Secure connectivity of AI Agents and identity threats : What business leaders need to know

​When Anthropic officially introduced the Model Context Protocol (MCP) as an open-source standard, the entire tech industry lapped it up as the next big step in the growth of Agent AI adoption. 

The need for AI agents to interact seamlessly with diverse external tools and data sources was growing. By providing a universal, open standard, MCP enables developers to build secure, two-way connections between AI systems and various data repositories, replacing fragmented integrations with a single, streamlined protocol.

“Anthropic’s Model Context Protocol represents a significant advancement in AI integration, offering a standardized approach to connecting AI models with external data sources,” reported Forbes.

It’s an understatement that AI agents – autonomous software entities powered by large language models – are quickly becoming embedded across business operations, from handling DevOps workflows to automating customer support. 

While these agents deliver immense value, they also introduce a new class of identity-based risks that traditional security tools aren’t equipped to handle. The core technologies enabling AI agents, Model Context  Protocol (MCP) and the Token Vaults, also fuel the prospects of serious identity security risks. Here’s how.

Understanding AI agents, MCP, and Token Vaults

AI Agents: The new autonomous workforce

AI agents are no longer simple chatbot interfaces. They can read emails, manage cloud infrastructure, pull analytics reports, and act on behalf of users, without a human in the loop. But with this autonomy comes a problem: how do you control and secure a digital entity that doesn’t have a face, a name,a password, or even MFA?

What Is MCP (Model Context Protocol)?

MCP is the emerging standard that allows AI agents to interact with external applications and systems securely and consistently. Developed initially by Anthropic, it provides a standardized way for AI agents to query systems, access resources, and execute commands: all with context-aware controls.

Think of MCP as the secure communication bridge. The AI agent uses it like a universal translator, converting its task request into a system-specific API call while ensuring security checks are baked into every interaction.

Key features of MCP:

• Context-aware authentication using OAuth 2.1 tokens
• Standardized language for accessing diverse systems
• Dynamic permissioning based on AI task context and user role

What are Token Vaults?

Token Vaults are the AI’s secure key management system. To call APIs on behalf of a user e.g. create a calendar invite, AI agents must securely store, issue, and manage access tokens in real time. Token Vaults:

• Grant short-lived, scoped tokens only when the AI agent needs them
• Handle token refresh and revocation, reducing human intervention
• Abstract credentials, so the AI agent never sees raw usernames, passwords, or API keys
• Work well with OAuth 2.0 flows and is based on open standards 

Together, MCP and Token Vaults allow AI agents to behave like enterprise users: querying systems, initiating actions, and integrating across tools, without compromising access security.

The top three AI identity security risks

Risk occurs when these capabilities are misused. For instance, stolen tokens can lead to the accumulation of privileges or the extension of existing ones. As we know now, AI agents have access to sensitive systems, yet operate differently from human users or services. This creates identity vulnerabilities that security teams must proactively address.

Privilege accumulation

AI agents often start with narrow permissions, but as their roles expand, they quietly accumulate more access rights than necessary. This “AI privilege creep” leads to the creation of stale roles, over-provisioned tokens, and unnecessary exposure.

Why it matters: A compromised or misbehaving AI agent with excessive privileges can alter cloud configurations, read customer data, or escalate its own access, without being flagged.

Prompt injection

AI agents interpret natural language prompts. Attackers can exploit this by feeding malicious instructions that bypass the AI’s built-in safety guardrails, causing it to take unintended actions.

Why it matters: A single misleading prompt can cause an AI agent to output confidential data or trigger a dangerous workflow, without breaching any technical perimeter.

Token theft

Tokens stored or transmitted insecurely can be intercepted or leaked, allowing attackers to impersonate the AI agent. This threat is amplified when AI agents handle access tokens for multiple services.

Why it matters: A leaked token is equivalent to a stolen identity. It allows full access to the target system, bypassing both the AI and the user who authorized it.

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