Building Secure AI Agents with the Model Context Protocol (MCP)

The Model Context Protocol (MCP) is accelerating AI integration by enabling models to dynamically access and interact with tools and data across enterprise ecosystems. However, this newfound connectivity brings forward serious governance, access, and security requirements.

In an era where AI agents can autonomously perform key functions — from escalating customer issues to influencing supply chain decisions — identity verification, permission enforcement, and comprehensive auditing are no longer optional. These elements form the backbone of trustworthy AI deployment, ensuring that AI acts within established boundaries, respects organizational policies, and enables governance teams to maintain visibility and control.

This article explores how MCP is transforming the security paradigm for AI agents by treating them as first-class identities and embedding robust security controls throughout their lifecycle.

The New Security Perimeter—AI Agents as “First-Class” Identities

As organizations integrate MCP-powered AI agents deeper into production workflows, the notion of security perimeters is dramatically changing. Where traditional systems focused on authenticating human users, AI agents now act on behalf of people—sometimes with broad or sensitive privileges. To prevent these powerful automations from becoming points of exploitation, leading enterprises treat each AI agent as a "first-class" identity with all the protections, checks, and controls applied to a human.

Michael Pytel, Lead Technologist at VASS U.S. & Canada, underscores the stakes:

“When an AI agent performs an action, it's doing so on behalf of a human. It's critical that MCP servers are built to validate the user's identity and enforce their specific permissions for every request, preventing the agent from becoming a security vulnerability. There are tons of unknowns here.”

Identity Delegation & Ephemeral Permissions in Practice

Modern organizations are pioneering ephemeral permissioning models to ensure that AI agents can only act precisely within the scope and duration intended. In SAP and cloud ecosystems, this means providing short-lived access tokens tied to a user’s identity and role, with every agent action continuously logged, linked, and auditable.

Siri Varma Vegiraju’s team at Microsoft Azure Security demonstrates this approach by employing Zero Trust principles in cloud environments:

• Each AI agent request is authenticated using real user credentials and role checks.
• Before any critical operation is approved, the agent's planned action is cross-checked against the user’s current permissions and network isolation rules.
• AI is never granted blanket access; context-aware, least-privilege access is the default.

Vegiraju explains the real-world impact:

“By pairing our domain-specific models with MCP-exposed tools, we’ve seen a 40% improvement in vulnerability detection accuracy compared to legacy systems. Its flexibility has allowed us to rapidly iterate and expand functionality.”

Attack Path Simulation and Role-Aware Controls

A powerful illustration of this new perimeter is the ability for MCP-enabled agents to simulate potential attack paths in cloud-native infrastructure. The Azure Security team’s agents can decompose a threat model, enumerate the real access and segmentation rules that apply to each asset, and surface where misconfigurations could expose critical business logic. This isn’t just hypothetical—these workflows empower security teams to proactively close gaps before attackers can exploit them.

Key Takeaway: For any enterprise deploying MCP, identity delegation, role-aware controls, and strong authentication are foundational. AI agents must never be “trusted by default.” Mature organizations ensure every agent action is traceable, properly scoped, and always tied back to a real human’s identity and intent.

Operationalizing Permissions and Context-Aware Access

As MCP-enabled AI agents become integral to enterprise workflows, managing permissions at scale—and tailoring access according to evolving contexts—is paramount. Firms must operationalize granular, real-time control mechanisms that govern what data and actions agents can access, ensuring compliance without throttling innovation.

User-Centric Permissions and Scoped Access

Karen Ng, SVP of Product and Partnerships at HubSpot, highlights the power of reflecting business user permissions directly into AI workflows.
She explains:

“We built our remote MCP server to ensure that agents see exactly the data the user could view through our platform’s UI, applying user-level permissions and granular scopes. This means our AI helpers operate under the same guardrails as the humans they represent.”

This model dramatically reduces risk, enabling companies to make AI functions accessible beyond developer teams—empowering marketers, analysts, and customer service reps safely and confidently.

Secure Workflows with Audit and Review

Wayne Burnette of Wayfound describes a sophisticated model for operational governance built into MCP services. Their approach leverages the ‘evaluate_session’ API, which inserts a compliance checkpoint before agents finalize sensitive actions.
He shares:

“Before an agent sends an email or triggers a change, the evaluate_session tool cross-references actions against guidelines about tone, bias, and PII, preventing problematic outputs. This layer of review enables safer agent autonomy and reduces downstream risks.”

This model empowers enterprises not just to delegate permissions—but to continuously audit and enforce policies during agent operation, blending automation with human oversight.

Seamless, Secure Developer Experiences

Julie Ferris-Tillman at Interdependence underlines how operationalizing context and permissions accelerates adoption.

“Our MCP implementation exposes data and AI tools with strict access controls but also enables teams to onboard new use cases quickly, thanks to consistent, predictable permission management.”

This balance of security and usability ensures that AI remains an enabler—not a bottleneck—in evolving enterprise processes.

Governing the Market—Maintaining Trust with Composable AI Tools

As the Model Context Protocol fosters a vibrant ecosystem of interoperable AI tools and plugins, enterprises face the critical challenge of maintaining trust across a distributed and rapidly evolving marketplace. Organizations must weave robust governance into the fabric of this composability to prevent risks stemming from malicious or misconfigured connectors.

Open Ecosystems Require Open Governance

Greg Jennings, VP of Engineering at Anaconda, highlights the immense opportunity — and attendant responsibility — in enabling broad community-driven tool development:

“MCP establishes a standard interface allowing any model to interact with a wide range of tools, sparking an explosion of creativity. Yet this openness surfaces governance challenges: verifying that third-party MCP interfaces don’t leak data or introduce malicious behavior, managing who can install or update connectors, and coordinating API changes across many independently published plugins.”

For enterprises, this mandates comprehensive vetting, transparent permissions, and enforceable contracts around every connector plugged into their environment.

Runtime Control and Telemetry

Furthermore, organizations like K2view and 0rcus are pioneering real-time governance models embedding controls at runtime to continuously audit, restrict, and adapt agent behaviors.

Yuval Perlov (K2view) emphasizes:

“Enterprises need granular, dynamic access controls that enforce masking and filtering per request — not just static API keys. Without this, connectors risk exposing sensitive data during inference.”

Nic Adams (0rcus) notes that this approach extends even to offensive cybersecurity simulations:

“By combining telemetry streams with agent execution policies, MCP enables chaining diverse modules while imposing context-aware guardrails, preventing runaway or unauthorized behaviors.”

These capabilities underpin safety and compliance at scale, ensuring AI agents remain accountable within complex, multi-tiered ecosystems.

Metrics and Best Practices for Secure MCP Adoption

Beyond implementing controls and policies, leading organizations recognize the importance of measuring the real-world effectiveness of their security model for MCP-powered AI agents. Mature adopters apply a mix of quantitative and qualitative metrics, learning from both wins and early setbacks to continually improve their governance approach.

Measuring What Matters: Security and Trust Metrics

Organizations like Microsoft Azure Security track reduction in manual analysis time and improvements in detection coverage as direct outcomes of their MCP-enabled workflows. Siri Varma Vegiraju reports a striking “40% improvement in vulnerability detection accuracy” by pairing domain-specific models with MCP-exposed tools and continuously assessing coverage gaps.

Wayfound’s Chad Burnette emphasizes the value of adoption and auditability:

“Success is primarily measured by usage of our MCP server. True validation comes when more interactions occur via MCP than our legacy REST API, reflecting both user trust and operational efficiency.”

Efficiency, Speed, and Risk Reduction

Others, like Interdependence Public Relations, track concrete latency and efficiency gains:

• Mean tagging latency dropped from 4 minutes to under 47 seconds as MCP enabled composable, real-time agent orchestration.
• 0rcus measures Mean Time-To-Chain (MTTC), emulation velocity, and marked reductions in manual orchestration glue code, demonstrating the tangible business value of streamlined, governable agentic workflows.

Lessons from Early Adopters

Organizations such as K2view warn that robust security is a journey, not a destination. As Yuval Perlov points out, MCP is a framework—success hinges on layering granular access controls, dynamic context logic, and runtime enforcement on top.
Greg Jennings at Anaconda recounts unexpected governance challenges, such as verifying third-party connectors and managing plugin updates and compliance—a reminder to build in transparent permissioning and continuous audit trails from day one.

Best Practices for Secure MCP Architectures

• Enforce least-privilege, granular permissions for all agent actions, tied to runtime context and user identity.
• Continuously log and monitor all agent tool invocations for compliance and forensic requirements.
• Regularly review and update access controls and connector vetting as business needs and regulations evolve.
• Instrument efficiency and risk metrics, moving from qualitative feedback to quantitative, automated insights as deployment scales.

Driving Efficiency, Security, and Innovation

The Model Context Protocol (MCP) is rapidly maturing as a foundational technology that empowers enterprises to harness AI agents securely, efficiently, and at scale. By establishing a universal, standardized interface for AI to interact with diverse tools and data sources, MCP unlocks unprecedented opportunities for workflow automation, enhanced decision-making, and new revenue streams.

Industry leaders report that MCP not only accelerates development cycles but also provides a critical framework for embedding robust security, governance, and compliance. From ensuring fine-grained user-centric permissions to enabling real-time policy enforcement and auditability, MCP transforms AI agents from potential risks into trusted collaborators within enterprise ecosystems.

Furthermore, the growing marketplace of interoperable MCP servers and connectors fuels innovation, democratizing AI capabilities across an organization and beyond. Yet, as adoption scales, enterprises must remain vigilant in evolving their governance models — continuously validating connectors, managing permissions, and monitoring telemetry to uphold trust.

With contributions from pioneering organizations like Salesforce, Microsoft Azure, Anthropic, HubSpot, and more, it is clear that MCP’s journey is just beginning. The protocol’s early success, coupled with insights on challenges and best practices, provide a blueprint for enterprises seeking to navigate the complex but rewarding path toward trustworthy AI deployment.

As MCP adoption broadens, enterprises stand poised to reap the full benefits of AI-powered agents — automating complex tasks, unlocking hidden insights, and redefining the future of work, all while maintaining security and control.