Enterprises do not adopt tools that cannot demonstrate regulatory alignment. That is not a preference. It is a procurement requirement. When a CISO evaluates an AI agent platform, one of the first questions is: which compliance frameworks does this map to? Until now, no trust-scoring system for AI agents could answer that question across multiple regulatory regimes simultaneously.

Today, CraftedTrust maps its 12-factor trust scoring and certification workflows to five major compliance frameworks: CoSAI, OWASP AI Security, EU AI Act, NIST AI RMF, and AIUC-1. Combined with audit history, governance views, and platform reporting, that gives enterprises the regulatory visibility they need to adopt AI agent tooling with confidence.

Why Compliance Matters for AI Agents

AI agents are no longer experimental. They are being deployed in production environments where they make API calls, access databases, process customer data, and execute business logic. The infrastructure that connects these agents to external capabilities -- MCP servers, tool registries, API endpoints -- is now subject to the same regulatory scrutiny as any other enterprise software.

The problem is that most AI agent tooling was built without compliance in mind. MCP servers were designed to be easy to publish and easy to connect. That simplicity is a strength for developers, but it creates a gap for enterprises that need to demonstrate due diligence before connecting agents to third-party infrastructure.

"If you cannot map your AI agent infrastructure to the frameworks your auditors care about, you cannot deploy it. Full stop."

CraftedTrust closes that gap. Every trust score we generate now includes framework-specific compliance mappings, so enterprises can see exactly how a given MCP server or AI agent tool aligns with the regulatory requirements that govern their industry.

The 5 Frameworks in Detail

1. CoSAI (Coalition for Safe AI)

The Coalition for Safe AI is an industry consortium dedicated to establishing practical safety standards for AI systems. CoSAI focuses on operational safety -- how AI systems behave in production, how they handle failure modes, and how organizations can verify that safety properties hold over time.

CraftedTrust's scoring engine has been CoSAI-aligned since its inception. Our 12 trust factors were designed to reflect the categories CoSAI identifies as critical for safe AI deployment: source provenance, permission boundaries, dependency integrity, and ongoing maintenance signals. When CraftedTrust scores an MCP server, each factor maps directly to CoSAI's safety categories, giving organizations a clear picture of how a server's trust posture aligns with consortium standards.

2. OWASP AI Security

The OWASP Foundation's work on AI security identifies the top risks facing AI applications, from prompt injection and training data poisoning to model theft and insecure output handling. OWASP's AI security guidance has become a de facto reference for development teams building AI-powered applications.

CraftedTrust maps trust factors to specific OWASP categories. For example, our Input Validation & Injection Resistance factor directly addresses OWASP's prompt injection risk category. Our Data Handling & Privacy factor maps to OWASP's sensitive information disclosure risks. Our Authentication & Authorization factor covers insecure plugin design. By surfacing these mappings in every trust report, we give development teams a way to evaluate MCP servers against the same risk taxonomy they already use for application security.

3. EU AI Act

The European Union's AI Act is the world's first comprehensive AI regulation. It classifies AI systems into risk tiers -- unacceptable, high, limited, and minimal -- with corresponding obligations for each tier. High-risk AI systems require conformity assessments, human oversight mechanisms, technical documentation, and ongoing monitoring.

MCP servers occupy an interesting position in the EU AI Act's risk taxonomy. They are not AI models themselves, but they are the infrastructure through which AI agents interact with external systems. A server that gives an AI agent access to a hiring database or a medical records system could place the overall system in the high-risk category, triggering the Act's most stringent requirements.

CraftedTrust's compliance mapping identifies which trust factors correspond to EU AI Act obligations. Our Compliance Alignment factor evaluates whether a server's documentation, data handling practices, and transparency measures meet the Act's requirements. Our Permission Scope factor flags servers whose capability requests could elevate the risk classification of connected AI systems. This allows enterprises serving EU customers to assess regulatory exposure before connecting an agent to a third-party server.

4. NIST AI RMF (Risk Management Framework)

The National Institute of Standards and Technology's AI Risk Management Framework provides a structured approach to identifying, assessing, and mitigating AI-related risks. The framework organizes risk management into four functions: Govern, Map, Measure, and Manage. It has become the primary reference for US federal agencies and an increasingly common requirement in federal contractor evaluations.

CraftedTrust's trust factors align with NIST AI RMF categories across all four functions. Our Source Verification and Maintainer Reputation factors support the Govern function by establishing provenance and accountability. Our Permission Scope and Dependency Health factors support the Map function by identifying where risk originates. Our scoring methodology itself implements the Measure function by quantifying risk across multiple dimensions. And our continuous monitoring and re-scoring capabilities support the Manage function by tracking risk posture over time.

We have also filed a formal comment with the NIST National Cybersecurity Center of Excellence (NCCoE) on the application of the AI RMF to AI agent infrastructure, specifically addressing how trust scoring for MCP servers and tool registries can serve as a practical implementation of NIST's risk measurement guidance.

5. AIUC-1 (Agent Interoperability and Use Cases)

AIUC-1 is a newer standard focused on agent interoperability -- how AI agents discover, evaluate, and connect to tools and services across different platforms and registries. It addresses the practical challenges of multi-agent architectures: how agents negotiate trust, how they verify the capabilities of external tools, and how they maintain security boundaries in cross-platform workflows.

CraftedTrust has submitted a technical contribution to AIUC-1 that proposes trust scoring as a standard mechanism for agent-to-tool trust negotiation. Our contribution defines how trust scores can be embedded in tool discovery protocols, allowing agents to programmatically evaluate whether a tool meets their organization's trust threshold before connecting. This is not theoretical -- it reflects the architecture we have already built into CraftedTrust's API and MCP tools.

How the Mapping Works

CraftedTrust evaluates MCP servers and AI agent tools across 12 trust factors: Source Verification, Permission Scope, Code Quality Signals, Dependency Health, Maintainer Reputation, Update Frequency, Community Validation, Authentication & Authorization, Transport Security, Input Validation & Injection Resistance, Data Handling & Privacy, and Compliance Alignment.

Each of these 12 factors maps to specific controls, requirements, or risk categories in all five frameworks. The mappings are not superficial labels. Each mapping identifies the specific clause, control, or risk category that a trust factor addresses, and the trust score for that factor reflects the degree to which the evaluated server meets the corresponding requirement.

For example, the Dependency Health factor maps to:

This cross-framework mapping is generated automatically for every scan. The result is a single trust report that an enterprise can hand to their compliance team, their auditors, or their procurement reviewers, with framework-specific evidence for every score.

Why This Matters

Before this capability existed, enterprises evaluating AI agent tooling had to perform manual compliance assessments against each framework independently. That process is slow, expensive, and error-prone. It is also a blocker. Teams that want to adopt AI agent infrastructure often cannot get past procurement without demonstrating framework alignment, and the manual assessment cost delays adoption by weeks or months.

CraftedTrust eliminates that bottleneck. One scan gives you compliance posture across five regulatory regimes. The mappings are specific, auditable, and updated as frameworks evolve. This is not a checkbox exercise -- it is the infrastructure layer that makes enterprise AI agent adoption possible.

Standards Engagement

We are not just mapping to these frameworks passively. We are actively engaged in the standards process:

Standards are not static. Frameworks will continue to evolve as AI agent architectures mature and new risks emerge. CraftedTrust's compliance mappings will evolve with them, ensuring that enterprises always have a current view of their regulatory posture.

AI agent infrastructure needs the same compliance rigor as any other enterprise software. CraftedTrust makes that possible by translating trust scores, certification evidence, and governance signals into the language that compliance teams, auditors, and regulators already understand. Explore the full platform at craftedtrust.com/platform.