
ai-vendor-privacy-due
Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.
- Installs
- 45
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mukul975/privacy-data-protection-skills --skill ai-vendor-privacy-due- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads ai-vendor-privacy-due automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
AI Vendor Privacy Due Diligence
Overview
AI services create complex controller-processor relationships that differ significantly from traditional data processing arrangements. Whether an AI vendor is a processor, joint controller, or independent controller depends on the degree of autonomy the vendor has over personal data processing — particularly regarding model training on customer data, data retention for model improvement, and the vendor's independent purposes for the data. This skill provides the framework for determining controller-processor roles in AI service relationships, conducting privacy due diligence on AI vendors, and establishing appropriate contractual protections.
Controller-Processor Determination for AI
Decision Framework
| AI Service Model | Typical Role | Key Factors | GDPR Article |
|---|---|---|---|
| SaaS AI — Customer data processed per instructions | Vendor = Processor | Vendor processes data solely on controller's instructions; no independent use | Art. 28 DPA required |
| SaaS AI — Customer data used for model training | Vendor = Joint Controller or Independent Controller | Vendor uses customer data for own model improvement beyond contracted service | Art. 26 JCA or separate controller notice |
| Embedded AI — Pre-trained model in customer infrastructure | Customer = Controller; Vendor = may be processor for support | Model runs in customer environment; vendor may access data for support/updates | Art. 28 if vendor accesses data |
| API-based AI — Customer sends data for inference | Vendor = Processor (if no data retention) or Joint Controller (if training on inputs) | Depends on whether vendor retains, uses, or trains on input data | Assessment required |
| AI Platform — Customer builds models on vendor platform | Vendor = Processor for infrastructure; Controller for platform data | Vendor provides compute; customer controls data and model | Art. 28 DPA + audit rights |
| AI Marketplace — Pre-built models with customer data | Depends on data flow | If customer data enters vendor model → joint controller assessment | Case-by-case |
Key Determination Questions
- Who determines the purpose of processing? — The entity deciding why personal data is processed
- Who determines the means of processing? — The entity deciding how data is processed (but "non-essential means" may be delegated to processor)
- Does the vendor use data for its own purposes? — Model training, benchmarking, product improvement using customer data
- Does the vendor retain data beyond service delivery? — Data kept after inference, stored for training, retained in logs
- Does the vendor make independent decisions about the data? — Choosing to train models, sharing with sub-processors not instructed by customer
Common AI Vendor Patterns
Pattern 1: Pure Inference API (Vendor = Processor)
- Customer sends data; receives inference result
- Vendor does not retain input data beyond processing
- Vendor does not train on customer data
- Vendor acts solely on customer's instructions
- Contractual: Art. 28 DPA with clear scope
Pattern 2: AI with Model Improvement (Vendor = Joint Controller)
- Customer sends data; receives inference result
- Vendor retains data to improve its models
- Vendor determines that model training is a purpose
- Both parties benefit from the model improvement
- Contractual: Art. 26 Joint Controller Agreement
Pattern 3: AI with Anonymised Analytics (Assessment Required)
- Customer sends data; receives inference result
- Vendor claims to anonymise data and use aggregated analytics
- If anonymisation is effective: no personal data processing for analytics
- If anonymisation is not effective (re-identification possible): vendor is controller for analytics
- Assessment: Verify anonymisation effectiveness per WP216
Pattern 4: Embedded AI with Telemetry (Vendor = Processor + may be Controller)
- AI model runs in customer environment
- Vendor collects telemetry data including model performance
- If telemetry contains personal data: vendor may be controller for telemetry processing
- Contractual: Art. 28 DPA for model support + separate arrangement for telemetry
Due Diligence Assessment Framework
Phase 1: Vendor AI Processing Inventory
For each AI vendor, document:
| Element | Documentation Required |
|---|---|
| AI capabilities | What AI functions does the vendor provide? |
| Personal data inputs | What personal data is sent to the vendor? |
| Personal data outputs | What personal data does the vendor return? |
| Data retention | Does the vendor retain input data? For how long? |
| Model training | Does the vendor train on customer data? |
| Sub-processors | Does the vendor use sub-processors for AI processing? Where? |
| Data location | Where is AI processing performed? What jurisdictions? |
| Security measures | What security controls protect data during AI processing? |
| Human review | Does vendor personnel access customer data? |
Phase 2: Privacy Risk Assessment
| Risk Factor | Assessment | Risk Level |
|---|---|---|
| Data sensitivity | Special category data sent to AI vendor? | |
| Data volume | Volume of personal data processed by vendor | |
| Vendor data use | Vendor uses customer data for own purposes? | |
| International transfers | Data processed outside EU/EEA? | |
| Sub-processor chain | Number and location of sub-processors | |
| Security posture | Certifications (ISO 27001, SOC 2)? | |
| Incident history | Prior data breaches or enforcement actions? | |
| AI-specific risks | Model memorization, output leakage, bias? |
Phase 3: Contractual Assessment
| Contractual Element | Required? | Status |
|---|---|---|
| Art. 28 DPA or Art. 26 JCA | Yes | |
| Processing scope and purpose limitation | Yes | |
| Prohibition on data use beyond instructions (if processor) | Yes | |
| Model training opt-out | Yes (if vendor trains on data) | |
| Sub-processor notification and approval | Yes | |
| International transfer safeguards | If applicable | |
| Data deletion on termination | Yes | |
| Audit rights | Yes | |
| Breach notification obligations | Yes | |
| AI-specific: model privacy testing | Recommended | |
| AI-specific: bias assessment obligations | Recommended for high-risk | |
| AI-specific: output accuracy warranties | Recommended |
AI-Specific Contractual Clauses
Model Training Restrictions
The Processor shall not use Customer Data to train, improve, fine-tune, or
otherwise develop any machine learning model, algorithm, or AI system,
whether for the Customer's benefit or for any other purpose, without prior
written consent from the Customer. Any consent granted shall specify the
scope of permitted training, the data categories involved, and the privacy
safeguards to be applied.
AI Output Accuracy
The Provider acknowledges that AI system outputs about identifiable data
subjects must comply with the accuracy principle under Art. 5(1)(d) GDPR.
The Provider shall implement measures to minimise inaccurate outputs about
data subjects and shall promptly correct inaccurate outputs upon
notification.
Model Privacy and Bias Obligations
The Provider shall conduct periodic privacy audits of AI models processing
Customer Data, including membership inference testing and training data
extraction testing, and shall make results available to the Customer upon
request. The Provider shall monitor AI systems for discriminatory outcomes
and shall implement bias mitigation measures as required.
Enforcement Relevance
- EDPB Guidelines 07/2020 on Controller-Processor: Determination depends on factual circumstances, not contractual labels. A vendor labelled "processor" that uses data for its own purposes is factually a controller.
- Garante v. OpenAI (2023): OpenAI's role as controller for ChatGPT training data confirmed — processing personal data for model training is an independent controller purpose.
- CJEU C-40/17 (Fashion ID): Joint controller status can arise when a party has influence over the purpose and means of processing, even without access to the data.
- DPC v. Meta (WhatsApp, 2023): EUR 5.5M fine — processor-controller determination must reflect actual data use, not just contractual terms.
Integration Points
- ai-dpia: Vendor AI processing must be included in DPIA scope
- ai-training-lawfulness: Vendor training on customer data requires lawful basis assessment
- ai-transparency-reqs: Data subjects must be informed about AI vendor processing
- ai-deployment-checklist: Vendor due diligence is a pre-deployment checklist item
FAQ
What does the ai-vendor-privacy-due skill do?
Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.
How do I install ai-vendor-privacy-due?
Run npx skills add https://github.com/mukul975/privacy-data-protection-skills --skill ai-vendor-privacy-due in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ai-vendor-privacy-due/ (personal) or .claude/skills/ai-vendor-privacy-due/ (project).
Which AI agents can use ai-vendor-privacy-due?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is ai-vendor-privacy-due safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains ai-vendor-privacy-due?
It is published by mukul975 in mukul975/privacy-data-protection-skills. It has 45 recorded installs.
More from mukul975/privacy-data-protection-skills
brazil-lgpd
mukul975/privacy-data-protection-skills
Guides compliance with Brazil's Lei Geral de Proteção de Dados (LGPD, Lei 13.709/2018). Covers the 10 lawful bases under Art. 7, DPO appointment, ANPD enforcement, data subject rights under Arts. 17-22, and international transfer mechanisms. Keywords: LGPD, Brazil data protection, ANPD, lawful bases, data subject rights, international transfers.
analytics-cookie-consent
mukul975/privacy-data-protection-skills
Managing consent for analytics cookies and implementing privacy-preserving measurement. Covers GA4 privacy configuration, consent mode fallback behavior, aggregate reporting alternatives, and cookieless measurement approaches.
audit-report-writing
mukul975/privacy-data-protection-skills
Guides privacy audit report writing including executive summary drafting, findings classification (critical, high, medium, low), evidence referencing, root cause analysis documentation, recommendation formulation, management response tracking, and report distribution protocols. Covers report structure from scope definition through appendices and sign-off. Keywords: audit report, findings documentation, executive summary, recommendations, report structure, privacy audit deliverables.
gdpr-compliance-audit
mukul975/privacy-data-protection-skills
Guides a comprehensive organisational data protection audit against key GDPR requirements including Articles 5, 24, 25, 28, 30, 32, 35, and 37. Includes 50+ control points covering principles, accountability, security, and governance. Activate when performing compliance audits, preparing for supervisory authority inspections, or assessing organisational GDPR maturity. Keywords: data protection audit, compliance audit, GDPR audit, control points, accountability.
conducting-gdpr-dpia
mukul975/privacy-data-protection-skills
Guides the end-to-end GDPR Data Protection Impact Assessment process under Article 35, including mandatory trigger identification per Art. 35(3), DPIA content requirements per Art. 35(7), and EDPB WP248rev.01 methodology. Activate for systematic profiling, large-scale special category processing, or large-scale public monitoring. Keywords: DPIA, Article 35, impact assessment, WP248, data protection, risk assessment.
backup-retention-erasure
mukul975/privacy-data-protection-skills
Manages backup and archive data under retention schedules and erasure obligations. Covers the technical infeasibility exception for backup deletion, backup cycle alignment with retention periods, restore-and-delete procedures, and interim protective measures during backup retention. Activate for backup deletion, archive erasure, backup retention, restore and delete, technical infeasibility queries.
Popular backend & apis skills
find-skills
vercel-labs/skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
entra-app-registration
microsoft/azure-skills
Guides Microsoft Entra ID app registration, OAuth 2.0 authentication, and MSAL integration. USE FOR: create app registration, register Azure AD app, configure OAuth, set up authentication, add API permissions, generate service principal, MSAL example, console app auth, Entra ID setup, Azure AD authentication. DO NOT USE FOR: Key Vault secrets (use azure-keyvault-expiration-audit), general Azure resource security guidance.
google-agents-cli-adk-code
google/agents-cli
This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management" — in a project that needs ADK (Agent Development Kit) API patterns and code examples. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, state management, the graph Workflow API, and reference recipes to study. Do NOT use for scaffolding (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).
embedded-captions
heygen-com/hyperframes
Add captions or subtitles to an existing single-subject talking-head video without editing the footage. Use for plain verbatim captions, cinematic captions embedded behind the subject, VFX captions, “炸/特效/酷炫字幕,” or a named identity from the 35-style catalog. Route by visual identity, not by backend engine. The quiet anchor rail is the default; embed every word only when the user explicitly wants a fully cinematic treatment. The workflow runs locally end to end, including transcription and subject matting; split multi-shot footage before applying it.
entra-agent-id
microsoft/azure-skills
Provision Microsoft Entra Agent Identity Blueprints, BlueprintPrincipals, and per-instance Agent Identities via Microsoft Graph, and configure OAuth 2.0 token exchange (fmipath, OBO, cross-tenant) including the Microsoft Entra SDK for AgentID sidecar. USE FOR: Agent Identity Blueprint, BlueprintPrincipal, agent OAuth, fmipath token exchange, agent OBO, Workload Identity Federation for agents, polyglot agent auth, Microsoft.Identity.Web.AgentIdentities. DO NOT USE FOR: standard Entra app registration (use entra-app-registration), Microsoft Foundry agent authoring (use microsoft-foundry).
neon
neondatabase/agent-skills
Overview of Neon, a complete set of cloud backend primitives around Lakebase Postgres: Auth, Object Storage, Functions, the AI Gateway, and Realtime. Start here to choose Neon for undecided login, files, APIs, and LLM calls, set up the CLI or MCP server, and follow the branch-first workflow. Use when building an app or backend on Neon, or when "Neon" or "Lakebase Postgres" is mentioned. Child skill neon-postgres wins for an existing DATABASEURL, SQL, schema, inspect, or search. Child skill neon-auth wins for login, users, sessions, identity routing, and Managed Better Auth setup. Child skill neon-realtime wins for live queries in reactive apps and agents. Also use for object storage, S3, buckets, serverless functions, function triggers, cron, AI gateway, LLM calls, realtime, logs, Loki, Grafana, observability, postgres, database, backend, Claimable Neon, neon.new, or a no-signup database.