8 MCP security risks every enterprise needs to know

Model Context Protocol (MCP) is quickly becoming a foundational layer for how AI agents interact with enterprise systems. And that’s exciting! MCP lets AI connect to real data, real tools, and real workflows — CRM systems, ticketing platforms, internal apps, APIs — all in pursuit of smarter automation and faster decisions. But here’s the part we don’t talk about enough: MCP also introduces an entirely new attack surface. One that most organizations are already exposing… without realizing it. I talk to security, IT, and product leaders every week who are racing to adopt AI. Many are already using MCP directly or indirectly, yet very few can confidently answer basic questions like: 

• Where is MCP being used today?
• Which agents and tools are connected?
• What data and systems are those connections touching?

That visibility gap is only the tip of the risk iceberg, with S.S. MCP sailing full steam ahead. Let’s break down the top eight MCP security risks every organization using generative AI needs to understand before those risks turn into real incidents.

1. Lack of visibility of MCP usage

The most common MCP risk is also the simplest: organizations don’t know where MCP is being used. Security teams often lack a complete inventory of:

• AI agents using MCP
• MCP servers being adopted locally, or deployed remotely
• The enterprise systems AI agents or clients can now access, or the identities they are leveraging

Without visibility, there’s no way to assess exposure, apply policy, or respond to incidents. MCP ends up interacting with databases, APIs, and internal tools without being governed like any of them. You can’t secure what you can’t see.

2. Uncontrolled access to remote MCP servers

MCP makes it easy for agents to connect to remote servers tied to critical SaaS enterprise platforms like CRM, ERP, or data platforms. But when those connections aren’t centrally managed, they can be spun up without approval or oversight. Agents and the users behind them may gain access to sensitive systems without security teams knowing it’s happening. That lack of control dramatically expands the attack surface and increases the risk of unauthorized or unintended access.

3. Risky or unapproved local MCP servers

MCP doesn’t just live in centralized infrastructure.Developers and power users can (and do!) spin up local MCP servers on their own machines to experiment or move faster. While that flexibility is part of MCP’s appeal, it also introduces serious risk. Local MCP servers often:

• Bypass enterprise security policies
• Operate without monitoring or logging
• Expose sensitive data outside approved environments

These “servers” effectively become shadow infrastructure invisible to security teams but fully capable of accessing enterprise systems.  Trying to stop local MCP usage means stopping developers from executing any code (and halting your business!).  And they aren’t just being used by developers - the ability to click to add an MCP server as an “integration” is now a native experience on most AI clients - making MCP present from the command line to the C-Suite. 

4. Destructive actions via MCP servers

Unlike passive integrations, MCP calls can trigger actions, including unintended ones because MCP isn’t a read-only integration. MCP requests can trigger real actions including destructive ones. Without proper guardrails, MCP can be used to delete data, change configurations, deploy malicious workloads, abuse APIs, or trigger workflows in unintended ways. This isn't a hypothetical risk. MCP is interactive by design, which means mistakes (or abuse) can have immediate operational consequences.

5. Persistent end-user authentication

Many AI clients authenticate MCP requests using the end user’s identity. That means the agent effectively inherits the user’s access rights. The problem is that those privileges were designed for use by a human, not for access by non-deterministic systems. Not only is the individual’s oversight missing, but the granted credentials can persist beyond the original session. If not tightly controlled, MCP activity can continue operating with elevated permissions long after a user has logged out increasing the risk of credential abuse, overreach, and unauthorized access.

6. MCP supply chain attacks

MCP doesn’t exist in isolation. It relies on a web of tools, dependencies, servers, and third-party components. If any part of that chain is compromised, attackers can manipulate AI agents, intercept requests, or exfiltrate data. These attacks hijack trust relationships organizations assume are safe and they’re extremely difficult to detect without MCP-specific visibility.

7. Data exfiltration to third-party AI services

Not all MCP endpoints are created equal. Unvetted tools or external MCP servers can quietly export enterprise prompts, context, or sensitive data to third-party AI services or malicious actors. Because this traffic often looks like legitimate MCP activity, it can slip past traditional security controls. The result is sensitive data leaving the organization unnoticed.

8. Corporate IP under personal licenses

One of the fastest-growing risks we’re seeing: MCP agents configured with personal AI accounts. When employees use consumer ChatGPT, Claude, or other personal licenses to access enterprise data through MCP, that data can end up in non-compliant environments. That puts corporate IP, regulated data, and compliance posture at serious risk often without malicious intent.

What effective MCP governance looks like

Understanding these risks is only half the battle. To truly manage MCP securely, organizations need controls that are specific to how MCP operates, not just traditional firewall and IAM policies.

Successful MCP adoption and enablement should:

• Enforce MCP access policies and block unapproved servers and tools in real time for both local and remote servers across any AI client or agent
• Detect and stop risky or destructive MCP requests before damage occurs
• Protect against rogue internal clients and MCP supply chain attacks
• Maintain continuous visibility across all MCP activity

When these controls are in place, security teams gain the confidence to support AI adoption — without slowing innovation. That’s exactly the gap SurePath AI was built to close. We help organizations adopt MCP safely, with visibility and control from day one — so teams can move fast and stay secure. 

If MCP is on your roadmap (or already in your environment), let’s talk! Book a demo.