New section for org-level Claude Code governance — fills the gap between individual dev security (security-hardening.md) and what engineering managers actually need when deploying at scale. New files: - guide/security/enterprise-governance.md (1117 lines) 6 sections: local/shared split, usage charter, MCP approval workflow, 4 guardrail tiers (Starter/Standard/Strict/Regulated), policy enforcement at scale, SOC2/ISO27001 compliance guide - examples/scripts/mcp-registry-template.yaml Org-level MCP registry with approved/pending/denied tracking - examples/hooks/bash/governance-enforcement-hook.sh SessionStart hook validating MCPs against approved registry - examples/scripts/ai-usage-charter-template.md Full charter template with data classification, use case rules, compliance mapping (SOC2/ISO27001/HIPAA/PCI DSS/GDPR) Enriched sections: - adoption-approaches.md: enterprise rollout (50+ devs) with 3-phase approach and common mistakes - observability.md: manager audit checklist, compliance reporting - ai-traceability.md: evidence collection table for auditors - production-safety.md + security-hardening.md: cross-references with explicit scope boundaries Integration: guide/README.md, reference.yaml (22 new entries), CHANGELOG.md Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
200 lines
8.2 KiB
Markdown
200 lines
8.2 KiB
Markdown
# AI Coding Tools Usage Charter
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> **Template** — Copy to `docs/ai-usage-charter.md` in your organization's docs repo.
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> Adapt sections marked with `[BRACKETS]`.
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>
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> Related: guide/security/enterprise-governance.md §2
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---
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**Organization**: [Your Organization]
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**Applies to**: Claude Code and all AI coding assistants
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**Effective date**: [DATE]
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**Owner**: [Engineering Lead / CTO / CISO]
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**Review cadence**: Quarterly
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**Version**: 1.0.0
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---
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## 1. Purpose
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This charter defines how [Organization] employees may use AI coding assistants, which tools are approved, what data can be used with them, and who is accountable for compliance. The goal is to enable productive AI-assisted development while managing security, privacy, and regulatory risk.
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---
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## 2. Approved Tools
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| Tool | Plan / Tier | Scope | Administered by |
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|------|-------------|-------|----------------|
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| Claude Code | Team/Enterprise | All engineering work | Platform Team |
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| Claude Code | Personal Pro | Personal dev only (no company data above INTERNAL) | Individual |
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| [Other Tool] | [Plan] | [Scope] | [Team] |
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**Using non-approved AI coding tools on company systems is prohibited.** If you believe an additional tool would benefit your work, submit an approval request to [engineering-lead@company.com].
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---
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## 3. Data Classification Rules
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All data at [Organization] is classified into four levels. Your use of AI tools must comply with this classification.
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| Classification | Examples | Claude Code permitted? | Notes |
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|----------------|----------|----------------------|-------|
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| **PUBLIC** | Open-source code, public documentation | Yes — no restrictions | |
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| **INTERNAL** | Internal tools, non-sensitive business code | Yes — standard config | Use company accounts |
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| **CONFIDENTIAL** | Customer data (non-PII), business secrets, proprietary algorithms | Yes — Enterprise plan only, with approved config | |
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| **RESTRICTED** | PCI card data, PHI, credentials, auth tokens, encryption keys | **Never** | No exceptions |
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### Hard Rules
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- RESTRICTED data **never** enters an AI context window — not in prompts, not in files Claude reads, not as examples.
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- Personal AI accounts (personal Pro subscriptions) may only be used with PUBLIC or INTERNAL data.
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- Company credentials and API keys are RESTRICTED. Never paste them into prompts.
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### Technical Enforcement
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Projects handling CONFIDENTIAL or RESTRICTED data must configure `permissions.deny` in `.claude/settings.json` to block AI access to sensitive files. See the [Standard or Regulated tier config](../security/enterprise-governance.md#4-guardrail-tiers) for ready-to-use configurations.
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---
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## 4. Approved Use Cases
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The following uses of AI coding assistants are approved without additional review:
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- Code completion and generation for approved data classifications
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- Code review and quality analysis
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- Test generation and mutation testing
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- Documentation drafting and updating
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- Debugging, root cause analysis, log analysis
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- Architecture analysis of internal systems
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- CLI scripting, automation, build tooling
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- Refactoring and code cleanup
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---
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## 5. Prohibited Use Cases
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The following are **not permitted** without explicit written approval:
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| Prohibited use | Reason | Exception process |
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|----------------|--------|------------------|
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| Processing raw PCI cardholder data | Regulatory (PCI DSS) | None — technically enforced |
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| Generating code handling unencrypted PHI without security review | Regulatory (HIPAA) | Security review required |
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| Autonomous deployment to production | Human oversight requirement | Approval from Eng Director |
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| Using personal AI accounts for CONFIDENTIAL data | Data residency/privacy | Upgrade to company account |
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| Sharing customer data as examples in prompts | Privacy, data handling | Use synthetic data |
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| Bypassing governance controls (hooks, deny rules) | Policy violation | None |
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---
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## 6. MCP Server Governance
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Model Context Protocol (MCP) servers extend Claude Code's capabilities and introduce additional risk surface. All MCP servers used on company projects must be:
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1. Listed in the approved registry (`.claude/mcp-registry.yaml` in the platform config repo)
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2. Pinned to an exact version (never `@latest`)
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3. Re-reviewed every 6 months or on major version bumps
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**Approval process**: Submit a request with server name, source URL, intended use case, and data scope to [platform-team@company.com] or [#claude-code-requests Slack channel].
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Unapproved MCPs detected in project configs will be flagged at session start. Developers have 48 hours to remove or seek approval.
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---
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## 7. Code Review and Attribution
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### AI Attribution
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All pull requests where AI assisted in writing code must include an AI disclosure section:
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```markdown
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## AI Assistance
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- AI tool used: Claude Code
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- Scope: [e.g., "Generated tests for auth module", "Refactored payment handler"]
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- Human review: Reviewer checked logic, security implications, and edge cases
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```
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The standard `Co-Authored-By: Claude <noreply@anthropic.com>` commit trailer (added automatically by Claude Code) satisfies attribution for routine changes. Explicit PR disclosure is required for:
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- Any change to authentication or authorization
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- Any change to payment or financial logic
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- Any database schema change
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- Any new external API integration
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### Code Review Expectations
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AI-generated code is not exempt from code review. Reviewers should apply the same — or stricter — scrutiny to AI-generated sections, particularly for:
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- Security-sensitive paths (auth, crypto, access control)
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- Data handling (PII, financial data)
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- Edge cases and error conditions that AI tools often miss
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---
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## 8. Accountability
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### Developer Responsibilities
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By using Claude Code on company systems, you agree to:
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- Follow this charter
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- Report suspected data exposure to [security@company.com] within 24 hours
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- Participate in quarterly access reviews
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- Complete AI tools onboarding checklist when joining or switching teams
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- Not circumvent governance controls (hooks, deny rules, registry)
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### Team Lead Responsibilities
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- Ensure projects are configured with the appropriate guardrail tier
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- Review MCP registry quarterly
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- Include AI charter in team onboarding
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- Escalate charter violations per §9
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### Platform Team Responsibilities
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- Maintain shared governance config (settings.json templates, hooks)
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- Review MCP approval requests within 1 week
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- Update charter when tools, tiers, or risks change
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- Conduct semi-annual governance audit
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---
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## 9. Incident Response
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### If you suspect data exposure
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1. **Stop** the current AI session immediately
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2. **Document** what data may have been included in the context (which files, what prompts)
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3. **Report** to [security@company.com] with "AI Data Exposure" in subject, within 24 hours
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4. **Do not** attempt to investigate or remediate without security guidance
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### Charter violations
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| Severity | Examples | Response |
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|----------|----------|---------|
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| **Minor** | Forgot AI attribution in PR, used unapproved MCP briefly | Coaching, remediation |
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| **Moderate** | Used personal account with CONFIDENTIAL data, bypassed a hook | Formal warning, re-training |
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| **Severe** | Processed RESTRICTED data with AI, persistent bypass of controls | HR/legal involvement |
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---
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## 10. Compliance Mapping
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This charter addresses the following compliance requirements:
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| Framework | Relevant Controls | This Charter Addresses |
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|-----------|------------------|----------------------|
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| **SOC 2** | CC6.1 (Logical access), CC7.1 (Monitoring), CC9.2 (Vendor risk) | Data classification, MCP registry, audit logging |
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| **ISO 27001** | A.8.3 (Access restriction), A.8.25 (Secure dev lifecycle), A.5.23 (Cloud services) | Approved tools, governance tiers, data handling |
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| **HIPAA** (if applicable) | Security Rule §164.312 (Access control, Audit controls) | RESTRICTED data prohibition, compliance-mode logging |
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| **PCI DSS** (if applicable) | Req 6.3 (Security vulnerabilities), Req 12 (Policy) | PCI data prohibition, code review requirements |
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| **GDPR/CCPA** | Data minimization, purpose limitation | CONFIDENTIAL/RESTRICTED classification, data scope rules |
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---
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## 11. Revision History
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| Version | Date | Author | Changes |
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|---------|------|--------|---------|
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| 1.0.0 | [DATE] | [Name] | Initial version |
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---
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*Questions or exceptions: [engineering-lead@company.com] | Slack: [#ai-tools-governance]*
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