As the CEO of a cybersecurity company, I hear a recurring theme from enterprise CISOs and CIOs: AI is moving faster than our ability to secure it. A year ago, most concerns focused on generative AI models, their data sources, and prompt injection. Today, the emergence of autonomous AI agents and the Model Context Protocol (MCP) has radically expanded the attack surface.

What used to be a single-user error or model hallucination can now cascade across dozens of systems in seconds. MCP enables agents to connect to tools, APIs, and databases — often without human oversight. While this integration unlocks powerful automation, it also raises fundamental questions about visibility, control, and accountability.

Here are five of the top questions CISOs are asking about AI agent and MCP security — and our recommendations on how to approach them.

1. MCP servers are popping up everywhere. How do we know if they’re secure?

The problem:
The Model Context Protocol (MCP) has quickly become the connective layer between AI agents and enterprise systems. It dramatically simplifies integration, allowing agents to connect to thousands of tools with minimal configuration. But with that convenience comes risk: there’s now an explosion of MCP servers, from both trusted vendors and unverified developers. Each one acts as a new dependency — and a potential entry point for attackers.

The implications:
This is a supply chain risk in motion. An unvetted MCP server could expose sensitive data, inject malicious instructions, or redirect agent outputs. Without visibility into which MCP servers are active, CISOs can’t enforce governance or patch vulnerabilities before they spread across AI workflows.

PointGuard AI’s recommendation:
Establish a baseline of MCP security hygiene. PointGuard AI automatically detects, catalogs, and evaluates MCP servers based on configuration, authentication methods, and security posture — helping organizations maintain a trusted MCP inventory.

➡ Learn more about MCP and agent discovery

2. How can we discover and create an inventory of AI agents, MCP servers, models, and connected applications?

The problem:
Traditional IT asset management tools were never built for AI. In most enterprises, there’s no single source of truth for AI agents, models, or MCP connections. Developers may spin up test agents that persist long after their use, or models may quietly connect to third-party tools through MCP without centralized logging. This creates the equivalent of “AI shadow IT.”

The implications:
Without an accurate inventory, CISOs are operating blind. Unknown agents can inadvertently handle sensitive data, connect to insecure MCP servers, or access regulated systems. The lack of visibility undermines compliance, auditing, and risk assessment efforts.

PointGuard AI’s recommendation:
Start with visibility. PointGuard AI provides a unified view of the AI ecosystem — agents, models, MCP servers, and APIs — allowing security teams to see what exists before defining policy or control. A clear inventory is the foundation of any sustainable AI governance model.

➡ Explore AI Discovery and inventorying solutions

3. What frameworks do you recommend for managing and governing AI security?

The problem:
AI innovation has moved faster than enterprise governance. Security teams are building policies for models, data, and agents — but without common standards, every organization is improvising. This leads to uneven protection, unclear accountability, and confusion about where “AI security” truly begins and ends.

The implications:
Without recognized frameworks, AI governance becomes reactive — responding to incidents rather than anticipating them. Teams may secure model endpoints but miss prompt-layer threats, or monitor data pipelines without understanding agentic behavior. Fragmentation makes it nearly impossible to communicate risk in a consistent or auditable way.

PointGuard AI’s recommendation:
Anchor your AI security strategy to established frameworks. The OWASP Top 10 for LLM Applications and OWASP Agentic Threats identify core vulnerabilities. MITRE ATLAS catalogs real-world AI attack techniques and mitigations. The NIST AI Risk Management Framework (AI RMF) offers an enterprise structure for governance, and the OWASP GenAI Incident Response (IR) Guide provides actionable playbooks for detecting, containing, and recovering from AI-related incidents. Together, they create a cohesive foundation for AI risk management and continuous assurance.

➡ Read more about the OWASP Top 10 for LLMs

4. What controls exist to monitor and contain agents that can spawn or collaborate with other agents?

The problem:
AI agents can now create, coordinate, or delegate tasks to other agents automatically. This inter-agent collaboration increases efficiency but poses a serious governance challenge. Without clear containment boundaries, one agent can trigger another to act outside its intended scope.

The implications:
Unchecked agent-to-agent communication can lead to privilege escalation, data exposure, or runaway task execution. In complex MCP environments, an agent might unintentionally pass data or instructions across security zones, expanding the attack surface exponentially.

PointGuard AI’s recommendation:
Adopt continuous agent observability. PointGuard AI provides guardrails to monitor agent interactions and limit agent chaining based on policy, context, and trust level. This ensures agents collaborate safely without compromising enterprise boundaries.

➡ Learn more about agentic runtime guardrails

5. How do we monitor agents for anomalous agent behavior?

The problem:
AI agents don’t produce standard telemetry. They generate dynamic interactions, learned behaviors, and decision outputs that traditional monitoring tools can’t interpret. Without a clear audit trail, it’s difficult to detect when an agent goes off-script or begins operating outside of expected parameters.

The implications:
A lack of observability means security incidents can develop unnoticed. An agent could start making unauthorized API calls, leaking data, or self-modifying its logic — all without triggering traditional alerts. For enterprises deploying AI at scale, this creates unacceptable operational and reputational risk.

PointGuard AI’s recommendation:
Implement continuous behavioral telemetry for models and agents. PointGuard AI is developing tools to capture each interaction, identifies anomalies, and enforce policy controls — enabling proactive response before an issue becomes an incident.

➡ Learn about AI Observability

Conclusion

AI security is evolving from protecting data to governing behavior across interconnected, autonomous systems. The rise of AI agents and the Model Context Protocol accelerate innovation but also exposes enterprises to new categories of operational risk — from unverified MCP servers to uncontrolled agent behavior.

For CISOs and CIOs, the path forward isn’t about slowing innovation. It’s about establishing visibility, accountability, and control within the AI ecosystem. Every connection, from a prompt to an MCP call, must be validated, monitored, and governed with the same rigor as any other enterprise system.

At PointGuard AI, our mission is simple: secure your path to AI adoption — giving organizations the confidence to embrace intelligent automation without compromising trust.

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