PRD-STD-018: Multi-Modal AI Governance
Standard ID: PRD-STD-018
Version: 1.0
Status: Active
Compliance Level: Level 3 (Defined)
Effective Date: April 2026
Last Reviewed: April 2026
This page is the normative source of requirements for governance of vision-enabled AI coding workflows.
For implementation support:
- Vision-to-code patterns: Vision-to-Code Workflows
- Tool guides: Kimi Code, Gemini Code Assist
- Related standards: PRD-STD-001: Prompt Engineering
Use the Compliance Level metadata to sequence adoption with other PRD-STDs.
1. Purpose
This standard defines governance requirements for AI-assisted development using multi-modal inputs (images, video, diagrams, mockups). Vision-enabled coding tools (Kimi K2.5, Gemini 2.5, Claude with vision) introduce unique risks around intellectual property, data privacy, and output verification that require specific controls.
Multi-modal AI coding enables:
- UI mockup to implementation conversion
- Screenshot to code generation
- Video workflow reconstruction
- Visual debugging and comparison
These capabilities require governance frameworks addressing visual input handling, intellectual property protection, and visual output verification.
2. Scope
This standard applies to:
- All AI-generated code derived from visual inputs (images, screenshots, mockups, videos, diagrams)
- All tools with vision capabilities including Kimi Code, Gemini Code Assist, Claude (vision mode), and similar
- All engineers using visual inputs for code generation in production workflows
- All visual assets used as AI context including UI designs, architecture diagrams, data visualizations
This standard does NOT apply to:
- Text-only AI coding workflows (covered by PRD-STD-001)
- Visual inputs used exclusively for documentation or communication (not code generation)
- Personal prototyping without production deployment intent
3. Definitions
| Term | Definition |
|---|---|
| Visual Prompt | Any image, screenshot, video, diagram, or mockup provided to an AI coding tool as input context |
| Vision-to-Code | The process of generating implementation code from visual specifications |
| Mockup | A visual representation of UI/UX design, typically in image format (PNG, JPG, Figma export) |
| Visual IP | Intellectual property rights associated with visual designs, including copyright and trademark |
| Source Provenance | Documentation of the origin, ownership, and licensing of visual inputs |
| Visual Diff | Comparison between visual specification and rendered output to verify fidelity |
| Multi-Modal Model | AI models trained on both visual and textual data (Kimi K2.5, Gemini 2.5) |
4. Requirements
4.1 Visual Input Governance
REQ-018-01: Visual inputs MUST NOT contain personally identifiable information (PII), sensitive personal data, confidential business information, or credentials.
REQ-018-02: All visual inputs MUST have documented source provenance including origin, ownership, and licensing status before being used as AI context.
REQ-018-03: Visual inputs from third-party sources (stock images, competitor screenshots, client materials) MUST be cleared for AI processing under their respective licenses.
REQ-018-04: Screenshots of production systems containing real user data MUST be anonymized or synthetic before AI processing.
REQ-018-05: Visual inputs SHOULD include a text description of the intended functionality to reduce ambiguity.
REQ-018-06: Teams SHOULD maintain a library of approved visual templates for common UI patterns to reduce one-off visual prompt usage.
4.2 Intellectual Property Protection
REQ-018-07: UI mockups and designs from external agencies or designers MUST have explicit licensing or work-for-hire agreements permitting AI-assisted implementation.
REQ-018-08: Visual inputs containing third-party trademarks, logos, or copyrighted characters MUST be flagged and reviewed by legal/compliance before code generation.
REQ-018-09: Code generated from visual inputs MUST NOT reproduce copyrighted visual elements (icons, illustrations, proprietary layouts) without license.
REQ-018-10: Organizations SHOULD establish approved visual asset libraries with clear AI-usage rights to minimize ad-hoc visual prompting.
REQ-018-11: Teams SHOULD document the visual-to-code transformation process to demonstrate independent implementation in case of IP disputes.
4.3 Output Verification
REQ-018-12: All code generated from visual inputs MUST undergo visual diff verification — comparing rendered output against the original specification.
REQ-018-13: Accessibility requirements (WCAG compliance, semantic HTML) MUST be verified independently of visual fidelity — AI may reproduce visual appearance without proper accessibility.
REQ-018-14: Responsive behavior MUST be tested across target breakpoints — visual inputs typically represent single viewport states.
REQ-018-15: Teams SHOULD establish pixel-precision thresholds (e.g., "within 2px") for visual fidelity acceptance.
REQ-018-16: Visual diffs SHOULD be captured as evidence for design review and compliance documentation.
4.4 Video and Animation Inputs
REQ-018-17: Video inputs used for code generation MUST specify the temporal scope (start/end timestamps) relevant to the implementation.
REQ-018-18: Animation timing and easing functions extracted from video MUST be documented as approximations requiring designer validation.
REQ-018-19: Video source provenance MUST include license verification for the source material.
REQ-018-20: Video-to-code workflows SHOULD extract keyframes rather than processing full video to reduce cost and improve focus.
4.5 Audit and Documentation
REQ-018-21: All vision-to-code workflows MUST log: (a) visual input source/provenance, (b) prompt description, (c) generated code location, (d) reviewer approval.
REQ-018-22: Visual inputs used for production code MUST be retained in version control or design system for the duration of the code lifecycle.
REQ-018-23: Teams SHOULD maintain a "visual prompt library" of approved patterns with standardized descriptions.
4.6 Security and Privacy
REQ-018-24: Visual inputs MUST be scanned for accidental inclusion of sensitive data (API keys in screenshots, PII in mock data, internal URLs).
REQ-018-25: Cloud-based vision models MUST be evaluated for data retention policies — visual inputs may be treated differently than text.
REQ-018-26: Self-hosted vision models (e.g., self-hosted Kimi K2.5) MUST be used for sensitive visual inputs where data sovereignty is required.
5. Compliance Levels
| Level | Requirement |
|---|---|
| Level 1 (Uncontrolled) | No specific multi-modal governance. Visual inputs used ad-hoc without verification. |
| Level 2 (Exploratory) | REQ-018-01, REQ-018-04, REQ-018-12 implemented. Basic visual input hygiene. |
| Level 3 (Defined) | All MANDATORY requirements implemented. Visual provenance tracked, IP verified, diffs required. |
| Level 4 (Managed) | All Level 3 + RECOMMENDED requirements. Visual asset library established, standardized workflows. |
| Level 5 (AI-First) | Automated visual diff testing, integrated accessibility validation, real-time IP checking. |
6. Tool-Specific Guidance
Kimi Code (Vision Capabilities)
Capabilities:
- Native multimodal training (15T visual-text tokens)
- 256K context supports multiple mockups
- Agent Swarm can process visual tasks in parallel
Governance Considerations:
- Self-hosting available for sensitive visual inputs
- Cost-efficient for large-scale visual processing
- Open-weight enables audit of vision encoder (MoonViT)
Configuration:
# Enable audit logging for visual inputs
export KIMI_AUDIT_VISUAL=true
# Use self-hosted for sensitive designs
kimi --endpoint http://internal-kimi/api "Process mockup.png"
Gemini Code Assist (Vision Capabilities)
Capabilities:
- 1M token context (largest available)
- Native integration with Google Workspace
- Video understanding capabilities
Governance Considerations:
- Google data policies apply to visual inputs
- Enterprise tier offers enhanced data protection
- Video inputs may require additional storage
Configuration:
// .gemini/config.json
{
"visualInputs": {
"requireProvenance": true,
"retainInProject": true,
"accessibilityCheck": true
}
}
Claude (Vision Mode)
Capabilities:
- 200K context with vision
- Strong reasoning about visual content
- Refusal capabilities for inappropriate visual inputs
Governance Considerations:
- Proprietary model — audit limited to outputs
- API-level logging available for compliance
- Higher cost per token for vision inputs
7. Implementation Checklist
Immediate (Week 1)
- Identify all vision-enabled tools in use
- Create visual input sanitization checklist
- Establish source provenance documentation template
- Train team on visual input requirements
Short Term (Month 1)
- Implement visual diff verification process
- Create approved visual asset library
- Document IP clearance workflow
- Configure tool-specific audit logging
Medium Term (Quarter 1)
- Establish visual prompt library with standardized descriptions
- Integrate accessibility testing into vision-to-code workflow
- Implement pixel-precision thresholds
- Create compliance dashboard for visual workflow metrics
8. Related Standards
- PRD-STD-001: Prompt Engineering — Base prompting standards
- PRD-STD-002: Code Review — Review requirements for AI-generated code
- PRD-STD-009: Autonomous Agent Governance — Agent workflow governance
- Vision-to-Code Workflows — Practical implementation guide
9. References
| Reference | Description |
|---|---|
| Kimi K2.5 Technical Report | Moonshot AI multimodal capabilities |
| Gemini 2.5 Pro Documentation | Google vision model specifications |
| WCAG 2.1 | Web Content Accessibility Guidelines |
| Copyright Act (various jurisdictions) | IP protection for visual designs |