MCP Access Control: OPA vs Cedar — The Definitive Guide

With the adoption of Model Context Protocol (MCP), AI gains a significant step towards autonomy, capable of not only querying information but also planning for and taking action in our digital systems. In order for us to take advantage of this increased utility, we must first understand and take control of the permissions with which AI agents are allowed to operate. Unlike traditional authorization systems that can assume an added guardrail of human context and judgement, MCP access control requires a policy engine that can evaluate who is making requests, what they're trying to access, and under what conditions access should be granted.

Two policy engines dominate the MCP access control landscape: Open Policy Agent (OPA) with its Rego language, and AWS Cedar. While both offer declarative permission-based policy languages, independent research and production usage reveal significant differences in their approach, security guarantees, and real-world performance.

The Security Reality: Independent Benchmarking Results

Recent independent research provides crucial insights beyond vendor claims. The results are telling: "Rego is expressive but error-prone, failing several tests due to runtime exceptions, non-determinism, and extensibility risks" while "Cedar is safe and deterministic, with strong validation and isolation." This isn't theoretical—it's based on automated testing of real security vulnerabilities including denial-of-service resilience, memory safety, and input validation.

Cedar's security advantage stems from deliberate design choices. As documented in Cedar's design philosophy, features like regex support were "intentionally omitted from the language because they work against safety goals." This isn't a limitation in the context of access control – it's a feature that prevents entire categories of security vulnerabilities.

OPA: The Flexible Powerhouse

OPA remains the gold standard for policy engines requiring sophisticated logic and extensive integrations. Built on Rego, a declarative query language extending Datalog, OPA can handle complex scenarios that would be impossible in simpler policy languages.

OPA's Strengths:

• Extreme expressiveness: Complex and dynamic authorization logic in policy definitions

• Rich integrations: Runtime access to databases and external APIs in policy decisions

• Deployment ease: Production-ready server provided out of the box 

• Mature developer ecosystem: Extensive command line and IDE tooling for policy testing and validation, CI/CD integrations, and monitoring support

• Operational readiness: Provides governance features, such as audit logging and policy versioning

Example OPA Policy for MCP:

# rego

package mcp.authz

# Risk-based access control with external data
allow if {
    input.principal.role == "devops"
    input.resource.type == "aws" 
    input.context.risk_score < user_risk_threshold
    is_business_hours
}

Cedar: The Security-First Choice

Cedar represents AWS's approach to authorization policy languages, prioritizing safety, performance, and mathematical certainty over raw expressiveness. Developed with formal verification in mind, Cedar offers unprecedented guarantees about policy behavior.

Cedar's Advantages:

• Mathematical certainty: Formal verification with proven correctness through Cedar Analysis

• Natural language syntax: Intuitive syntax with static type checking

• Sub-millisecond performance: Production deployments achieving extreme low-latency responses with low resource overheads

• Lightweight integration: Simpler embedding in existing applications with more control over a service wrapper

• Security by design: Built-in protection against entire categories of vulnerabilities

• Automated conflict detection: Cedar Analysis identifies policy conflicts and redundancies

Example Cedar Policy for MCP:

// cedar

// Clear, readable access control
permit(
    principal in Role::"devops",
    action in [Action::"describe", Action::"list", Action::"create"],
    resource
) when {
    resource.type == "aws" &&
    context.risk_score < 50 &&
    context.business_hours == true
};

// Hierarchical resource permissions
permit(
    principal,
    action == Action::"read",
    resource
) when {
    resource.type == "github" &&
    resource.repository.visibility == "internal" &&
    principal.team == resource.repository.owner_team
};

Cedar's Constraints: Cedar's security-first design comes with trade-offs. It intentionally lacks dynamic logic and external data source integration during policy evaluation. For MCP implementations requiring complex data processing or sophisticated business logic, these limitations can be significant.

The Decision Matrix

In addition to the decision criteria revealed by independent analysis, a deeper dive into the broader ecosystem adds important practical considerations regarding development support and operational needs:

Choose Cedar when:

• Security is paramount: Formal verification and safety guarantees are essential

• AWS-centric environment: Strong IAM integration and managed service preference

• Performance critical: Sub-millisecond response times required

• Simple to moderate policies: Straightforward access control without complex logic

• Team expertise limited: Immediate policy readability preferred over programming complexity

• Compliance requirements: Mathematical proofs of policy correctness needed

• AuthZEN compliance: Cedar's AWS AuthZEN interface provides standards-based interoperability

Choose OPA when:

• Complex integration requirements: External data sources, sophisticated identity providers

• Advanced business logic: Policies requiring dynamic logic and  external data 

• Existing operational maturity: Low set-up time with built-in developer tooling, CI/CD integrations, and a production-ready server

• Team has policy expertise: Comfortable with 30-40 hour Rego learning investment

• Ecosystem diversity: Need for Topaz-style specialization or OpenFGA-style ReBAC integration

Future Considerations

The policy engine landscape continues to evolve rapidly. Cedar Analysis provides mathematical verification capabilities that OPA's testing framework cannot match, while OPA's V1 language standard promises complex evaluation with improved tooling and reduced integration time. In the context of AI agent authorization, the two main policy engine choices seem to pose a fundamental question – is it more important to deterministically authenticate AI agents, providing impermeable guardrails within which agents can operate, or to dynamically evaluate each authentication request, attempting to understand the situational context? 

From a practical standpoint, organizations should consider not just current capabilities but trajectory and ecosystem momentum. MCP access control is a pressing problem in need of an immediate solution, but it is paramount to evaluate what is a short-term limitation versus a long-term compatibility with the problem at hand.

OpenID AuthZEN represents the most significant trend in this space, aiming to standardize authorization protocols similar to how OAuth standardized authentication. This effort could reduce vendor lock-in and enable seamless integration between different policy engines. For MCP implementations, AuthZEN compliance may become as important as OAuth compliance is today.

AI-assisted policy writing, zero-trust architecture integration, and cross-cloud policy federation represent additional emerging trends that may influence long-term strategic decisions.

Bottom Line: Evidence-Based Decision Making

Independent security research, production deployments, and industry expert insights provide clear guidance beyond marketing claims. Cedar excels in security-first environments where safety trumps flexibility, particularly in AWS-centric architectures with straightforward access control needs. OPA dominates mature operational scenarios requiring complex logic and extensive integrations.

The choice isn't about finding the "best" engine—it's about matching engine characteristics to your specific use case. For MCP access control, success depends on honestly assessing your security requirements, performance needs, integration complexity, and team capabilities. Both engines are production-ready and battle-tested, but they serve fundamentally different use cases and authorization philosophies.