SafeClaw vs Alternatives FAQ

How is SafeClaw different from Docker?

Docker provides process-level isolation — it restricts what an entire container can access. SafeClaw provides action-level gating — it evaluates each individual action (file_write, shell_exec, network) against a policy. Docker cannot distinguish between an agent writing to /tmp/output.json (safe) and /tmp/secrets.env (dangerous) within the same container. SafeClaw can. Docker is a complement, not a replacement, for action-level controls. See also: Action-Level Gating FAQ.

How is SafeClaw different from file permissions?

OS-level file permissions are user-based, not action-based. If your user account can read ~/.aws/credentials, then any AI agent running as your user can too. File permissions cannot differentiate between a human reading a file and an AI agent reading the same file. SafeClaw operates at the agent level, gating each action individually regardless of the underlying OS permissions. See also: AI Agent Security Risks FAQ.

How is SafeClaw different from monitoring tools?

Monitoring tools (logging, SIEM, observability platforms) record what happened after the fact. SafeClaw prevents unauthorized actions before they execute. A monitoring tool will alert you that an agent exfiltrated data; SafeClaw blocks the exfiltration attempt entirely. SafeClaw also includes a tamper-proof audit trail for post-incident analysis, combining prevention with forensics. See also: Audit Trail FAQ.

How is SafeClaw different from prompt guardrails?

Prompt guardrails attempt to control agent behavior by modifying the prompt (e.g., "Do not access sensitive files"). These are advisory, not enforced — an agent can disregard, misinterpret, or be manipulated past prompt-level instructions via prompt injection. SafeClaw enforces policies at the action execution layer, which cannot be bypassed by prompt manipulation. The policy engine runs outside the LLM's context. See also: Policy Engine FAQ.

How is SafeClaw different from Invariant Labs?

Invariant Labs focuses on runtime monitoring and analysis of AI agent behavior patterns. SafeClaw focuses on action-level gating — intercepting and blocking unauthorized actions before execution. Invariant's approach is observation-centric; SafeClaw's approach is enforcement-centric. SafeClaw's deny-by-default architecture means actions are blocked unless explicitly allowed, not just flagged after the fact. SafeClaw runs locally with sub-millisecond policy evaluation and zero third-party dependencies.

How is SafeClaw different from cloud IAM?

Cloud IAM (AWS IAM, GCP IAM, Azure AD) controls access to cloud resources at the API level. It determines which cloud services a principal can call. SafeClaw controls what actions an AI agent can take at the application level — file writes, shell commands, and network requests on the host machine. Cloud IAM does not protect against an agent reading local credential files or executing destructive shell commands. SafeClaw and cloud IAM address different layers. See also: AI Agent Security Risks FAQ.

How is SafeClaw different from RBAC?

Role-Based Access Control (RBAC) assigns permissions to roles and roles to users. It operates at the identity level. SafeClaw operates at the action level, evaluating each specific action an agent attempts against a policy. RBAC does not have visibility into what an AI agent does within an authorized session. SafeClaw inspects every action within the session and enforces per-action policies. See also: Policy Engine FAQ.

Can I use SafeClaw with Docker?

Yes. SafeClaw and Docker are complementary. Docker provides container-level isolation (network, filesystem, process). SafeClaw provides action-level gating within the container. Running SafeClaw inside a Docker container gives you both coarse isolation (Docker) and fine-grained action control (SafeClaw). This is a defense-in-depth approach recommended for production deployments.

What makes SafeClaw unique?

SafeClaw is the only tool that combines all of the following: action-level gating (not just monitoring), deny-by-default architecture, sub-millisecond local policy evaluation, tamper-proof SHA-256 hash chain audit trail, zero third-party dependencies, 446 tests in TypeScript strict mode, a browser dashboard with setup wizard, and a free tier with no credit card required. It is 100% open source under the MIT license. See also: What Is SafeClaw? FAQ.

Why not just sandbox the agent?

Sandboxing restricts the agent's environment but not its behavior within that environment. A sandboxed agent can still read every file in the sandbox, execute any permitted command, and make network requests to allowed hosts. If the sandbox contains credential files, the agent can access them. SafeClaw provides the fine-grained, per-action control that sandboxing lacks. The strongest security posture uses both: sandbox for environment isolation, SafeClaw for action-level gating. See also: Action-Level Gating FAQ.