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No. MCP can standardize how an AI application connects to tools and data sources, but it does not by itself establish who an agent is, what it may do, which policies apply, or how its actions are contained and audited. Enterprise teams need controls around the protocol as well as the protocol itself. The seven boundaries below are a practical architecture model—not an official MCP, NIST, or NSA taxonomy.
What MCP covers—and what it leaves to your architecture
MCP is an interoperability interface between AI applications and tools or data sources. That interface matters, but a connection format is not a complete security or governance design. An enterprise still has to decide how identities are established, how permissions travel with requests, where policy is enforced, what information may enter a model or tool, how actions run, what network paths are allowed, and what evidence is retained.
The distinction is important because an agent can dynamically select tools, rely on implicit trust between components, and share context across calls. The NSA’s security release on agentic MCP systems identifies those as systemic concerns alongside familiar needs such as authentication, authorization, and input validation. NSA security design considerations for AI-driven automation leveraging MCP
There is no reviewed standard that mandates exactly seven boundaries. This model groups documented concerns into areas teams can assign, test, and monitor. It is intended to help answer two practical questions: “How do you govern MCP servers in an enterprise?” and “How do you control what an AI agent can do?”
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1. Identity: distinguish the person from the agent and its services
A tool call may involve several identities: the human who asked for work, the AI agent acting on the request, the workload running the agent, and a service account used to reach a tool. Treating these as one undifferentiated identity makes it difficult to determine whose intent a request represents or which component actually performed an action.
Establish identities and lifecycles for the relevant actors, and make their relationship visible in the request path. NIST’s 2026 NCCoE concept paper discusses OpenID Connect for expressing authentication, consent, and authorization information; SPIFFE/SPIRE for workload identity and attestation; SCIM for identity provisioning and lifecycle management; and NGAC as a fine-grained access-control approach. These are candidate building blocks, not interchangeable solutions or a universal pattern. The paper describes a project focused on enterprise use cases where greater control and visibility can be maintained over agents and the systems they access. NIST NCCoE concept paper on software and AI agent identity and authorization
2. Authorization and delegation: carry scoped authority through every call
Authentication answers who or what is making a request; authorization determines what that actor may do. An agent should not inherit broad, ambient access merely because it is running on behalf of an authenticated employee. The system needs to preserve relevant user and workload context across calls, constrain permissions to the task, and provide a way to revoke access.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Define what delegation means in your design: which actions may be performed under the user’s authority, which require an agent or service identity, and which must be denied or escalated. Apply least privilege at the tool or data operation where possible, rather than relying on a prompt telling the model to behave safely. AWS guidance identifies identity propagation and permission boundaries as operational concerns for agents; these are vendor-authored recommendations, not requirements of MCP. AWS guidance on the agent layer and operations
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3. Policy and governance: enforce rules outside the model’s reasoning
Organizational rules should govern which models, tools, data, and actions are allowed in a given context. Make those rules enforceable at a control point that can evaluate a request before it crosses a boundary; do not make the model’s own interpretation the only guardrail. Policy should cover ordinary requests as well as tool use, data access, and requests that cross organizational or system boundaries.
Governance as code can help make rules reviewable, versioned, and consistently applied. AWS recommends organizational standards for agent protocols and governance as code. Separately, NIST’s published summary of comments reports that commenters supported a logically separate governance component to evaluate and enforce requests under defined policies. That is a summary of feedback, not a formal NIST requirement. AWS guidance on what needs to be governed · NIST summary of comments on agent identity and authorization
4. Data and context: control what crosses into prompts and tools
Decide which context an agent may receive, which information a tool may return, and what can be shared between components. Validate inputs and outputs at relevant boundaries, and consider whether a tool result or user-provided content could influence a later action. Context sharing is not just a prompt-design concern: the NSA identifies it as a systemic issue in agentic MCP systems.
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There is no single data-loss-prevention design established here for every enterprise. Choose controls based on the data involved and the paths it can take—for example, access restrictions at the source, limits on what enters model context, and checks on information returned by tools. Record enough about the path to investigate problems, while applying appropriate privacy and retention limits to the records themselves.
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5. Tool execution and containment: treat calls as consequential actions
A tool call can change a record, send a message, run code, or trigger another system. Classify actions by impact, validate requests before execution, and use human approval when the potential consequence warrants it. For work that can fail or be misused, define a bounded execution environment with clear limits on accessible resources and available actions.
AWS recommends designated sandbox environments with clear boundaries, circuit breakers, and tracking of agent execution paths. Those recommendations describe architecture choices; MCP itself does not provide a universal sandbox or prescribe a particular containment design. AWS governance guidance for agentic AI · AWS agent operations guidance
6. Network and protocol ingress and egress: govern the paths to servers
Inventory the MCP servers an organization uses, authenticate remote traffic, and decide which inbound and outbound connections are permitted. A server’s availability through a protocol does not mean every agent should be able to reach it. Apply network controls and identity-aware policy at the points where traffic enters or leaves the environment.
Product implementations can help with discovery and enforcement, but their capabilities and deployment status are distinct from MCP’s protocol properties. Microsoft describes an identity-centric MCP firewall for visibility, policy enforcement, and runtime protection; its documentation labels the capability preview. Azure API Management documentation describes MCP server discovery and JWT-based authentication and authorization patterns. Check the linked documentation for current availability and limitations before relying on a product capability. Microsoft MCP firewall documentation · Azure API Management MCP server overview
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7. Observability and audit: make actions reconstructable
Operational teams need enough evidence to understand what happened when an agent makes an unexpected request, produces an error, or performs a consequential action. Capture useful execution-path context, such as the initiating identity, the agent and tool involved, relevant policy decisions, and the result. AWS guidance recommends tracking agent execution paths and maintaining audit trails; NIST’s concept paper also emphasizes enterprise control and visibility.
Set retention and access rules for logs deliberately. More collection is not automatically better if records expose sensitive prompts or data, or if teams cannot reliably use them. Decide who can inspect records, what needs to be redacted or minimized, and how long evidence must remain available for operational and compliance needs.
How to turn the seven boundaries into an implementation plan
- Map one real workflow. Follow a request from the human who initiates it through the agent, policy checks, MCP server, downstream tool, and resulting data. Mark where identity or context changes.
- Assign an owner and enforcement point to each boundary. Specify which component makes the decision, which component enforces it, and how a denied or escalated request behaves.
- Test least privilege and delegation. Verify that the workflow can perform its intended task, cannot use unrelated tools or data, and loses access when its authorization is revoked.
- Exercise failure and containment paths. Test invalid inputs, unavailable dependencies, unexpected tool requests, and actions requiring approval. Confirm that limits and circuit breakers work as intended.
- Review evidence and exposure. Reconstruct a test action from logs, then check that the records contain enough context without retaining unnecessary sensitive content.
- Reassess integrations and status. Review protocol standards, server inventories, product capabilities, and policy as the deployment changes. Preview or early-access features should not be treated as stable protocol guarantees.
How to assess an implementation
When comparing architecture choices, assess how well each option preserves identity context, how finely it can authorize actions, where policy is enforced, what isolation it provides, what can be audited, how it interoperates with your systems, whether it is generally available or preview, and what operational work it adds. A gateway, identity system, sandbox, or logging platform may address part of the design; none should be assumed to replace the other boundaries.
NIST’s AI Risk Management Framework is voluntary, was released on January 26, 2023, and is being revised. NIST’s AI Agent Standards Initiative describes work to facilitate industry-led standards and community-led protocols. Neither establishes this seven-part architecture as mandatory. NIST AI Risk Management Framework · NIST AI Agent Standards Initiative
The practical verdict
Use MCP for interoperability, then design the surrounding controls to identify actors, constrain delegated authority, enforce policy, govern context, contain execution, control network paths, and produce useful audit evidence. The seven boundaries are a way to organize that work, not a claim that one protocol—or one product—can secure an enterprise agent by itself.
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