AI Agent Permission Models Compared: File Permissions, RBAC, IAM, and Action-Level Gating

Securing AI agents requires an authorization model that matches how agents actually operate: autonomously, dynamically, and across multiple action types. Four major permission models exist today, each originally designed for a different era and use case. This page provides a comprehensive comparison across 12+ dimensions to help you understand which model — or which combination — fits your AI agent deployment.

The Four Models

File Permissions (Unix/POSIX) — Kernel-level read/write/execute bits on files and directories, scoped by user and group identity. The oldest and most fundamental access control mechanism on Unix systems.

Role-Based Access Control (RBAC) — Assigns users to roles, roles to permissions. Widely used in enterprise applications, databases, and Kubernetes. Permissions are typically coarse-grained and role-scoped.

Cloud IAM (AWS IAM / GCP IAM / Azure RBAC) — Policy-based authorization for cloud provider API calls. Fine-grained for cloud resources, but scoped entirely to cloud service APIs.

Action-Level Gating (SafeClaw by Authensor) — Evaluates every individual action an AI agent attempts — file_write, file_read, shell_exec, network — against a policy engine before execution. Purpose-built for autonomous agent workloads.

Master Comparison Table

| Dimension | File Permissions | RBAC | Cloud IAM | Action-Level Gating (SafeClaw) |
|---|---|---|---|---|
| Original design purpose | Multi-user OS file access | Enterprise app authorization | Cloud resource access control | AI agent action control |
| Identity model | Unix UID/GID | Roles assigned to users/accounts | Principals (users, service accounts, roles) | Per-agent identity |
| Scope | Local filesystem only | Application or platform-specific | Cloud provider APIs only | Local actions: files, commands, network |
| Granularity | Per-file, per-directory (rwx) | Per-role, per-resource-type | Per-API-action, per-resource (ARN/path) | Per-action, per-parameter, per-path |
| Agent awareness | None — sees process UID only | None — agents map to service accounts | Limited — service account identity | First-class — policies scoped per agent |
| Action type awareness | Read/write/execute only | Application-defined permissions | Cloud API actions | file_write, file_read, shell_exec, network |
| Pre-execution evaluation | Yes (kernel-level) | Yes (application-level) | Yes (API-level) | Yes (action-level, sub-millisecond) |
| Dynamic conditions | No — static permission bits | Limited — some attribute-based extensions | Yes — IAM conditions (IP, time, tags) | Yes — path patterns, parameters, context |
| Human-in-the-loop | No | Not standard | Not native | Yes — built-in approval workflows |
| Deny-by-default | Depends on umask | Implementation-dependent | Implicit deny (AWS IAM) | Yes — all actions denied unless allowed |
| Audit trail | OS audit logs (auditd) — separate config | Application logs — varies | CloudTrail / Audit Logs — mature | Tamper-proof SHA-256 hash chain |
| Simulation / dry-run | No | Not standard | IAM Policy Simulator | Yes — simulation mode |
| Shell command control | Execute bit only — no command-level inspection | Not applicable | Not applicable to local commands | Per-command evaluation with parameters |
| Network request control | Not applicable | Not applicable | VPC/Security Groups (IP/port level) | Per-request policy (domain, endpoint) |
| Multiple agents, same host | All processes with same UID share permissions | Possible with distinct service accounts | Distinct service accounts per agent | Each agent has unique policy — no UID dependency |
| Setup complexity | Built into every Unix system | Identity provider + role definition | Cloud account + IAM config | npx @authensor/safeclaw — one command |
| Dependencies | None — OS-native | Identity provider, application code | Cloud provider SDK, account | Zero third-party dependencies |
| Offline operation | Yes | Depends on implementation | No — requires cloud connectivity | Yes — local policy evaluation |
| Open source | OS-native | Varies by implementation | No — proprietary cloud services | Yes — 100% open source client, MIT license |

How Each Model Handles Common Agent Actions

| Agent Action | File Permissions | RBAC | Cloud IAM | SafeClaw |
|---|---|---|---|---|
| Write to /data/output/result.json | Allowed if user has write on /data/output/ | Allowed if role has "write" permission | N/A — not a cloud API | Evaluated per-path, per-file pattern |
| Read /etc/secrets/api-key.txt | Allowed if user has read on file | Allowed if role has "read secrets" | N/A — not a cloud API | Can deny specific paths, require approval |
| Execute git commit -m "update" | Allowed if user has execute on git | N/A — no command-level control | N/A — not a cloud API | Evaluated per-command with argument inspection |
| Execute rm -rf /data/ | Allowed if user has write on /data/ | N/A — no command-level control | N/A — not a cloud API | Denied by default, requires explicit allow |
| HTTP POST to https://external.api/data | N/A — no network control | N/A — no network-level control | VPC egress rules (IP level) | Per-domain, per-endpoint policy |
| Upload to S3 bucket | N/A — not a local file operation | Possibly, if application-level | Yes — s3:PutObject on bucket ARN | N/A — cloud API (use Cloud IAM) |

Scoring Matrix

Rate each model on a 1-5 scale for AI agent safety requirements:

| Requirement | File Permissions | RBAC | Cloud IAM | SafeClaw |
|---|---|---|---|---|
| Per-action granularity | 2 | 2 | 4 | 5 |
| Agent identity awareness | 1 | 2 | 3 | 5 |
| Local file protection | 3 | 1 | 0 | 5 |
| Local command control | 1 | 0 | 0 | 5 |
| Network request control | 0 | 0 | 3 | 5 |
| Human-in-the-loop | 0 | 1 | 1 | 5 |
| Tamper-proof audit | 2 | 2 | 4 | 5 |
| Setup simplicity | 5 | 2 | 2 | 4 |
| Cloud resource control | 0 | 2 | 5 | 0 |
| Total (out of 45) | 14 | 12 | 22 | 39 |

Scores are specific to AI agent safety use cases. Each model may score differently for other use cases (e.g., RBAC scores highly for enterprise application authorization).

Key Takeaways

• No single model covers everything. File permissions handle kernel-level filesystem access. RBAC handles enterprise application authorization. Cloud IAM handles cloud resource access. SafeClaw handles local agent action gating. Production deployments need a combination.
• SafeClaw is the only model purpose-built for AI agents. The other three models were designed for human users, enterprise applications, or cloud infrastructure. They lack agent identity awareness, per-action evaluation with parameter inspection, and human-in-the-loop approval.
• The local action gap is the largest unaddressed risk. File permissions, RBAC, and Cloud IAM have minimal or zero control over local shell commands and network requests from AI agents. SafeClaw fills this gap directly.
• Cloud IAM is essential for cloud resources, irrelevant for local actions. If your agents call cloud APIs, Cloud IAM is non-negotiable. But it cannot protect the local machine where the agent runs.
• File permissions are a necessary foundation, not a sufficient safety layer. Every system should have proper file permissions. But they lack agent awareness, conditional logic, and cross-action-type control.

Recommended Layered Architecture

For production AI agent deployments, combine models for defense in depth:

Layer 1: File Permissions     — Kernel-level filesystem boundary
Layer 2: SafeClaw             — Per-action gating with human-in-the-loop
Layer 3: Cloud IAM            — Cloud resource authorization
Layer 4: RBAC (if applicable) — Application-level role management

Each layer catches what the others miss. File permissions enforce hard boundaries. SafeClaw provides intelligent, agent-aware, per-action evaluation. Cloud IAM governs cloud resource access. RBAC manages coarse-grained application permissions.

When to Use Which

File Permissions: Always — as a foundational kernel-level boundary. But never as your only control for AI agents.

RBAC: When your agents interact with enterprise applications that use role-based authorization. Not sufficient alone for agent action control.

Cloud IAM: When your agents call cloud provider APIs. Essential for cloud resource access, irrelevant for local action safety.

SafeClaw: When your agents perform local actions (file operations, shell commands, network requests) and you need per-action gating, human approval, and tamper-proof audit. The primary safety layer for autonomous agent workloads.

The Bottom Line

AI agents operate across authorization boundaries that no legacy permission model was designed to handle. File permissions, RBAC, and Cloud IAM each cover a piece of the puzzle. SafeClaw by Authensor fills the critical gap: per-action, pre-execution gating for local agent operations with 446 tests, zero dependencies, sub-millisecond evaluation, and deny-by-default architecture. Install: npx @authensor/safeclaw. Free tier at authensor.com.

See also: SafeClaw vs File Permissions | SafeClaw vs RBAC | SafeClaw vs Cloud IAM | AI Agent Safety Tools Landscape 2026