Role-Based Access Control for AI Agents

Role-based access control (RBAC) for AI agents assigns different permission sets based on the operator's role, the agent's purpose, or the environment context. SafeClaw by Authensor implements this through composable YAML policies where each role maps to a distinct set of allowed actions, evaluated with deny-by-default semantics. A junior developer's agent gets read-only source access while a senior engineer's agent can execute deployments — all enforced before any action runs.

Quick Start

npx @authensor/safeclaw

Defining Roles as Policy Files

Create separate policy files per role:

.safeclaw/
  roles/
    junior-developer.yaml
    senior-developer.yaml
    team-lead.yaml
    admin.yaml
    ci-agent.yaml

Junior Developer Policy

version: "1.0"
description: "Junior developer — read source, write tests only"

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

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

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

- action: shell.execute
    command: "npm run lint"
    effect: allow

- action: "*"
    effect: deny
    reason: "Junior role: restricted by default"

Senior Developer Policy

version: "1.0"
description: "Senior developer — full source access, controlled deployments"

rules:
  - action: file.read
    path: "**"
    effect: allow
    excludePaths:
      - ".env*"
      - "secrets/**"

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

- action: shell.execute
    command: "npm *"
    effect: allow

- action: shell.execute
    command: "docker build *"
    effect: allow

- action: network.request
    domain: "registry.npmjs.org"
    effect: allow

- action: "*"
    effect: deny
    reason: "Senior role: explicit permissions only"

Admin / Platform Engineer Policy

version: "1.0"
description: "Admin — infrastructure management with audit logging"

rules:
  - action: file.read
    path: "**"
    effect: allow

- action: file.write
    path: "infrastructure/**"
    effect: allow

- action: shell.execute
    command: "terraform *"
    effect: allow

- action: shell.execute
    command: "kubectl *"
    effect: allow

- action: network.request
    domain: "*.amazonaws.com"
    effect: allow

- action: shell.execute
    command: "rm -rf *"
    effect: deny
    reason: "Destructive operations blocked even for admins"

- action: "*"
    effect: deny
    reason: "Admin role: explicit permissions only"

Assigning Roles

Configure which policy applies via the SafeClaw config:

# .safeclaw/config.yaml
activePolicy: "roles/senior-developer.yaml"

For CI/CD environments, set the role via environment variable:

SAFECLAW_POLICY=roles/ci-agent.yaml npx @authensor/safeclaw

Role Escalation Controls

SafeClaw prevents role escalation by design. An agent running under junior-developer.yaml cannot modify its own policy file because write access to .safeclaw/ is denied by default. Only operators with repository commit access can change role assignments.

To add explicit protection:

  - action: file.write
    path: ".safeclaw/**"
    effect: deny
    reason: "Agents cannot modify their own policies"

- action: file.read
    path: ".safeclaw/**"
    effect: deny
    reason: "Agents cannot read policy configurations"

Auditing Role-Based Decisions

Every action evaluation includes the active policy name in the audit log. Filter by role:

npx @authensor/safeclaw audit --filter policy=junior-developer --since "24h"

This reveals whether a role is too restrictive (high deny rate on legitimate actions) or too permissive (allows actions that should be scoped down).

Why SafeClaw

• 446 tests validate policy composition and first-match-wins evaluation
• Deny-by-default ensures no role inherits hidden permissions
• Sub-millisecond evaluation keeps agent workflows responsive regardless of policy complexity
• Hash-chained audit trail logs every decision with tamper-proof integrity
• Works with Claude AND OpenAI — role policies apply regardless of LLM provider
• MIT licensed — adopt RBAC patterns without vendor lock-in

See Also

• How to Set AI Agent Policies for Engineering Teams
• Zero Trust Architecture for AI Agents
• Permission Models for AI Agents
• Building an AI Governance Framework with SafeClaw