AI Agent Access Control
AI agent access control for Moltbot. RBAC, ABAC, policy-based access control and granular permission models for AI agent systems.
What is Access Control? Simply Explained
Access control is like a bouncer for AI agent actions: it decides what an agent can and cannot do. RBAC (Role-Based Access Control) assigns roles with permissions. ABAC (Attribute-Based Access Control) uses context attributes for fine-grained decisions. Least privilege means minimal necessary rights. Just-In-Time access grants temporary rights only when needed. Access Policy as Code defines policies programmatically with OPA. Without access control, AI agents can access data unauthorized, perform sensitive operations, or compromise the system.
↓ Jump to core concepts and implementation
Core Concepts
1. Role-Based Access Control (RBAC)
Role-based access control for AI agents. Clear roles with defined permissions — no wildcard access.
2. Attribute-Based Access Control (ABAC)
Attribute-based access decisions for fine-grained control. Context-awareness in access policies.
3. Least Privilege Enforcement
Enforcement of the least-privilege principle for every agent. Regular access reviews and privilege cleanup.
4. Just-In-Time Access
Temporary access only when needed. AI agents receive elevated permissions only for the duration of a task.
5. Access Policy as Code
Access policies as code with Open Policy Agent (OPA). Versioned, testable and automatically enforced.
Advanced Techniques
OPA Gatekeeper
OPA Gatekeeper for Kubernetes policy enforcement. AI agent pods without correct annotations are blocked.
Dynamic Authorization
Context-dependent authorization at runtime. Access rights based on current risk level and context.
Access Governance
Regular access certification campaigns. Manager confirmation of all agent permissions quarterly.
Privilege Escalation Detection
Detection of privilege escalation attempts by AI agents. Alert on unexpected permission changes.