CTOs adopting AI agents across engineering teams need a governance framework that enforces safety without blocking productivity, produces audit evidence for compliance, and avoids vendor lock-in. SafeClaw by Authensor is an open-source, MIT-licensed tool that provides deny-by-default action gating, a tamper-proof hash-chained audit trail, and provider-agnostic support for both Claude and OpenAI agents. Install with npx @authensor/safeclaw and deploy a policy file that scales from one developer to hundreds.

The Governance Problem

AI agents are no longer experimental. Engineering teams use them for code generation, debugging, infrastructure management, and data analysis. Each of these agents operates with the permissions of the developer who launched it — meaning a single agent can access databases, read credentials, modify infrastructure, and push code to production.

Without governance, every AI agent is an unaudited, autonomous actor with full system access. The risks to the business are:

• Compliance violations — uncontrolled data access that breaches GDPR, HIPAA, SOC 2, or PCI DSS requirements
• Intellectual property exposure — agents reading proprietary code and sending it to third-party LLM providers
• Infrastructure incidents — agents making destructive changes to production systems
• Liability — no audit trail proving due diligence in controlling AI agent behavior

What SafeClaw Provides for Governance

SafeClaw operates at a level CTOs care about: policy-as-code that is version-controlled, auditable, and enforceable.

# safeclaw.yaml — organization-wide base policy
version: 1
default: deny

rules:
  - action: file_read
    path: "src/**"
    decision: allow
    reason: "Source code is readable by agents"

- action: file_write
    path: "src/**"
    decision: prompt
    reason: "All code writes require developer review"

- action: file_read
    path: "*/.env"
    decision: deny
    reason: "Environment secrets are never accessible"

- action: file_read
    path: "*/secret*"
    decision: deny
    reason: "Secret files are blocked"

- action: shell_execute
    command: "git push*"
    decision: prompt
    reason: "Pushes require explicit approval"

- action: network_request
    destination: "*"
    decision: deny
    reason: "No outbound network access"

This policy lives in version control alongside the codebase. Changes to policy go through the same PR review process as code changes. The default: deny directive means new agent capabilities are blocked until explicitly authorized — the same principle as network firewall rules.

Compliance and Audit

SafeClaw's hash-chained audit trail produces a tamper-evident log of every agent action. Each log entry includes:

• Timestamp
• Action type (file_read, file_write, shell_execute, network_request)
• Target (file path, command, URL)
• Policy decision (allow, deny, prompt)
• Matching rule
• SHA-256 hash linking to previous entry

Scaling Across Teams

SafeClaw's policy file can be standardized across the organization and customized per team:

1. Base policy — organization-wide rules in a shared repository
2. Team overrides — team-specific rules layered on top for their unique needs
3. Simulation mode — test new policies without blocking agents using npx @authensor/safeclaw --simulate
4. CI enforcement — run SafeClaw in CI pipelines to ensure agents in automated workflows respect policy

Related pages:

• Enterprise AI Governance Framework
• SOC 2 Compliance for AI Agents
• AI Agent Safety Maturity Model
• Policy-as-Code Pattern