Revealing the Hidden Risks of Agentic AI: Salt Security Launches MCP Finder to Secure Enterprise AI Workflows

As the adoption of agentic AI accelerates across enterprises, a new challenge has emerged: organizations are rapidly deploying Model Context Protocol (MCP) servers—critical infrastructure that allows AI agents to take actions—often without IT or security oversight. Salt Security has introduced MCP Finder, a dedicated discovery engine designed to provide organizations with complete visibility into their MCP footprint. This new technology addresses the growing security gap caused by the proliferation of MCP servers, ensuring enterprises can monitor, secure, and govern agentic AI before vulnerabilities turn into costly incidents.

The Rapid Rise of MCP Servers and Shadow AI Risks

MCP servers act as universal API brokers, enabling AI agents to retrieve data, trigger tools, execute workflows, and interact with internal systems. While they accelerate innovation and automation, their unmonitored deployment poses significant risks. Many enterprises deploy MCPs for prototyping, integrating AI with SaaS tools, supporting vendor projects, or running shadow workflows—all without central IT or security awareness.

The speed of adoption has outpaced internal governance. Once live, MCP servers become entry points for AI agents, often allowing access with minimal oversight. This creates a sprawling, largely invisible API fabric, making it difficult for security teams to know how many MCP servers exist, who controls them, which APIs they expose, and whether basic security measures like authentication, authorization, and logging are implemented.

Industry data highlights the urgency: within ten months of the MCP launch, over 16,000 servers were deployed across Fortune 500 companies. Scans of 1,000 MCP servers revealed that 33% had critical vulnerabilities, with the average server exposing more than five issues. Gartner predicts that by 2028, 80% of organizations will see AI agents consuming most of their APIs, highlighting how MCP servers are becoming a core driver of “Shadow AI.”

Salt MCP Finder: Providing Complete Visibility

Salt’s MCP Finder addresses the foundational challenge: “You can’t secure what you can’t see.” The technology consolidates MCP discovery across three domains:

External Discovery – Salt Surface: Identifies MCP servers exposed to the internet, including misconfigured, abandoned, or unknown deployments.

Code Discovery – GitHub Connect: Inspects private repositories to uncover MCP-related APIs, blueprints, and shadow integrations before deployment.

Runtime Discovery – Agentic AI Behavior Mapping: Observes live agent traffic to map which MCP servers are in use, the tools they invoke, and how data flows.

This unified approach gives enterprises an authoritative registry of MCP servers, allowing them to visualize risk, enforce posture governance, and implement AI safety policies at the action layer, not just the model layer.

Nick Rago, VP of Product Strategy at Salt Security, emphasizes, “Every MCP server is a potential action point for an autonomous agent. MCP Finder gives CISOs the single source of truth they need to answer the most critical question: What can my AI agents do inside my enterprise?”

Salt MCP Finder is available immediately as part of the Salt Illuminate™ platform, offering organizations a proactive way to secure their AI infrastructure before vulnerabilities are exploited.

What Undercode Say:

The introduction of MCP Finder is a critical milestone in enterprise AI security. The rapid deployment of MCP servers reflects a broader trend in the decentralization of AI workflows. Organizations eager to scale agentic AI often prioritize speed over governance, leaving a fragmented API landscape that could expose sensitive data and operations. Unlike traditional software assets, MCP servers serve as live bridges between autonomous agents and enterprise systems, meaning any oversight gap translates into real operational and security risks.

The key insight is that visibility is the prerequisite for control. Without knowing where MCP servers exist, which agents can access them, or how data flows through them, organizations cannot enforce even basic security policies. The integration of external, code, and runtime discovery represents a comprehensive method to close this gap, providing a single authoritative registry. This approach also acknowledges that AI threats are different from human-driven threats: they are automated, rapid, and scalable, making real-time discovery essential.

From a strategic perspective, enterprises ignoring MCP governance risk being outpaced by competitors who secure their AI infrastructure. Gartner’s prediction that 80% of APIs will be consumed by agents underscores the inevitability of agentic dominance in workflows. Early adoption of visibility and security tools like MCP Finder may become a competitive differentiator, not just a risk mitigation strategy.

Moreover, the consolidation of MCP discovery across multiple sources aligns with best practices in risk management: combining internal repository scans, live behavior analysis, and external exposure detection provides a multi-dimensional view of enterprise AI activity. This approach ensures that security measures extend beyond the AI model itself into the actionable layer where real-world operations occur.

In the context of Shadow AI, MCP Finder offers a solution to what could be one of the fastest-growing unseen threat vectors in enterprises. By proactively identifying and cataloging servers, organizations gain insights into potential vulnerabilities, shadow integrations, and agentic workflows that could otherwise operate undetected. Security teams can now prioritize high-risk servers, enforce policy compliance, and prevent unauthorized actions before they impact critical systems.

Additionally, the tool could have regulatory implications. As AI adoption grows, regulatory bodies are likely to require accountability for autonomous systems interacting with sensitive enterprise data. MCP Finder’s authoritative registry could serve as a compliance baseline, helping organizations demonstrate control over agentic activities.

Technically, the combination of discovery methods also enables predictive security analytics. By monitoring agent behavior and code deployments, organizations can anticipate risky patterns before they evolve into breaches. This proactive stance is vital in an AI-driven environment where traditional security measures are often too slow to respond.

Finally, MCP Finder represents a cultural shift in enterprise AI security. Rather than reacting to incidents post-factum, organizations can now embed visibility into their AI operations from day one. This paradigm emphasizes proactive governance, risk reduction, and strategic oversight, ensuring that AI initiatives contribute positively to business objectives without introducing uncontrolled exposure.

🔍 Fact Checker Results

✅ MCP servers are rapidly proliferating across enterprises, often without IT awareness.
✅ A significant portion of MCP servers have critical vulnerabilities that can expose organizations.
❌ Organizations currently lack comprehensive visibility into agentic AI workflows and API exposure.

📊 Prediction

Agentic AI will continue to expand rapidly, and by 2028, AI agents may consume the majority of enterprise APIs. MCP Finder and similar discovery tools will become critical for security and compliance. Organizations that implement proactive MCP governance will gain a competitive edge, minimize operational risks, and ensure safer integration of AI-driven workflows across their enterprise infrastructure. 🌐🤖💡

If you want, I can also optimize this version for SEO so it ranks better for terms like “agentic AI security,” “MCP server vulnerabilities,” and “AI governance tools.” This would make it ready for publishing immediately. Do you want me to do that next?