Team Governance 9 min read

AI Governance and Security for Claude Code Teams

A comprehensive framework for maintaining security, compliance, and visibility when scaling Claude Code across your engineering organization.

By Julian Pechler |

The Governance Challenge

Your engineering team has adopted Claude Code. Productivity is up. But in the security team’s weekly review, uncomfortable questions emerge:

“What data are we sending to Claude? Who’s using it for what? How do we know sensitive information isn’t leaking? What’s our audit trail for compliance?”

These aren’t theoretical concerns. Organizations without AI governance have experienced:

  • Credential exposure: Developers accidentally including API keys, database credentials, or service account tokens in prompts
  • PII leakage: Customer data, employee information, or health records inadvertently sent for processing
  • Proprietary code exposure: Core algorithms or competitive advantages shared without appropriate controls
  • Compliance gaps: Inability to demonstrate appropriate AI oversight to auditors or regulators
  • Shadow AI: Teams using personal accounts or unapproved tools when official guidance is unclear

The challenge isn’t whether to allow Claude Code—the productivity benefits are too significant. The challenge is how to enable safe, compliant, visible usage that your security team and regulators can trust.

Building a Governance Framework

Effective AI governance operates on three levels: visibility (knowing what’s happening), policy (defining what should happen), and enforcement (ensuring it does happen). Most organizations fail by jumping straight to enforcement without establishing visibility first.

Level 1: Visibility

Before you can govern, you need to see. Visibility means understanding:

Who is using Claude Code:

  • Individual developer identity
  • Team/department affiliation
  • Role and access level
  • Usage frequency and patterns

What they’re doing:

  • Types of tasks (code generation, review, documentation)
  • Skills and prompts being used
  • Volume and complexity of requests
  • Input and output patterns (without storing sensitive content)

When usage occurs:

  • Time patterns (business hours vs. off-hours)
  • Correlation with sprints, releases, incidents
  • Trends over time

Where in your infrastructure:

  • Which projects and repositories
  • What environments (dev, staging, prod)
  • Integration points

Implementing visibility doesn’t require blocking or restricting usage. Start with logging and analytics to understand actual patterns before defining policies.

Level 2: Policy

Policies define acceptable use. Good policies are:

  • Specific enough to be actionable: “Don’t include sensitive data” is vague. “Never include database connection strings, API keys, or customer email addresses in prompts” is specific.
  • General enough to cover edge cases: You can’t enumerate every scenario. Provide principles as well as rules.
  • Aligned with existing standards: Don’t create parallel security processes. Extend existing data classification, access control, and compliance frameworks.

Key policy areas for Claude Code:

Data Classification:

  • What data categories exist in your organization?
  • Which categories can be included in Claude Code prompts?
  • What handling is required for each category?

Access Control:

  • Who can use Claude Code for what purposes?
  • What skills/capabilities require elevated permissions?
  • How are permissions granted and revoked?

Usage Boundaries:

  • What tasks are appropriate for Claude Code assistance?
  • What decisions require human review regardless of AI recommendation?
  • What outputs need approval before implementation?

Incident Response:

  • What constitutes a security incident involving Claude Code?
  • What’s the escalation path?
  • What remediation steps are required?

Level 3: Enforcement

Enforcement mechanisms implement policies. The goal isn’t to block developers—it’s to make compliant behavior the easy default.

Preventive Controls (stop bad things before they happen):

  • Pre-flight content scanning
  • Input validation and sanitization
  • Access control and authentication
  • Approved skill/prompt libraries

Detective Controls (identify bad things that happened):

  • Audit logging
  • Anomaly detection
  • Regular access reviews
  • Compliance monitoring

Corrective Controls (fix bad things after detection):

  • Automated alerting
  • Incident response procedures
  • User education and training
  • Policy refinement based on incidents

Technical Implementation

Let’s get specific about implementing governance controls for Claude Code.

Secret Scanning

The most critical control. Secrets in prompts create immediate security risk.

Pre-flight Scanning: Before any prompt is sent to Claude, scan for:

  • API keys and tokens (AWS, GCP, Azure patterns)
  • Database connection strings
  • SSH private keys
  • OAuth tokens and secrets
  • Hardcoded passwords
  • Environment variable patterns

Implementation approaches:

  • Hook into Claude Code CLI with pre-send validation
  • Proxy layer that intercepts and scans requests
  • IDE plugin that validates before send

Post-flight Scanning: Scan Claude Code outputs for:

  • Hardcoded credentials in generated code
  • Placeholder passwords that might be committed
  • Connection strings with real values
  • API keys in configuration examples

Response Actions:

  • Block send when secrets detected (with clear error message)
  • Auto-redact secrets before sending (with user notification)
  • Alert security team for review
  • Log incident for audit trail

Audit Logging

Comprehensive logging enables compliance demonstration and incident investigation.

What to Log:

{
  "timestamp": "2025-01-09T14:32:17Z",
  "user_id": "jsmith@company.com",
  "team": "platform-engineering",
  "session_id": "abc123",
  "skill_used": "code-review/typescript",
  "input_summary": {
    "type": "code_file",
    "language": "typescript",
    "lines": 247,
    "content_hash": "sha256:abc..."
  },
  "output_summary": {
    "type": "review_comments",
    "item_count": 8,
    "content_hash": "sha256:def..."
  },
  "tokens_used": 4521,
  "latency_ms": 2340,
  "policy_checks": {
    "secret_scan": "passed",
    "pii_scan": "passed",
    "content_classification": "internal"
  }
}

What NOT to Log (privacy considerations):

  • Full prompt content (may contain sensitive data)
  • Full output content (may contain PII)
  • Individual keystrokes or iterations
  • Personal notes or comments

Storage and Retention:

  • Encrypt audit logs at rest
  • Restrict access to security and compliance teams
  • Retain per your organization’s policy (typically 1-7 years)
  • Enable efficient querying for investigations

Access Control

Control who can use what capabilities.

Role-Based Access:

  • Developer: Standard skills library, non-production contexts
  • Senior Engineer: Extended skills, production read access
  • Platform Team: Full skills, production write capabilities
  • Security Team: Audit access, policy management

Skill-Level Permissions:

  • Public Skills: Available to all authenticated users
  • Team Skills: Restricted to specific teams
  • Privileged Skills: Require additional approval
  • Experimental: Personal sandbox, no production access

Context-Based Rules:

  • Production environment requires senior approval
  • Customer data requires data handling certification
  • Financial systems require compliance training
  • After-hours usage may have additional review

Content Classification

Automatically classify content to apply appropriate handling.

Classification Levels:

  • Public: Can be freely shared, no restrictions
  • Internal: Company confidential, standard handling
  • Confidential: Restricted access, enhanced logging
  • Restricted: Requires explicit approval, full audit

Classification Signals:

  • File paths and repository names
  • Content patterns (customer IDs, financial data)
  • User-provided tags
  • Historical classification of similar content

Automated Actions by Classification:

  • Public: Standard processing
  • Internal: Standard logging
  • Confidential: Enhanced logging, manager notification
  • Restricted: Manual review required, security alert

Compliance Mapping

For regulated industries, Claude Code governance must integrate with existing compliance frameworks.

GDPR Considerations

If your organization processes EU personal data:

Data Processing:

  • Claude Code usage may constitute data processing
  • Ensure appropriate legal basis (legitimate interest, consent)
  • Document processing activities in your DPIA
  • Consider data minimization in prompt design

Rights Compliance:

  • Can you respond to access requests about AI processing?
  • Is deletion possible if data sent to Claude?
  • How do you demonstrate compliance to regulators?

Recommendations:

  • Minimize PII in prompts through design
  • Document AI processing in privacy policy
  • Include Claude Code in your processing records
  • Train developers on GDPR-compliant usage

SOC2 Alignment

For organizations pursuing or maintaining SOC2:

Security (CC6.0):

  • Access control: Align with CC6.1-6.8
  • System boundaries: Include Claude Code in scope
  • Vendor management: Document Anthropic as sub-processor

Availability (A1.0):

  • Dependency mapping: Claude Code as critical service
  • Backup procedures: Fallback for Claude Code unavailability
  • Incident response: Include AI-related scenarios

Confidentiality (C1.0):

  • Data classification: Extend to AI-processed data
  • Access control: Limit Claude Code to classified needs
  • Encryption: Ensure transit encryption for prompts

Privacy (P1.0):

  • Notice: Inform about AI processing
  • Choice: Allow opt-out where appropriate
  • Collection: Minimize data in prompts

Industry-Specific

Healthcare (HIPAA):

  • Never include PHI in Claude Code prompts
  • Document BAA considerations with Anthropic
  • Implement strict access controls for healthcare data
  • Train staff on HIPAA-compliant AI usage

Financial Services (PCI-DSS, SOX):

  • Exclude cardholder data from prompts
  • Document controls for SOX compliance
  • Implement separation of duties
  • Maintain audit trails for regulators

Governance Maturity Model

Organizations typically progress through governance maturity levels:

Level 1: Ad Hoc

Characteristics:

  • Individual developers use Claude Code with personal judgment
  • No centralized visibility or policy
  • Security relies on developer awareness
  • Compliance status unknown

Risks: High exposure, no audit trail, potential compliance gaps

Level 2: Documented

Characteristics:

  • Written policies for Claude Code usage
  • Training materials available
  • Manual compliance processes
  • Basic usage tracking

Risks: Policies may not be followed, limited enforcement

Level 3: Managed

Characteristics:

  • Automated visibility and logging
  • Centralized skill libraries
  • Secret scanning implemented
  • Regular compliance reviews

Risks: May miss edge cases, reactive rather than proactive

Level 4: Measured

Characteristics:

  • Comprehensive metrics and dashboards
  • Continuous compliance monitoring
  • Automated policy enforcement
  • Regular governance reviews

Risks: May become overly restrictive, false positives

Level 5: Optimized

Characteristics:

  • Governance enables rather than restricts
  • Continuous improvement based on data
  • Balanced security and productivity
  • Industry-leading practices

Risks: Requires ongoing investment to maintain

Getting Started

Ready to implement AI governance for your Claude Code deployment?

  1. Start with visibility: Implement basic logging before policies
  2. Inventory current usage: Understand what’s happening today
  3. Map to existing frameworks: Don’t create parallel processes
  4. Implement critical controls first: Secret scanning is non-negotiable
  5. Iterate based on data: Let actual usage inform policy refinement

For organizations needing structured implementation support, learn about our team consulting services or read our technical guide on building a Claude Code plugin architecture.

This article is part of our governance series for engineering teams. For implementation details, see Claude Code Plugin Architecture Guide. For prompt standardization, explore Prompt Templates for Development Teams.

Tags

AI governance security compliance Claude Code enterprise GDPR SOC2