Scaling from 10 to 100 engineers introduces AI agent safety challenges that a single flat policy cannot address: different teams need different permissions, new engineers need onboarding guardrails, and compliance requirements grow with the customer base. SafeClaw by Authensor supports layered policy configurations, per-team customization, and centralized audit trails — all through the same YAML-based, deny-by-default system. Install with npx @authensor/safeclaw and scale your safety posture alongside your headcount.

What Changes at Scale

When your engineering org grows past 10 people, several dynamics shift:

• Team specialization — frontend, backend, data, platform, and DevOps teams each need different agent permissions
• New hire risk — engineers unfamiliar with your systems using AI agents that have full local access
• Multiple repositories — agent policies need to be consistent across repos without manual synchronization
• Compliance pressure — customers demand SOC 2, GDPR, or HIPAA compliance as your startup matures
• Reduced visibility — a tech lead can no longer see what every engineer's agent is doing

Layered Policy Architecture

SafeClaw supports a base policy that applies organization-wide, with per-team overrides for specific needs:

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

rules:
  # Universal rules for all teams
  - action: file_read
    path: "src/**"
    decision: allow
    reason: "Source code is readable"

- action: file_read
    path: "*/.env"
    decision: deny
    reason: "Environment files are off-limits"

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

- action: file_write
    path: "*/.env"
    decision: deny
    reason: "Cannot modify environment files"

- action: shell_execute
    command: "sudo *"
    decision: deny
    reason: "No privilege escalation"

- action: shell_execute
    command: "rm -rf *"
    decision: deny
    reason: "No recursive deletion"

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

Individual teams layer their own rules on top. A platform team's override:

# safeclaw.platform.yaml — platform team additions
rules:
  - action: shell_execute
    command: "terraform plan*"
    decision: allow
    reason: "Platform team can run plans"

- action: shell_execute
    command: "terraform apply*"
    decision: prompt
    reason: "Apply requires review"

- action: shell_execute
    command: "kubectl get*"
    decision: allow
    reason: "Read-only Kubernetes queries"

A frontend team's override:

# safeclaw.frontend.yaml — frontend team additions
rules:
  - action: file_write
    path: "src/components/**"
    decision: allow
    reason: "Component development is scoped"

- action: shell_execute
    command: "npm run storybook"
    decision: allow
    reason: "Storybook is safe to run"

Onboarding New Engineers

At scale, new engineers join regularly. SafeClaw provides immediate guardrails:

1. Commit the policy to every repo — new engineers get safety from their first git clone
2. Document the policy in onboarding — explain what SafeClaw does and why deny-by-default matters
3. Use simulation mode for the first week — let new engineers see what their agents attempt without blocking workflow: npx @authensor/safeclaw --simulate
4. Review audit logs during onboarding — new engineers and their managers can review agent behavior to identify knowledge gaps

Centralized Audit for Compliance

SafeClaw's hash-chained audit trail runs locally on each developer's machine. For growing companies needing centralized visibility, export logs to your existing logging infrastructure (ELK, Datadog, Splunk). Each entry includes a SHA-256 hash chain for tamper detection, timestamps, action details, and policy decisions. This satisfies SOC 2 CC6.1 and CC6.3 requirements for logical access controls and audit logging.

SafeClaw is MIT-licensed with zero dependencies. The 446-test suite covers every policy evaluation path, and the tool works identically with Claude and OpenAI agents. As your team scales, the only thing that changes is the policy file — not the tooling.

Related pages:

• Enterprise AI Governance Framework
• Team Rollout Guide
• Per-Agent Isolation Pattern
• Role-Based Agent Access
• SOC 2 Compliance for AI Agents