A complete AI governance program requires five core policy documents: an AI Use Policy, an AI Risk Management Policy, an AI Incident Response Policy, an AI Data Governance Policy, and a Vendor AI Policy. This guide explains what each policy must cover, why each is required under TRAIGA, and how to generate them without starting from scratch.

Why written policies are required

AI governance controls are only as strong as the policies that define them. A human oversight mechanism that is not documented in policy can be circumvented without consequence. A bias testing procedure that exists only in someone's head is not auditable. Policies transform governance intentions into enforceable organizational commitments.

Under TRAIGA, documented policies are not optional — they are the evidence that your governance program is real. In an enforcement investigation, the AG's office will ask to see your policies. The absence of documented policies is itself evidence of non-compliance.

Policy 1: AI Use Policy

The AI Use Policy defines what AI systems the organization is permitted to use, under what conditions, and with what approvals. It is the governance foundation for your entire program.

A compliant AI Use Policy must cover:

  • Scope: What counts as an AI system for governance purposes, and which uses are in-scope for the policy
  • Approval process: How new AI systems are evaluated, approved, and registered before deployment
  • Prohibited uses: AI applications that the organization will not permit under any circumstances
  • Acceptable use standards: Expectations for employees using AI tools in their work
  • Review requirements: How often AI systems must be reviewed and who is responsible

Policy 2: AI Risk Management Policy

The AI Risk Management Policy defines how the organization identifies, assesses, and manages risk from AI systems. It must specify:

  • The risk classification framework (how systems are categorized as LOW, MODERATE, HIGH, or CRITICAL)
  • The risk assessment methodology (what factors are evaluated, how the questionnaire is structured, how classifications are determined)
  • Control requirements by risk level (what controls are mandatory for each classification tier)
  • Re-assessment triggers (when systems must be re-evaluated outside of the normal review cycle)
  • Escalation procedures for high-risk findings

Policy 3: AI Incident Response Policy

The AI Incident Response Policy defines what constitutes an AI incident, how incidents are reported and investigated, and when external notification is required. Without this policy, your team has no defined process when something goes wrong — and things will go wrong.

Required elements:

  • Incident definition: What triggers the incident response process (erroneous outputs, biased decisions, system failures, security events)
  • Severity classification: A defined framework for categorizing incidents as Critical, High, Medium, or Low
  • Reporting chain: Who to notify at each severity level, within what time window
  • Investigation requirements: What documentation is required, who leads the investigation, and what constitutes resolution
  • External notification: Circumstances that require notifying regulators, affected individuals, or the public
  • Post-incident review: How incidents feed back into risk assessments and control updates

Policy 4: AI Data Governance Policy

AI systems are only as trustworthy as the data they use. The AI Data Governance Policy documents how data is managed across the AI lifecycle: collection, training, inference, and retention.

Required coverage:

  • Data quality standards for training and inference data
  • Bias monitoring requirements — how often data is audited for representational gaps, and who is responsible
  • Data retention and deletion policies, including obligations under HIPAA, CCPA, and other applicable regulations
  • Third-party data sharing — what data can be shared with AI vendors and what restrictions apply
  • Prohibited data uses — data categories that may not be used in AI decision-making without explicit additional governance

Policy 5: Vendor AI Policy

The majority of AI systems in most organizations are third-party tools — off-the-shelf software with embedded AI capabilities. Under TRAIGA, the deploying organization (not the vendor) bears the governance obligations. The Vendor AI Policy defines how third-party AI is evaluated, approved, and monitored.

Required elements:

  • Vendor due diligence requirements before deploying a third-party AI system
  • Contractual obligations — what governance representations must vendors provide
  • Ongoing monitoring — how the organization verifies that vendor AI systems continue to perform as represented
  • Termination triggers — circumstances that require discontinuing a vendor AI system

Generating compliant policies efficiently

Writing all five policies from scratch typically takes 4–8 weeks of legal and compliance staff time. The TRAIGA platform generates policy templates pre-populated with your organization's specific system inventory, risk classifications, and control assignments — reducing policy development to a review and customization exercise that typically takes days, not weeks.

Whichever approach you use, ensure that your policies are reviewed by qualified legal counsel, approved at the appropriate organizational level, and reviewed on a defined schedule — at least annually, or when material changes occur in your AI portfolio.