Docker-only sandboxing limits the blast radius of AI agent failures but does not prevent harmful actions within the container. SafeClaw by Authensor adds the missing layer: deny-by-default action gating that controls what your agent can do inside the sandbox, not just where it can do it. Install it with npx @authensor/safeclaw to upgrade from container isolation to complete agent safety.

What Docker Gives You (and What It Does Not)

Docker containers provide valuable isolation:

• File system isolation: The agent cannot access the host file system directly
• Network isolation: Network policies can restrict external access
• Resource limits: CPU, memory, and disk constraints prevent runaway consumption
• Process isolation: The agent cannot interfere with host processes

• An agent can still delete all files inside the container, destroying its working directory and any mounted volumes
• An agent can still exfiltrate data through any allowed network endpoint
• An agent can still execute arbitrary shell commands within the container
• An agent can still consume all allocated resources without granular control
• An agent can still write to mounted volumes that may contain shared data
• There is no action-level audit trail of what the agent actually did

The Layered Security Model

Docker and SafeClaw are complementary, not competing. The strongest safety posture uses both:

[ Agent Logic ]
      |
[ SafeClaw: Action-level gating ]  <-- What can the agent do?
      |
[ Docker: Container isolation ]     <-- Where can the agent operate?
      |
[ Host System ]

SafeClaw gates every action request before execution. Docker contains the execution environment. Together, they provide defense in depth.

Step-by-Step Upgrade

Step 1: Install SafeClaw Inside Your Container

Add SafeClaw to your Dockerfile or install it at runtime:

# In your Dockerfile
RUN npx @authensor/safeclaw

Or install at runtime:

npx @authensor/safeclaw

Step 2: Audit Your Container's Action Surface

Run SafeClaw in simulation mode inside the container to observe what your agent actually does:

safeclaw.init({
  mode: 'simulation',
  audit: true
});

You may discover that your agent performs actions you did not expect, actions that Docker alone does not prevent.

Step 3: Define Deny-by-Default Policies

Based on the simulation audit, write policies for what the agent should be allowed to do within the container:

rules:
  - action: "file:read"
    path: "/workspace/**"
    effect: "allow"
  - action: "file:write"
    path: "/workspace/output/**"
    effect: "allow"
  - action: "shell:execute"
    command: "python *.py"
    effect: "allow"
  - action: "network:request"
    host: "api.approved-service.com"
    effect: "allow"
  # Everything else denied by default

Step 4: Correlate Docker and SafeClaw Policies

Ensure your SafeClaw policies and Docker configuration are aligned:

| Layer | Control | Example |
|---|---|---|
| Docker network | Which hosts are reachable | Only api.example.com on port 443 |
| SafeClaw network | Which requests are permitted | Only GET requests to /api/data |
| Docker volumes | Which paths are mounted | Only /workspace mounted read-write |
| SafeClaw file | Which file operations are allowed | Only writes to /workspace/output/ |

SafeClaw provides granular control within the boundaries Docker establishes.

Step 5: Enable Enforcement and Audit

Switch to enforcement mode with hash-chained audit logging:

safeclaw.init({
  mode: 'enforce',
  policy: './safeclaw-policy.yaml',
  audit: true
});

Now your agent operates in a container with both environmental isolation (Docker) and behavioral control (SafeClaw). The audit trail records every action for compliance and debugging.

Real-World Example: The Mounted Volume Problem

A common Docker pattern mounts a host directory as a volume for the agent to work with. Docker provides no control over what the agent does inside that volume. Without SafeClaw:

• Agent can rm -rf /workspace/* and destroy all project files
• Agent can read .env files containing secrets
• Agent can modify package.json to add malicious dependencies

Performance Considerations

SafeClaw adds negligible overhead inside a container. The policy engine evaluates action requests in memory with no external calls. For containerized deployments where latency matters, SafeClaw's zero-dependency, in-process architecture is ideal.

Related reading:

• Migration Guide: Adding SafeClaw to an Existing AI Agent


• SafeClaw Compared: How It Stacks Up Against Every Alternative


• SafeClaw Features: Everything You Get Out of the Box


• The Complete Guide to AI Agent Safety (2026)