Introduction
Artificial intelligence is rapidly moving from simple chatbots to autonomous AI agents that can browse websites, access files, send emails, interact with software and complete multi-step tasks on behalf of users. While these capabilities can improve productivity, they are also creating a familiar cybersecurity problem in a new form- who controls the credentials and permissions given to these systems?
Security researchers and organizations are increasingly focusing on AI agent identity because traditional identity and access management systems were largely designed around human users and conventional software. The National Institute of Standards and Technology (NIST) has specifically warned that giving AI agents access to data, applications and tools creates new identity and authorization challenges.
The result is a situation that resembles the early days of password security. Organizations may be giving powerful digital entities access to sensitive systems before they have fully developed the infrastructure needed to manage, monitor and revoke that access.
Why AI Agents Need Digital Identities
An AI agent is different from a conventional chatbot.
A chatbot generally responds to a question. An AI agent can take action. It might read a company’s database, update a customer record, create a software ticket, send an email or interact with an external application.
To perform these actions, the agent needs some form of authentication and authorization.
That could involve API keys, access tokens, service accounts, delegated user credentials or other forms of machine identity.
Microsoft’s security researchers argue that organizations should treat AI agents as first-class security principals, giving each agent a managed identity, explicit roles and tightly scoped permissions.
This is where the security challenge begins.
AI Agents Could Recreate the Credential-Sharing Problem
For decades, cybersecurity professionals have warned people not to share passwords.
The reason is simple- once credentials are shared, it becomes difficult to determine who actually performed an action and whether that person was authorized to do it.
NIST says a similar issue is emerging with AI agents. Its August 2026 analysis notes that users are increasingly giving agents their own credentials to access data, applications and services. According to NIST, credential sharing can create accountability, privacy and legal problems because organizations may no longer have a clear distinction between the human user and the agent acting on that user’s behalf.
This is essentially the old password problem appearing in a new technological environment.
Instead of asking an employee to share a password with another person, organizations could end up allowing an AI agent to operate using a human’s identity.
The Problem With Overprivileged AI Agents
One of the biggest risks is excessive permissions.
Imagine an employee creates an AI agent to summarize customer emails. The agent only needs access to a specific mailbox.
However, instead of receiving narrowly defined permissions, it is given access to the employee’s broader account.
Suddenly, the agent may have access to calendars, documents, cloud storage, internal applications and other sensitive information.
Microsoft highlights this type of problem in its guidance on AI agent security. The company recommends combining identity management, role-based access controls, narrow scopes and safe tool binding to reduce the potential impact of unintended agent actions.
The danger increases when an agent can perform actions rather than simply read information.
An agent with write or deletion privileges can potentially modify records, send messages, delete files or make changes to systems without a human approving every individual action.
Why Traditional IAM Systems Are Being Challenged
Traditional Identity and Access Management (IAM) systems are primarily built around recognizable identities such as employees, contractors, applications and service accounts.
AI agents complicate this model.
An agent may act on behalf of a human, operate autonomously or delegate work to another agent. It may also interact with multiple systems during a single workflow.
NIST’s work on AI agent identity and authorization specifically focuses on developing ways to apply identity standards to software and AI agents because existing approaches may not fully address these new requirements.
The central question becomes-
Who is actually performing the action?
Is it the employee?
The AI agent?
The application hosting the agent?
Or another AI agent that was called during the workflow?
Without a clear answer, security investigations and accountability can become much more difficult.
AI Agents Can Move Across Multiple Systems
The risk isn’t limited to a single application.
An enterprise AI agent might have access to-
Individually, each permission may appear reasonable.
The problem emerges when they are combined.
Microsoft notes that an agent with access to email, files, ticketing systems and code repositories can potentially correlate information across those environments in ways that were not evaluated when each permission was granted separately.
This creates what could be described as permission multiplication.
The agent does not necessarily need one extremely powerful permission. A collection of smaller permissions can create significant effective access.
Prompt Injection Can Become an Identity Problem
AI agent security is not only about passwords and API keys.
Agents make decisions based on information they encounter while performing tasks. That creates another attack vector- prompt injection.
A malicious instruction could be hidden inside a webpage, document, email or other content that an AI agent processes.
If the agent follows that instruction, it could potentially perform an action using its legitimate permissions.
This means that even if an organization’s credentials are technically protected, the agent could still misuse the authority associated with those credentials.
Security researchers increasingly describe this as an intersection between AI security and identity security. Traditional authentication can establish who an agent is, but organizations also need controls that restrict what the agent is allowed to do with that identity.
The Rise of Non-Human Identities
AI agents are part of a broader category known as non-human identities (NHIs).
These include-
The growth of AI is accelerating the importance of this category.
Tenable India has highlighted excessive permissions and dormant machine identities as significant concerns as organizations deploy more AI agents.
Unlike employees, AI agents can potentially operate continuously and at machine speed.
That means an overly broad permission may be used much faster than a human could exploit the same access.
Why Static Credentials Are Particularly Dangerous
Static credentials create another major challenge.
Some agent implementations rely on API keys, bearer tokens or other secrets that remain valid for extended periods.
If such a credential is stolen, an attacker may be able to use it without needing to compromise the original AI model.
The Hacker News has highlighted hardcoded credentials and long-lived secrets in agent environments as important security concerns.
The fundamental problem is familiar from traditional cybersecurity- a credential that lasts too long and has too many permissions creates a larger window for abuse.
AI agents can make that problem more complicated because one credential may be used across several automated workflows.
How Companies Can Secure AI Agents
Organizations deploying AI agents need to treat identity and access management as a core part of AI security rather than an afterthought.
Use Dedicated Agent Identities
Each AI agent should ideally have its own identifiable security principal rather than simply borrowing a human employee’s credentials.
This makes it easier to determine which agent performed an action and who authorized that agent.
Follow Least-Privilege Access
Agents should receive only the permissions necessary for their specific tasks.
An agent designed to summarize emails does not necessarily need permission to delete files or modify financial records.
Microsoft recommends explicit roles and tightly scoped permissions as part of an agent identity security model.
Use Short-Lived Credentials
Where possible, organizations should reduce dependence on permanent secrets and use credentials that expire or can be dynamically issued.
This can limit the potential damage if an access token is compromised.
Monitor Agent Activity
Companies need detailed logs showing-
Without this information, investigating an AI-related security incident can become extremely difficult.
Review Permissions Regularly
AI agents can change over time.
An agent initially designed for a simple task might eventually gain access to additional tools and applications.
Security teams should therefore periodically review agent permissions and remove access that is no longer required.
Create Approval Controls for High-Risk Actions
Not every action should be fully autonomous.
Organizations can require human approval for sensitive activities such as transferring money, deleting large amounts of data, changing security settings or sending confidential information externally.
AI Security Needs an Identity-First Approach
The growing focus on AI agent identity represents a broader shift in cybersecurity.
For years, organizations concentrated heavily on protecting networks, applications and devices. Identity subsequently became one of the most important security layers.
AI agents are now creating another identity challenge.
NIST says organizations need to treat agents as distinct entities with unique identifiers, credentials and associated permissions rather than simply allowing them to operate through shared human credentials.
That approach can improve accountability while limiting the potential consequences of an agent behaving unexpectedly.
The Future of AI Agent Security
The number of AI agents operating inside businesses is likely to grow as companies automate customer service, software development, research, administration and other workflows.
That growth makes identity management increasingly important.
The security industry is already developing new approaches for agent identity, authorization and runtime protection. Google Cloud, Microsoft and NIST are among the organizations working on frameworks and controls specifically designed for the agentic AI environment.
The goal is not to prevent AI agents from accessing systems altogether.
Instead, organizations need to make sure every agent has a clearly defined identity, limited authority and an auditable trail of activity.
Conclusion
AI agents promise to transform how businesses use software, but their ability to act autonomously creates a cybersecurity challenge that looks surprisingly familiar.
The industry spent decades trying to solve problems created by weak passwords, shared credentials and excessive permissions. AI agents risk recreating some of those same problems if businesses simply hand them broad access to existing accounts.
The emerging solution is an identity-first approach to AI security.
Every AI agent should have a clear identity, limited permissions, appropriate authentication, continuous monitoring and the ability to have its access revoked.
As AI moves from answering questions to taking real-world actions, knowing who or what is acting, what it is allowed to do and who authorized it will become just as important as protecting the AI model itself.
Kode Digital Blog is a technology-driven platform sharing insights on software development, AI, digital marketing, and business growth.
© 2026 Kode Digital. All rights reserved.
