form.

ENTERPRISE AI GOVERNANCE AND DEPLOYMENT POLICY

Version: 1.0
Effective Date: [Date]
Policy Owner: Chief Information Officer / Chief Data Officer
Review Cycle: Quarterly

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1. PURPOSE AND SCOPE

This policy establishes a standardized framework for evaluating, procuring, deploying, and monitoring Artificial Intelligence (AI) tools across all business units, subsidiaries, and departments. It ensures that AI implementations align with organizational risk tolerance, data protection obligations, operational requirements, and strategic financial objectives.

Scope: Applies to all AI/ML tools, including:

• Generative AI (LLMs, text/image generation)
• Predictive analytics and decision-support systems
• Automated workflow and RPA tools with embedded AI
• Third-party SaaS products utilizing AI/ML functionality
• Internally developed AI models

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2. GOVERNANCE STRUCTURE

AI Steering Committee (AISC)
Chaired by the CIO/CDO, comprising representatives from Legal, InfoSec, Procurement, Finance, and business unit leaders. Responsible for:

• Approving all AI deployments beyond pilot phase
• Maintaining the Approved AI Vendor List (AAVL)
• Adjudicating risk exceptions

AI Review Board (AIRB)
Technical sub-committee evaluating specific tools against criteria in Section 4.

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3. PRE-EVALUATION REQUIREMENTS

Before formal evaluation, departments must submit an AI Intake Request documenting:

• Business problem and use case description
• Data classes involved (per Data Classification Policy)
• Integration touchpoints with existing systems
• Preliminary budget and resource requirements

Prohibited Use Cases (Automatic Denial):

• AI systems requiring unfettered access to production databases containing PII/PHI without anonymization
• Fully autonomous decision-making for hiring, firing, or credit/lending without human-in-the-loop
• Vendors unable to provide model training data provenance

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4. EVALUATION CRITERIA FRAMEWORK

All AI tools must undergo assessment across four mandatory dimensions. Minimum passing score: 75/100 per dimension.

4.1 Data Security and Privacy (Weight: 30%)

Requirement	Standard	Verification Method
Data Residency	Customer data must remain in specified geographic regions (US, EU, etc.)	Contractual guarantees + infrastructure audit
Encryption	AES-256 at rest; TLS 1.3 in transit; end-to-end encryption for sensitive inputs	Penetration testing results
Model Training Prohibition	Explicit contractual prohibition on using enterprise data for model training without opt-in	Data Processing Agreement (DPA) review
PII Handling	Ability to detect, mask, and exclude PII from model inputs	Technical demonstration + SOC 2 Type II validation
Output Sanitization	Mechanisms to prevent leakage of training data or sensitive embeddings in outputs	Red-team testing
Retention & Deletion	Automated data purging within 30 days of contract termination; right to deletion audits	Policy documentation review

Compliance Standards: GDPR Article 22, CCPA/CPRA, SOC 2 Type II, ISO 27001:2022, NIST AI RMF

4.2 Vendor Compliance and Risk Management (Weight: 25%)

Requirement	Standard	Documentation Required
Financial Stability	Vendor operational for >3 years or backed by qualified institutional capital	Financial statements/VC backing verification
Insurance	$5M+ Cyber Liability + E&O coverage	Certificate of insurance
Audit Rights	Annual third-party security audits and algorithmic impact assessments	Contract clauses + audit reports
Business Continuity	99.9% SLA minimum; data portability guarantees; 90-day wind-down assistance	SLA documentation
AI Ethics Certification	Adherence to IEEE 2857-2021 or equivalent algorithmic bias standards	Third-party bias audit results
Subprocessor Transparency	Disclosure of all AI infrastructure providers (cloud, model APIs, annotation services)	Subprocessor list and DPA cascade

Vendor Risk Tiers:

• Critical Risk: Tools processing >10,000 customer records or affecting financial decisions → Executive approval required
• Standard Risk: General productivity tools → Department head + InfoSec approval
• Low Risk: Publicly available tools with no data input → Self-assessment checklist

4.3 Technical Integration and Workflow Compatibility (Weight: 25%)

Requirement	Standard	Evaluation Method
API Standards	RESTful APIs or GraphQL with OpenAPI documentation; OAuth 2.0 / SAML SSO	Technical architecture review
Legacy Compatibility	Support for integration with [ERP System], [CRM Platform], and existing data lakes	POC integration testing
Scalability	Ability to handle 3x projected peak load without latency degradation >500ms	Load testing in staging environment
Human-in-the-Loop	UI requirements for override capabilities, confidence scoring visibility, and audit trails	UX review with end-users
Change Management	Training resources, change impact documentation, and pilot program support	Vendor implementation plan review
Exit Strategy	Data export in standard formats (JSON, CSV, ONNX for models); API deprecation notices (12 months minimum)	Contract terms

Integration Checkpoints:

• Week 1-2: API compatibility testing
• Week 3-4: Sandbox deployment with synthetic data
• Week 5-8: Pilot with 5-10 users in isolated workflow

4.4 ROI and Value Measurement (Weight: 20%)

Mandatory Metrics (Track for 6 months post-deployment):

Quantitative:

• Efficiency Gains: Hours saved per week per FTE (target: >20% time reduction for targeted tasks)
• Cost Avoidance: Reduction in external contractor spend or error correction costs
• Revenue Impact: Direct attribution to sales conversion or customer retention improvements
• TCO Calculation: Licensing + integration + training costs vs. legacy process costs (minimum 15% net savings Year 1)

Qualitative:

• User Adoption Rate: >70% active usage within 30 days of rollout
• Error Reduction: <2% hallucination rate (for generative AI) or <1% false positive rate (for predictive models)
• CSAT Impact: Maintenance or improvement of customer satisfaction scores post-implementation

ROI Governance:

• Finance must approve measurement methodology pre-deployment
• Tools failing to meet 80% of projected ROI at 6-month mark enter remediation or sunset procedures
• Vendor payment terms should include 20% holdback pending ROI validation

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5. DEPLOYMENT LIFECYCLE

Phase 1: Pilot (30-90 days)

• Maximum 25 users
• Synthetic or anonymized data only
• Daily monitoring of outputs for drift or bias
• Weekly steering committee check-ins

Phase 2: Controlled Production (Months 3-6)

• Expanded to single department
• Real data permitted per DPA
• A/B testing against incumbent processes
• Monthly security scans

Phase 3: Enterprise Rollout

• Cross-departmental deployment
• Full integration with SSO and audit systems
• Quarterly vendor business reviews
• Annual algorithmic audit

No-Go Criteria (Deployment Halt Triggers):

• Data leakage incidents
• Bias detection affecting protected classes (race, gender, age) with >5% variance in outcomes
• Vendor acquisition or leadership change affecting data handling
• Regulatory changes rendering use case non-compliant

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6. RISK MANAGEMENT AND ETHICAL AI

Algorithmic Accountability:

• All automated decision-making requires "Explainability Statements" documenting model logic
• Annual bias audits for tools affecting customers or employees
• Maintain "Human Override" requirements for high-stakes decisions

Business Continuity:

• AI tools may not be sole decision-makers for critical infrastructure or financial controls
• Fallback procedures must exist for AI outages (manual processes maintained and tested quarterly)

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7. TRAINING AND CHANGE MANAGEMENT

• Mandatory Training: All users must complete "AI Ethics and Data Handling" certification before access
• Role-Based Access: Tiered permissions (Viewer, Operator, Administrator) based on data sensitivity
• Prompt Engineering Standards: Approved prompt libraries for generative AI to prevent data leakage

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8. MONITORING AND COMPLIANCE

Continuous Monitoring:

• Automated scanning of AI outputs for PII leakage
• Monthly access reviews
• Annual third-party penetration testing

Audit Rights:

• Internal Audit may review any AI deployment with 48 hours notice
• Vendor must provide model cards and training data summaries upon request

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9. EXCEPTIONS AND VIOLATIONS

Exception Process: Written request to AISC with risk mitigation plan; requires CIO + General Counsel signature.

Violations:

• Unauthorized deployment of non-approved AI tools: Immediate suspension of tool access, disciplinary action up to termination
• Data leakage through AI tools: Incident response plan activation, vendor suspension, regulatory notification as required

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10. APPENDICES

Appendix A: AI Evaluation Scorecard (Quantitative rubric for standardized scoring)
Appendix B: Approved Vendor List (Pre-vetted providers meeting Section 4 criteria)
Appendix C: Data Classification Matrix (Mapping data types to approved AI use cases)
Appendix D: Incident Response Playbook (Specific procedures for AI-related security events)

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Approval Signatures:

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Chief Information Officer
Date: ___________

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General Counsel
Date: ___________

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Chief Information Security Officer
Date: ___________

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Review History:

• v1.0: Initial policy enactment
• Next Review: [Date + 90 days]