The Pre-Commit Secrets Policy Your Developers Will Actually Use

Your team committed 29 million new hardcoded secrets to public repositories in 2025—a 34% increase from the previous year. AI services contributed to 81% more leaks year over year, and internal repositories were 6x more likely to leak secrets than public ones.

You need a secrets policy that prevents credentials from reaching your repositories. Here's a template you can deploy this week.

What This Template Does

This policy establishes automated pre-commit controls for secrets detection, with specific requirements for AI service credentials, internal repository scanning, and non-code environments like Slack and Confluence. It's designed to catch secrets before they spread.

The template addresses three critical gaps:

• AI service credentials (OpenAI keys, Anthropic tokens, Model Context Protocol configurations)
• Internal repository scanning (where 6x more leaks occur)
• Non-code collaboration tools that traditional scanners miss

Prerequisites

Before implementing this policy, ensure you have:

Technical infrastructure:

• Git hooks capability on developer workstations (Git 2.9 or later)
• CI/CD pipeline with policy enforcement gates
• Secrets scanning tool (options: detect-secrets, Gitleaks, TruffleHog)
• Centralized secrets management system (HashiCorp Vault, AWS Secrets Manager, or equivalent)

Organizational readiness:

• Developer workstation management policy (to deploy hooks)
• Incident response process for detected secrets
• Secrets rotation procedures
• Executive sponsor for enforcement decisions

Compliance mapping:

• PCI DSS v4.0.1 Requirement 8.3.2 (cryptographic key management)
• SOC 2 Type II CC6.1 (logical access controls)
• ISO 27001 Control 8.3 (information access restriction)

The Policy Template

# Secrets Management and Detection Policy
Version: 1.0
Effective Date: [INSERT DATE]
Owner: [CISO/Security Engineering Lead]

## 1. Scope

This policy applies to:
- All source code repositories (public and internal)
- Infrastructure-as-code configurations
- CI/CD pipeline definitions
- Documentation repositories
- AI service integrations and prompt libraries
- Collaboration tool content (Slack, Confluence, Notion)

## 2. Prohibited Credential Storage

The following MUST NOT be committed to any repository:

### 2.1 Traditional Credentials
- API keys and tokens
- Database connection strings with embedded passwords
- Private keys (SSH, TLS, GPG)
- OAuth client secrets
- Service account credentials
- Encryption keys

### 2.2 AI Service Credentials (High Priority)
- OpenAI API keys (sk-*, org-*)
- Anthropic API keys
- Google AI/Vertex credentials
- Azure OpenAI keys
- Model Context Protocol (MCP) server configurations with embedded tokens
- LLM infrastructure credentials (vector DB, embedding services)

### 2.3 Internal System Credentials
- LDAP/Active Directory service accounts
- Internal API authentication tokens
- VPN credentials
- Monitoring system API keys
- Internal tool passwords

## 3. Pre-Commit Controls (REQUIRED)

### 3.1 Developer Workstation Requirements
All developers MUST have pre-commit hooks installed that:
- Scan for secrets patterns before allowing commits
- Block commits containing detected secrets
- Log detection events to central security logging

Installation verification: Security team validates hook installation quarterly.

### 3.2 Scanning Configuration
Pre-commit scanners MUST detect:
- High-entropy strings (entropy > 4.5)
- Pattern matches for known credential formats
- AI service key patterns: `(sk-[a-zA-Z0-9]{48}|api[_-]?key.*['\"][a-zA-Z0-9]{32,})`
- MCP configuration files with inline credentials
- Cloud provider credential patterns (AWS, Azure, GCP)

### 3.3 Bypass Procedures
Developers MAY bypass pre-commit hooks only when:
- Committing test fixtures with fake credentials (marked with `# FAKE_CREDENTIAL`)
- Committing public key material (must be reviewed)
- Emergency production fixes (requires post-commit review within 4 hours)

All bypasses MUST be logged and reviewed weekly by the security team.

## 4. CI/CD Pipeline Gates (REQUIRED)

### 4.1 Build-Time Scanning
Every pipeline MUST include secrets scanning that:
- Runs before deployment to any environment
- Fails the build on detection (no warnings-only mode)
- Scans commit history for the current branch (last 10 commits minimum)
- Checks for secrets in environment variable definitions

### 4.2 Internal Repository Priority
Internal repositories require:
- Daily full-history scans (not just new commits)
- Scanning of non-code files (markdown, YAML, JSON)
- Alert routing to repository owner + security team

Rationale: Internal repos show 6x higher leak rates than public repos.

## 5. Non-Code Environment Controls

### 5.1 Collaboration Tools
Security team MUST implement:
- Automated scanning of Slack channels (public and private) for credential patterns
- Confluence/wiki page scanning for embedded secrets
- Jira ticket scanning for credentials in descriptions/comments
- Automated deletion or masking of detected credentials within 1 hour

### 5.2 AI Coding Assistants
Developers using AI coding assistants (GitHub Copilot, Cursor, etc.) MUST:
- Disable credential sharing in assistant settings
- Review all AI-generated code for embedded secrets before committing
- Use credential placeholders in prompts (e.g., `YOUR_API_KEY_HERE`)

## 6. Detection Response

### 6.1 Immediate Actions (Within 1 Hour)
When secrets are detected:
1. Revoke the exposed credential immediately
2. Generate new credential
3. Update all systems using the credential
4. Remove secret from repository history (BFG Repo-Cleaner or git-filter-repo)
5. Notify security team

### 6.2 Investigation Requirements
For each incident, document:
- How the secret was committed (direct commit, merge, AI-generated)
- Duration of exposure
- Systems accessed by the credential
- Evidence of unauthorized access
- Root cause (training gap, tool misconfiguration, etc.)

### 6.3 Repeat Violations
Developers with 3+ violations in 90 days:
- Mandatory secrets management training
- Manager notification
- Potential workstation access restriction pending training completion

## 7. Approved Alternatives

Instead of committing secrets, use:

### 7.1 Local Development
- Environment variables loaded from `.env` files (add `.env` to `.gitignore`)
- Local secrets management tools (direnv, dotenv)
- Developer-specific configuration files (not committed)

### 7.2 CI/CD Environments
- Pipeline secrets management (GitHub Secrets, GitLab CI/CD variables)
- Cloud provider secrets managers (AWS Secrets Manager, Azure Key Vault)
- HashiCorp Vault with dynamic credentials

### 7.3 Production
- Centralized secrets management system (REQUIRED)
- Secrets rotation every 90 days (minimum)
- Audit logging of all secrets access

## 8. Compliance Mapping

This policy satisfies:
- [PCI DSS](https://www.pcisecuritystandards.org/) v4.0.1 Requirement 8.3.2: Secure cryptographic key management
- [SOC 2](https://www.aicpa-cima.com/topic/audit-assurance/audit-and-assurance-greater-than-soc-2) Type II CC6.1: Logical and physical access controls
- [ISO 27001](https://www.iso.org/standard/27001) Control 8.3: Information access restriction

## 9. Policy Exceptions

Exceptions require:
- Written justification from requesting team
- Compensating controls documentation
- CISO approval
- Annual renewal

## 10. Review Schedule

This policy is reviewed:
- Annually (minimum)
- After any secrets exposure incident affecting production
- When new AI services are adopted
- When compliance requirements change

How to Customize It

For your organization size:

• Under 100 developers: Remove quarterly hook verification; do annual reviews instead
• Over 1000 developers: Add automated hook deployment via MDM/configuration management
• Distributed teams: Add VPN-based repository access requirements

For your tech stack:

• Add your specific cloud provider patterns (Oracle Cloud, IBM Cloud, etc.)
• Include proprietary internal service credential patterns
• Add your CI/CD platform's specific secrets syntax

For your risk tolerance:

• Higher risk tolerance: Allow 5 violations before mandatory training (instead of 3)
• Lower risk tolerance: Require security team approval for ALL pre-commit hook bypasses
• Regulated industries: Add specific compliance section numbers for your frameworks (HIPAA, GDPR, etc.)

For AI adoption stage:

• Early AI adoption: Focus Section 2.2 on your current AI vendors only
• Heavy AI usage: Add scanning for prompt injection attempts and PII in prompts
• MCP implementations: Add specific MCP server configuration scanning rules

Validation Steps

Week 1: Technical validation

1. Install pre-commit hooks on 5 test developer workstations
2. Attempt to commit a test secret (use a fake API key: sk-test1234567890abcdefghijklmnopqrstuvwxyz123456)
3. Verify the commit is blocked
4. Check that the detection event appears in your security logging system
5. Test the bypass procedure with a fake credential marked # FAKE_CREDENTIAL

Week 2: Pipeline validation

1. Add secrets scanning to one non-production pipeline
2. Introduce a test secret in a feature branch
3. Verify the build fails
4. Confirm security team receives the alert
5. Test the incident response workflow (revocation, rotation, history cleanup)

Week 3: Internal repository validation

1. Run full-history scan on 3 internal repositories
2. Document all findings (expect to find secrets—internal repos leak 6x more)
3. Follow the response procedure for each finding
4. Measure time-to-revocation (target: under 1 hour)

Week 4: Non-code environment validation

1. Post a test credential in a Slack channel
2. Verify automated detection and masking
3. Test Confluence scanning on 5 high-traffic pages
4. Review AI coding assistant settings across your team

Month 2: Policy enforcement

1. Deploy pre-commit hooks to all developer workstations
2. Enable pipeline gates in production CI/CD
3. Begin weekly bypass log reviews
4. Start tracking repeat violations

Quarterly review metrics:

• Secrets detected pre-commit vs. in CI/CD (goal: 90%+ caught pre-commit)
• Time-to-revocation (goal: under 1 hour)
• Repeat violation rate (goal: under 5% of developers)
• Internal repository scan coverage (goal: 100%)
• AI service credential leaks (track trend—should decrease)

If your time-to-revocation exceeds 4 hours, your incident response process needs work before you can effectively enforce this policy. Fix that first.