Model Context Protocol (MCP) has quickly become a foundational building block for agentic AI. By standardizing how AI agents discover tools, retrieve context, and take action, MCP makes it dramatically easier to connect models to real systems. That ease of integration is exactly why teams are adopting it so quickly — and exactly why security teams are uneasy.
MCP wasn’t designed to be dangerous. It was designed to be flexible. And as with most flexible integration layers, security risks don’t come from one obvious flaw but from how many small, reasonable decisions can combine into something exploitable.
A return to the office
To make this concrete, consider a familiar workplace analogy — assuming you’ve returned to the office.
Imagine an office building where employees badge in to each area, like the lobby, conference rooms, and supply closets. This badge also grants access to resources or services, such as elevators and printers. None of those permissions individually seems risky.
But if someone can badge into every room, bring guests without approval, and access filing cabinets without oversight, the building is suddenly far less secure — without anyone intentionally breaking the rules.
MCP works much the same way. Each server, tool, or permission may be defensible on its own. The risk emerges when agents are allowed to combine access, reasoning, and execution without sufficient guardrails.
This concern isn’t theoretical. As Gartner® notes in their latest research, “MCP was built for interoperability, ease of use and flexibility first, so security mistakes can manifest without continuous oversight for agentic AI.”
This blog breaks down practical strategies for securing MCP in two places where it matters most:
- Protecting MCP servers themselves
- Protecting how MCP is used inside AI systems
Part 1: Defining clear trust boundaries and checks for MCP
From a Zero Trust perspective, MCP servers should be treated as independent products with explicit trust boundaries, not shared utilities available to any agent that asks.
Many MCP servers start life as “just an integration.” A lightweight service that exposes data or actions to an agent. But once an MCP server is deployed, it effectively becomes a new API surface—one that an AI can reason over, chain, and misuse in ways traditional software never would.
Scope MCP Servers to a clear domain
One of the most common early mistakes is building MCP servers that are overly general. A server that can “read files,” “query databases,” and “send messages” across domains may be convenient, but it creates compounding risk. Intent should be defined, and deviations from that intent flagged for remediation.
A safer pattern is to treat each MCP server like a domain-owned product:
- A finance MCP server exposes only finance-relevant queries and actions.
- An HR MCP server exposes only HR workflows.
- A DevOps MCP server never overlaps with customer data.
This mirrors how mature organizations already manage APIs. Domain experts define what actions make sense, what data is appropriate, and what should never be automated. Once the scope and intent are defined, we still can’t trust that configurations or behavior won’t change. Auditing is required to regularly scan for changes or drift in MCP traffic.
Apply least privilege to agents, not just humans
Traditional IAM focuses on what people can do. MCP forces a shift toward what agents can do. Even if a human user has broad access, the agent acting on their behalf should not. MCP servers should enforce per-agent authentication (not shared tokens) and explicit role boundaries for agents, along with continuous reauthentication for long-running sessions.
Harden the MCP supply chain
MCP servers are often pulled from public repositories or installed via packages. That convenience introduces classic supply chain risk—now amplified by the fact that AI agents will happily execute whatever logic they are given.
We first need to centralize approved MCP servers in an internal registry and block the execution of unapproved or locally run MCP servers.
Aligned with zero trust, you should also validate schemas and manifests to prevent silent command remapping. This is especially important given the rise of attacks that hide malicious behavior behind otherwise legitimate MCP interfaces.
Log for accountability, not just debugging
Logging is often treated as a troubleshooting tool. For MCP, logging is a security control.
Effective MCP server logging should capture:
- Who initiated the request
- What the original prompt was
- Which tools were invoked
- What parameters were passed
- What external systems were contacted
Without this level of visibility, security teams are left guessing whether an incident was accidental, malicious, or simply emergent agent behavior.
Part 2: Protecting MCP inside AI Systems — risks of embedded trust
One of the most misunderstood aspects of securing MCP is the assumption that agent behavior can be validated in advance. As Gartner® puts it, “AI agent programs behave dynamically, determined by both the task and the data, so security testing cannot assess AI agent behavior before execution.”
Traditional security assumes behavior can be validated before deployment. Agentic AI breaks that assumption. A Zero Trust model accepts that intent and behavior must be verified at runtime, because agents reason dynamically based on prompts, data, and tools.
Even perfectly designed MCP servers can become dangerous when placed inside agentic AI systems. This is where risks like prompt injection, tool poisoning, and data exfiltration stop being theoretical.
The Varonis Threat Labs' research into MCP-based DNS rebinding attacks is a good example. It shows how agents can be manipulated into redirecting requests, exfiltrating data, or invoking tools in unintended ways — without exploiting a traditional vulnerability.
Assume you cannot fully test agent behavior ahead of time
Unlike traditional software, agent behavior is determined at runtime — by prompts, data, and model reasoning. That means pre-deployment testing can never fully prove safety.
Instead of relying solely on testing, continuously monitor and alert on anomalous agent behavior or tool usage and when agents deviate from expected patterns.
This mindset shift is critical. Security moves from “prove it’s safe” to “detect when it’s not behaving safely.”
Insert runtime controls between agents and MCP
One of the most effective ways to reduce MCP risk is to place enforcement in the execution path, not just in design documents.
Runtime controls can:
- Block prompt injection attempts
- Prevent unauthorized tool usage
- Stop sensitive data from leaving approved boundaries
- Detect when agents attempt actions that violate policy
It’s like configuring employee badges so access is limited to specific rooms and systems, instead of issuing a single badge that opens every door in the office – while also detecting attempts to access other areas or resources in the building.
Treat agents as potential adversaries
Some of the most dangerous MCP failures don’t come from bugs or attackers — but from well-intentioned agents trying too hard to be helpful. Agents may combine tools in unsafe ways or escalate privileges to complete a task. Designing controls with this in mind helps prevent accidental harm, not just malicious abuse.
How Varonis Atlas helps organizations apply Zero Trust to MCP
Atlas operationalizes Zero Trust for agentic AI by treating every agent action, MCP call, and tool invocation as something to be verified, enforced, and observed continuously, not trusted by default.
Securing MCP requires more than guidelines—it requires continuous visibility, enforcement, and feedback loops. This is where Atlas fits naturally into the MCP security lifecycle.
Atlas helps organizations:
- Discover MCP usage automatically: By inventorying AI agents, tools, and MCP servers across code, cloud, and hosted services, teams gain visibility into both approved and shadow MCP usage.
- Assess MCP-related risk proactively: Atlas identifies misconfigurations, vulnerable dependencies, agentic risks like tool poisoning, and unsafe agent behavior before incidents occur.
- Enforce runtime guardrails: Through policy-based controls, Atlas can block malicious or unintended tool invocations, prevent sensitive data leakage, and stop unsafe agent actions in real time.
- Monitor and alert continuously: Detailed telemetry shows how MCP tools are actually used, which controls are firing, and when behavior deviates from expectations—enabling fast response.
- Support governance and compliance: By tying MCP usage to broader AI risk frameworks and regulatory requirements, Atlas helps organizations demonstrate control without slowing innovation.
Watch a full demo of Varonis Atlas in this video.
In short, Atlas turns MCP security from a static checklist into a living system — one that evolves as agents, tools, and use cases evolve. MCP is not inherently insecure. But like any powerful integration layer, it amplifies both good and bad design decisions.
Organizations that trust but verify all MCP client and server interactions — not a one-time review — will be far better positioned to scale agentic AI safely. The goal isn’t to slow down innovation. It’s to make sure the doors MCP opens don’t stay unlocked longer than they should.
Gartner, Best Practices to Counter MCP Security Risks, Aaron Lord, Keith Guttridge, Alex Coqueiro, 5 February 2026
GARTNER is a trademark of Gartner, Inc. and/or its affiliates.
