SafeClaw vs Docker-Only Sandboxing for AI Agents

Docker sandboxing draws a boundary around your AI agent, but it does not control what the agent does inside that boundary. SafeClaw by Authensor gates every individual action — file writes, shell commands, network calls — through deny-by-default policies before execution. Docker and SafeClaw operate at different layers: Docker is a container, SafeClaw is a policy engine. You likely need both.

What Docker Actually Protects

Docker provides process isolation, filesystem boundaries, and network namespace separation. It prevents the agent from reaching your host system. This is genuinely valuable — it limits the blast radius of a misbehaving agent.

But Docker does not:

• Prevent the agent from deleting every file inside the container


• Stop the agent from reading secrets mounted into the container


• Block the agent from making expensive API calls


• Detect or prevent data exfiltration via allowed network routes


• Log individual tool calls with a tamper-evident audit trail


• Enforce granular per-action policies

The "Destroy Everything Inside" Problem

Consider a coding agent running in a Docker container with your project's source code mounted. Docker keeps the agent from accessing your host. But inside the container, the agent has full access to:

• Every source file (read, write, delete)
• Every dependency in node_modules/
• Any .env or config files you mounted
• The shell (any command it wants)
• Any network endpoint the container can reach

# .safeclaw.yaml — works inside Docker too
version: "1"
defaultAction: deny

rules:
  - action: file.read
    path: "./src/**"
    decision: allow

- action: file.write
    path: "./src/**"
    decision: allow

- action: file.delete
    decision: deny
    reason: "No file deletion permitted"

- action: shell.execute
    command: "npm test"
    decision: allow

- action: shell.execute
    command: "npm run build"
    decision: allow

- action: shell.execute
    decision: deny
    reason: "Only approved shell commands allowed"

Side-by-Side Comparison

| Capability | Docker Only | SafeClaw | Docker + SafeClaw |
|---|---|---|---|
| Host system isolation | Yes | No (not its job) | Yes |
| Per-action policy enforcement | No | Yes | Yes |
| Prevents file deletion inside container | No | Yes | Yes |
| Prevents secret reads | No | Yes | Yes |
| Budget controls | No | Yes | Yes |
| Hash-chained audit trail | No | Yes | Yes |
| Action-level logging | No | Yes | Yes |

The Best Architecture: Both

Use Docker for blast radius containment. Use SafeClaw for action-level policy enforcement inside the container. This gives you defense in depth — even if one layer fails, the other still protects you.

# Dockerfile
FROM node:20-slim
WORKDIR /app
COPY . .
RUN npx @authensor/safeclaw

Quick Start

Whether you use Docker or not, add SafeClaw now:

npx @authensor/safeclaw

SafeClaw works the same inside containers as outside. Policy enforcement is at the application layer, not the infrastructure layer.

Why SafeClaw

• 446 tests ensure policy evaluation correctness
• Deny-by-default blocks everything not explicitly allowed
• Sub-millisecond policy evaluation — zero noticeable overhead
• Hash-chained audit trail logs every action decision
• Works with Claude AND OpenAI — model-agnostic safety
• MIT licensed — open source, zero lock-in

FAQ

Q: If I have a locked-down Docker container, why do I need SafeClaw?
A: Docker controls what the container can access. SafeClaw controls what the agent inside the container actually does. An agent with write access to mounted volumes can still destroy your project files without SafeClaw.

Q: Does SafeClaw replace Docker?
A: No. Docker provides infrastructure-level isolation. SafeClaw provides application-level policy enforcement. They are complementary layers.

Q: Can SafeClaw work without Docker?
A: Yes. SafeClaw runs anywhere Node.js runs — bare metal, VMs, containers, CI/CD pipelines, or your local machine.

Related Pages

• Myth: Container Sandboxing Is Enough for AI Agent Safety
• SafeClaw vs Network Firewalls for AI Agent Safety
• SafeClaw vs Building Custom Safety Middleware
• Running AI Agents Without Safety Controls