Any time a new technology emerges that includes the ability for an AI model to autonomously “reach out and interact with the world,” legal and operations teams take notice. And rightly so, the legal and operational implications of any AI autonomy deserve careful consideration. MCP connectors are powerful precisely because they reduce friction between AI models and databases, and between each prompt and the resulting action, resulting in a proliferation of agentic AI. But reduced friction cuts both ways. The same architecture that makes MCP efficient also makes it a meaningful source of risk that requires appropriate governance.
Below, we examine the key legal and operational risks that an organization should consider before and during any MCP connector deployment in its AI portfolio. It is worth noting that risks inherent in generative AI use more broadly persist when an MCP connector conduits to an LLM, but the below analysis focuses on the risk specific to the new MCP connection infrastructure.
Data Access and Privacy Liability
As we discussed in the first installment of this series, the MCP is designed to enable AI models, via a more consistent and predictable connection, broad, dynamic and enduring access to organizational data. In practice, this means a connected AI model can read or write source databases, document repositories, CRMs, and other enterprise systems in real time. This level of access comes with risk, including the following:
- Over-Permissioned Access
MCP servers are often configured to expose the querying LLM to more data than any individual query requires. For example, a model directed by a lawyer to draft a client summary might have access to, review and leverage information in its response from billing records, human resources files or litigation documents in the same connected system. Unlike a human employee who exercises judgment about what to look at, an AI model will use whatever context it can access. Over-permissioned access is already a real problem for organizations even when using mainstream AI tools such as Microsoft CoPilot. - Regulatory Compliance
If an MCP connector touches systems containing personal data, the full weight of applicable privacy law follows (e.g., GDPR, CCPA, HIPAA). The fact that data was accessed by an AI model rather than a human employee does not create an exemption or change the obligations under privacy law. Particularly in the health care and financial services industries, organizations must assess whether AI-based data access triggers additional obligations around data minimization, purpose limitation or consent. Organizations may benefit from creating AI-specific compliance frameworks where an MCP connector exposes an AI model to personal data. - Cross-Border Data Flows
Enterprise systems often span multiple countries and jurisdictions. When an MCP connector retrieves data from a system hosted in the European Union to generate output processed by a model running on U.S. infrastructure, that transfer will likely implicate data localization requirements or cross-border transfer restrictions. This risk is sharpened by the growing global trend toward data sovereignty requirements, which go beyond regulating how data moves to mandating where data may be stored and processed at all. Countries including China, India, Russia and several EU member states have enacted or proposed sector-specific sovereignty rules that could be implicated any time an MCP connector retrieves locally hosted data for processing by a model running on foreign infrastructure.
Unauthorized or Erroneous Actions
As we’ve described elsewhere, the MCP can use its “primitives”—the standardized components that define how models interact with external systems—to move from information retrieval into consequential action. An AI model with tool access via an MCP connection can take action within a connected application by itself (e.g., creating tickets, sending messages, updating records, executing transactions or triggering downstream workflows) without a human manually confirming each step.
- Human Oversight Is Optional
MCP does support human-in-the-loop approval, but such approvals are optional rather than a default. Organizations that deploy MCP connectors without carefully evaluating whether appropriate approvals have been implemented are introducing major operational and legal risk. An AI model that misinterprets an ambiguous instruction and deletes a record, sends an unintended communication, or initiates a transaction is not an edge case; it is a core and foreseeable hazard that organizations should expressly plan for.
In certain regulated contexts, there are express requirements for human supervision of consequential decisions. Where such supervision is required, deploying MCP in a way that routes consequential actions through an AI model without meaningful human review will put organizations at odds with these obligations, regardless of whether the AI takes an action that is technically correct.
- Contractual and Regulatory Consequences
Errors in AI-driven actions are not excused by the fact that a human did not personally execute them. If an MCP-connected model sends an erroneous notice to a counterparty, submits a regulatory filing with incorrect data or modifies a contract record, the legal consequences flow back to the organization. “The AI did it” is not a recognized defense, and as courts become more familiar with AI errors, they are becoming less and less sympathetic to organizations failing to put proper oversight into place. - Cascading Failures
Because MCP connectors can chain together (for example, a model might pull data from one system, process it, and write results to another), errors can propagate across multiple systems before they are caught by the organization. The more autonomy that is built into the workflow, the wider the potential blast radius of a single error.
Security Vulnerabilities
MCP connectors materially expand the attack surface of any system they interface with. Each server represents a potential entry point, and the novelty of the protocol means that security best practices are still maturing. The key security risk associated with MCP connectors is that they generally widen the scope of data accessible to the AI model. All other potential security issues flow from that expanded data access.
- Credential and Authentication Risks
MCP servers must authenticate with the external systems they connect to. This typically means storing or managing credentials, API keys, or OAuth tokens. A compromised MCP server could provide an attacker with broad access to connected enterprise systems and reveal credentials that an AI model would otherwise not have access to. - Data-Related Vulnerabilities
MCP connections can enable not only read-access to systems and data, but also write-access, which means users will have to carefully ensure that an MCP connection does not allow an AI model to autonomously change or delete data in unexpected ways. These unexpected interactions have the potential to cause data loss or exposure issues.
Greater access to systems compounds risk. A well-configured MCP deployment uses least-privilege access (i.e., each server can reach only what it requires to operate). However, a misconfigured or overly permissive MCP server could allow an attacker who gains access to the model or the MCP layer to traverse connected systems that would otherwise be inaccessible.
Further, because MCP resources allow users to more easily expose external data as context that the AI model will read and act on, a malicious actor who can influence what data gets returned will have an increased opportunity to manipulate the model’s behavior. For example, such an actor could embed adversarial instructions in a document the model retrieves, thus introducing those instructions into the model’s behavior. This is the AI equivalent of a SQL injection attack. While this risk exists in any AI-related data set, whether connected to via MCP or otherwise, the increased interoperability that the MCP allows for further exacerbates this risk of malicious data injection. Organizations that allow MCP connectors to read from systems where external parties contribute content (e.g., shared drives, ticketing systems, email) should treat this as a live threat vector.
Accountability and Governance Gaps
MCP connectors make it substantively harder to answer the question “who is responsible for what happened?” Any tool empowered by MCP connectors to reach in and act on an external system, without proper controls, could introduce the risk of the “techno-responsibility gap” – a point at which a manufacturer of a machine is not capable of predicting the future machine behavior any more, and thus there is uncertainty on which party should be held morally responsible or liable for its actions with no established standard expectations. This impacts both operational and legal accountability.
- Diffuse Liability Chains
An MCP-driven workflow might involve an AI model (from one vendor), an MCP connector (either from the same vendor or another), the enterprise data source, and the user who initiated the original prompt. When something inevitably goes wrong, assigning responsibility across that chain requires developing new legal frameworks to clearly allocate responsibility. - Auditability
Regulated industries are accustomed to being able to, and are often required to be able to, explain what happened in any given transaction or decision. By default, AI systems that act autonomously via MCP connectors may not produce audit trails that satisfy regulatory requirements.
Third-Party Tool and Data License Risks
MCP connectors access external tools, databases and data sources that are themselves governed by contractual terms, licensing agreements and usage policies. Many agreements and relationships are still running to catch up to the new technology, and it is inevitable that users will be judged harshly when new use cases don’t fit into the original mold.
- Licensing Restrictions on Automated and AI-Driven Access
Many enterprise data licenses, API agreements and SaaS subscription contracts include provisions that restrict use to human users, prohibit automated querying or expressly exclude AI-driven access. These restrictions are common in agreements covering financial data feeds, legal research platforms, market intelligence services and proprietary databases, among others. An MCP connector that programmatically queries a licensed data source may violate these restrictions even if the underlying subscription is otherwise current and in good standing (though note that some organizations are taking a proactive approach to enable, in a controlled manner, such access and use). - Per-Seat and Per-User Pricing Implications
Many SaaS and data platform agreements price access on a per-user or per-seat basis. When an MCP connector routes AI-model requests through a shared credential or service account, the contractual question of whether the AI model counts as a “user” under the applicable agreement is often unresolved. Some vendors are beginning to take the position that each AI agent or model accessing their system constitutes a separate user for pricing purposes. Even where a vendor has not yet taken that position, the volume of queries generated by an AI model may trigger audit rights, true-up obligations or renegotiation demands. - Rate Limiting and Throttling
AI models connected via MCP can generate query volumes that far exceed what any individual human user would produce. Most APIs and enterprise platforms implement rate limits to protect system integrity and allocate capacity across their user base. An MCP-connected model that floods a downstream system with requests can trigger throttling, temporary access suspension or degraded performance across dependent workflows. In a worst case, sustained over-consumption could constitute a violation of the platform’s acceptable use policy. - Terms of Service Violations with Downstream Consequences
Beyond formal licensing agreements, many platforms govern access through terms of service that prohibit scraping, bulk extraction or use of retrieved data for model training or AI-generated outputs. An MCP connector that retrieves content from such a platform and feeds it into a model’s context window may implicate these restrictions, even if the retrieval is technically permitted under the subscription tier. Where the platform is a counterparty with whom the organization has a broader commercial relationship, a terms of service violation can have consequences well beyond the immediate data access issue.
Conclusion
MCP connectors represent a fundamental shift in how AI models interact with enterprise systems, and the legal and operational landscape has not yet caught up. The potential for efficiency gains is real, but they come with looming legal and operational risks. The organizations that will manage this technology best are those that approach it not just as a technical deployment question, but as an enterprise risk question that must be addressed by the appropriate legal, security, compliance, and operations teams.
The window to get ahead of this is open, but it won’t stay that way. Regulatory attention is increasing, vendors are tightening their terms, and courts are showing less patience with organizations that treat AI errors as unforeseeable. The time to build the framework is before something goes wrong, not after. In the next installment, we’ll move from diagnosis to action, offering a practical starting framework for mitigating the risks examined here.
For more information on enterprise AI governance or MCP connector risk management, contact Ed Cavazos or Brooke Daniels, or visit Artificial Intelligence (AI) Law | Pillsbury Law.
This is the second in a five-part series on the Model Context Protocol. In our first installment, we covered how the MCP works and what makes it, and the MCP Connectors it is a part of, architecturally distinct. Here, we examine the risks that enterprises should understand before deploying MCP Connectors as part of their AI use cases.
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