This guide explains the four areas where AI creates new access risk, and how CloudConnexa addresses each one.
Agentic AI security risks: why AI agents need a different approach
AI agents don't just answer questions. They call APIs, query databases, read internal documents, and take actions without a person approving every step. That makes them powerful, and it makes their access a security decision.
Three things make agents riskier than traditional software:
- They can be manipulated. A prompt injection hidden in an email, document, or ticket can instruct an agent to do something its owner never intended.
- They act at machine speed. A misconfigured agent can make thousands of requests before anyone notices.
- They often get too much access. Teams frequently connect agents to a VPN or network segment with access to everything on it, because it's quicker than scoping access properly. OWASP calls this risk excessive agency.
The principle that fixes all three is least privilege: an agent should be able to reach only what its job requires, so that if it's tricked or misbehaves, the damage is contained. As one recent post on this blog put it, your AI agent should get less network access than your intern.
What is a Zero Trust Application Broker?
A Zero Trust Application Broker (ZTAB) connects an authenticated user or device to specific, authorized applications, without joining that device to the network. In CloudConnexa, each authorized application is reached through its own intermediary IP address, so the real server IP and your internal network layout stay hidden. A device that isn't authorized for an application has no route to it at all.
For AI, this matters because the same mechanism works for people, laptops running AI tools, and autonomous AI agents. Each one gets access to named applications, not to a network. For a closer look at the architecture, see how CloudConnexa works as an identity-aware proxy as well as a full network.
How AI agents get an identity in CloudConnexa
Every connection to CloudConnexa is authenticated, whether it comes from a person or a machine. There are two patterns:
- An AI tool used by a person (such as an AI assistant or coding tool on a laptop): the person connects with the OpenVPN Connect app and signs in with SAML, LDAP, or username and password. The AI tool can reach only what that person is authorized to reach.
- An autonomous AI agent running unattended on a server or VM: the machine connects as a Host Connector (on Linux, it runs the OpenVPN 3 client) and authenticates with the certificate in its own connection profile, using mutual TLS. That certificate is the agent's identity, and you can grant, limit, or remove access for that identity alone.
The four areas of agentic AI security
1. Securing MCP servers
The Model Context Protocol (MCP) lets AI assistants connect to internal tools and data. An exposed MCP server is a direct path into company systems. CloudConnexa keeps MCP servers off the public internet and reachable only by authorized users and agents, using domain-based routing so private subnets are never sent as routes. For control at the MCP layer itself, we're building MCP Gateway.
2. AI agent identity and least-privilege access
Autonomous agents need their own identity and tightly scoped access. CloudConnexa gives each agent a certificate-based identity and limits it to specific applications with Access Groups, which deny by default. If an agent misbehaves, you can remove its access without touching anyone else's. Standards bodies are working on the same problem: NIST's NCCoE has a project on software and AI agent identity and authorization.
3. Shadow AI control
Employees and agents can send company data to AI services you haven't approved. CloudConnexa's Cyber Shield lets you block unapproved AI services by using the Domain Block List as a custom category, while keeping approved tools available. Because each Shield policy is assigned to User Groups, different teams can have different rules.
4. AI coding tools and device posture
AI coding assistants work with the developer's access to source code. CloudConnexa can make access to repositories and internal dev services conditional on the laptop meeting your security requirements through device posture checks on OS version, antivirus, disk encryption, and client certificates, both when the connection starts and periodically during the session.
How to secure AI agents: a starting checklist
- List the AI agents and AI tools in use, and what systems each one touches.
- Give every autonomous agent its own identity instead of a shared or human account.
- Grant each identity access to named applications only, with everything else denied by default.
- Take MCP servers and internal tools off the public internet.
- Block AI services you haven't approved, and allow the ones you have.
- Require a healthy device before developer laptops reach source code.
- Review who and what accessed each application with Access Visibility, or stream logs to your SIEM.
What CloudConnexa does and doesn't do for AI security
Being clear about scope helps you build the right stack.
CloudConnexa controls who and what can reach each application, enforced by identity, for people, devices, and AI agents. It hides internal applications from the internet, limits lateral movement, blocks unapproved destinations, and checks device posture.
CloudConnexa does not inspect the content of AI traffic. It doesn't read prompts, inspect MCP or agent-to-agent messages, or catalog the AI agents in your environment. If you need content-level controls, pair CloudConnexa with tools built for that purpose. If MCP-level control matters to you, tell us what you need from MCP Gateway.
For most SMB and mid-market teams, access control is the right place to start: it's simple to deploy, and it limits the damage any single agent or compromised account can do.
Frequently asked questions
What is agentic AI security?
Agentic AI security is the practice of controlling what autonomous AI agents can access and do, so that a manipulated or misconfigured agent can't reach sensitive systems or data. It applies Zero Trust principles, such as verified identity and least-privilege access, to AI agents.
What are the biggest AI agent security risks?
The most common are prompt injection (an agent follows hidden instructions in content it reads), excessive access (an agent can reach far more than its task requires), and speed (a misbehaving agent can make thousands of requests before anyone notices). Exposed MCP servers and unapproved AI services add to the risk.
Can a VPN secure AI agents?
A traditional VPN puts a device on the network, which usually gives an agent far more access than it needs. A Zero Trust approach like CloudConnexa's Zero Trust Application Broker connects the agent to specific authorized applications only, so it can't reach anything else.
How does an AI agent authenticate to CloudConnexa?
An agent running unattended on a server or VM connects as a Host Connector and authenticates with mutual TLS, using the certificate in its OpenVPN connection profile. That certificate serves as the agent's identity. An AI tool used by a person on a laptop uses that person's sign-in, through SAML, LDAP, or username and password.
Does CloudConnexa inspect AI prompts or MCP messages?
No. CloudConnexa controls which applications each user, device, or agent can reach. It does not inspect prompt content or the contents of MCP or agent-to-agent messages.
How do I block unapproved AI tools?
In CloudConnexa, add the AI services' domains to the Domain Block List in a Shield policy, which works as a custom filtering category, and assign that policy to the User Groups it should apply to. Approved AI tools stay reachable.
Is CloudConnexa suitable for small businesses?
Yes. CloudConnexa is a cloud-delivered service with a free plan, so small and mid-sized teams can secure access to applications, including AI tools and MCP servers, without running their own infrastructure.