Permission Models for AI Agents: A Technical Comparison

Permission models determine what an AI agent can do and how decisions are made when no explicit rule exists. The four dominant models are allow-by-default, deny-by-default, capability-based, and role-based access control (RBAC). SafeClaw by Authensor implements deny-by-default with first-match-wins evaluation, which security professionals broadly regard as the safest model for autonomous agents because it guarantees that any action not explicitly authorized is blocked before execution.

Quick Start

npx @authensor/safeclaw

The Four Models

1. Allow-by-Default

How it works: Every action is permitted unless a rule explicitly denies it.

# Allow-by-default pseudocode
rules:
  - action: shell.execute
    command: "rm -rf /"
    effect: deny
  # Everything else: allowed

Problems for AI agents:

• New action types (tools, APIs, file paths) are automatically permitted


• You must anticipate every dangerous action and write a deny rule for it


• A single missed deny rule creates a vulnerability


• Agents routinely discover new attack surfaces that no deny rule covers

2. Deny-by-Default

How it works: Every action is blocked unless a rule explicitly allows it.

# SafeClaw deny-by-default
rules:
  - action: file.read
    path: "src/**"
    effect: allow

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

- action: "*"
    effect: deny
    reason: "Default deny: not explicitly allowed"

Advantages for AI agents:

• New action types are automatically blocked


• You only need to enumerate what the agent should do, not what it should not do


• The allow list is typically 10-20 rules; the deny surface is infinite


• Policy is auditable: the allow list is the complete capability set

3. Capability-Based

How it works: Agents receive capability tokens that grant specific permissions. To perform an action, the agent must present the corresponding token.

Agent receives: [cap:file.read:src/**, cap:shell.execute:npm test]
Agent attempts: file.write src/app.ts → No capability → DENIED

Advantages:

• Fine-grained, transferable permissions


• Good for multi-agent delegation

• Complex token management


• Capability leakage if tokens are passed between agents


• Requires a token authority and revocation system

4. Role-Based Access Control (RBAC)

How it works: Agents are assigned roles, and roles map to permission sets.

roles:
  junior-agent:
    - file.read: "src/**"
    - shell.execute: "npm test"
  senior-agent:
    - file.read: "**"
    - file.write: "src/**"
    - shell.execute: "npm *"

Advantages:

• Familiar to enterprise security teams


• Easy to reason about at the organizational level

• Role explosion in complex environments


• Roles often become too broad over time


• Does not enforce deny-by-default unless explicitly designed to

Comparison Matrix

| Property | Allow-Default | Deny-Default | Capability | RBAC |
|---|---|---|---|---|
| Safety for unknown actions | None | Full | Partial | Depends |
| Policy complexity | High (deny list) | Low (allow list) | Medium (tokens) | Medium (roles) |
| Audit clarity | Low | High | Medium | Medium |
| Scalability | Poor | Good | Good | Good |
| Fail-safe behavior | Fail-open | Fail-closed | Fail-closed | Depends |

First-Match-Wins Evaluation

SafeClaw evaluates rules top-to-bottom and applies the first matching rule:

rules:
  # Rule 1: Specific deny
  - action: file.read
    path: "src/config/secrets.yaml"
    effect: deny

# Rule 2: Broader allow
  - action: file.read
    path: "src/**"
    effect: allow

# Rule 3: Catch-all deny
  - action: "*"
    effect: deny

For file.read src/config/secrets.yaml: Rule 1 matches first, result is deny.
For file.read src/app.ts: Rule 1 does not match, Rule 2 matches, result is allow.
For network.request example.com: Rules 1-2 do not match, Rule 3 matches, result is deny.

This model is predictable, auditable, and mirrors firewall rule evaluation — familiar to security engineers.

Implementation Recommendation

For any AI agent deployment:

1. Start with deny-by-default (SafeClaw baseline)
2. Add allow rules for the specific actions the agent needs
3. Layer RBAC on top by assigning different policy files to different roles
4. Use the audit trail to identify missing allow rules (legitimate denies) and tighten overly broad allows
5. Review policies quarterly

version: "1.0"
description: "Production agent — deny-by-default with RBAC"

rules:
  - action: file.read
    path: "src/**"
    effect: allow
  - action: shell.execute
    command: "npm test"
    effect: allow
  - action: "*"
    effect: deny
    reason: "Deny-by-default baseline"

Why SafeClaw

• 446 tests validate first-match-wins evaluation across every edge case
• Deny-by-default is the safest model for autonomous agents
• Sub-millisecond evaluation makes the permission model invisible to users
• Hash-chained audit trail logs every evaluation for security review
• Works with Claude AND OpenAI — permission model is provider-agnostic
• MIT licensed — adopt the safest permission model without cost

See Also

• Role-Based Access Control for AI Agents
• Zero Trust Architecture for AI Agents
• How to Set AI Agent Policies for Engineering Teams
• Building an AI Governance Framework with SafeClaw