Artificial intelligence is moving quickly from simple chatbots and AI assistants to systems that can actually perform tasks on their own. AI agents can interact with applications, access information, make decisions, call APIs, and complete different workflows with very little human involvement.
This is useful for businesses, but it also creates a new security challenge.
Every AI agent needs some level of access to systems and data to perform its job. It may need an API key, service account, cloud permission, database credential, or privileged access. This means AI agents are becoming another type of digital identity that security teams need to protect.
For CyberArk professionals, this is an important shift. Privileged Access Management is no longer only about protecting administrator passwords. It is also about understanding how automated and intelligent systems receive, use, and manage access.
What Exactly Is an AI Agent?
An AI agent is a software system that can perform tasks based on a goal or instruction. Unlike a basic application that follows a fixed process, an AI agent can sometimes decide what actions it needs to take and interact with different tools or systems to complete the task.
For example, an enterprise AI agent might:
- Retrieve information from a company database
- Create or update support tickets
- Access business applications
- Call APIs
- Generate reports
- Trigger automated workflows
- Work with cloud services
- Communicate with other software systems
To perform these actions, the agent needs permissions.
And that’s where identity security becomes important.
Why AI Agents Are Different From Traditional Machine Identities
Organizations have been dealing with machine identities for years. Applications, APIs, service accounts, automation bots, and workloads all need credentials.
AI agents create a slightly different challenge because their behavior can be more dynamic.
A traditional application may have a predefined workflow. An AI agent can potentially decide which tools or resources it needs based on the task it is given.
This creates questions that security teams need to answer:
- What identity does the AI agent use?
- What resources can it access?
- How much privilege should it receive?
- How long should that access remain active?
- Can the agent request additional permissions?
- How can its activities be monitored?
- What happens if the agent’s credentials are compromised?
These questions are becoming increasingly important as organizations start using AI agents in production environments.
The Growing Identity Security Problem
One of the biggest concerns is that organizations may create AI agents faster than they can properly secure them.
An organization could have hundreds of AI agents working across different departments. Each agent may have its own credentials, API access, cloud permissions, or connections to internal applications.
If these identities are poorly managed, the attack surface becomes much larger.
For example, an AI agent with excessive permissions could potentially access resources that aren’t necessary for its task. If its credentials are stolen or the agent is manipulated, attackers could potentially use that access to move deeper into the environment.
This is why identity security needs to be considered from the beginning when deploying AI agents.
How CyberArk Professionals Fit Into This
CyberArk professionals already work with many of the technologies needed to address this problem.
Privileged Access Management focuses on controlling and protecting high-risk access. In an AI-driven environment, that concept needs to extend to machine identities and automated workloads as well.
CyberArk professionals may need to understand how to:
- Secure credentials used by AI agents
- Manage privileged machine identities
- Protect application secrets
- Control access to sensitive resources
- Apply least-privilege policies
- Monitor automated activities
- Rotate credentials and secrets
- Reduce excessive permissions
This makes identity security knowledge even more valuable for professionals working with CyberArk.
Least Privilege Becomes Even More Important
Least privilege is one of the most important security principles when dealing with AI agents.
An AI agent should not automatically receive administrator-level access just because it needs to perform a particular task.
Instead, organizations should determine exactly what the agent needs and provide only those permissions.
For example, if an AI agent only needs to read information from a specific database, there may be no reason to give it permission to modify the database or access other systems.
This reduces the possible impact if something goes wrong.
CyberArk professionals can play an important role in designing and implementing these access controls.
Protecting AI Agent Credentials
Credentials are another major concern.
AI agents may use passwords, API keys, tokens, certificates, and other secrets to connect with business systems. Storing these credentials directly in code or configuration files can create serious security risks.
A centralized secrets management approach can help organizations protect these credentials.
Instead of exposing a secret permanently, the AI agent can retrieve the required credential securely when it needs it.
This approach can also make credential rotation and monitoring easier.
Monitoring AI Agent Activity
Traditional identity security often focuses on human users. AI agents require a different level of visibility because they can perform actions automatically and at a much higher speed.
Security teams need to understand what an AI agent is doing and whether its behavior is expected.
Monitoring can help answer questions such as:
- Which systems did the agent access?
- What resources did it request?
- When did it access them?
- Was the access expected?
- Did the agent suddenly start accessing new resources?
- Was a privileged credential used?
This type of visibility becomes important for detecting suspicious activity early.
AI Agents and Zero Trust
AI agents also fit directly into the growing Zero Trust security model.
Zero Trust means organizations should not automatically trust an identity simply because it belongs to an approved application or internal system.
The same principle should apply to AI agents.
Every access request should be evaluated based on factors such as identity, permissions, resource sensitivity, and context.
Identity security provides the foundation for making these decisions.
This is another reason CyberArk professionals who understand Zero Trust principles can be valuable in AI-focused security teams.
Why CyberArk Training Is Changing
As identity security evolves, CyberArk training also needs to go beyond traditional PAM concepts.
Learning how to create a vault, manage privileged accounts, and rotate passwords is important. But modern security environments require professionals to understand a much wider identity landscape.
CyberArk professionals should increasingly understand the following:
- Machine identity security
- Secrets management
- Cloud identity
- Non-human identities
- API security basics
- AI workload security
- Least-privilege access
- Zero Trust principles
- Privileged access monitoring
These skills can help professionals better understand how identity security works in modern enterprise environments.
What Organizations Should Do Now
Organizations don’t need to wait until AI agents become a major security problem.
Some practical steps include:
Identify AI agents:
Maintain visibility into which AI agents are being used across the organization.
Define ownership:
Every AI agent should have an accountable owner.
Limit permissions:
Give agents only the access they actually require.
Protect secrets:
Avoid storing credentials directly in source code or configuration files.
Monitor activities:
Track privileged and sensitive actions performed by AI agents.
Review access regularly:
Permissions should be reviewed and removed when they are no longer required.
Plan for credential rotation:
Secrets and credentials should have proper lifecycle management.
These practices can help reduce identity-related risks as AI adoption grows.
Career Opportunities for CyberArk Professionals
The growth of AI doesn’t make traditional identity security skills less important. In many ways, it makes them more valuable.
Companies will need professionals who understand how identity security works across humans, applications, machines, cloud workloads, and AI agents.
This can create opportunities in roles such as the following:
- CyberArk Administrator
- PAM Engineer
- Identity Security Engineer
- IAM Specialist
- Cloud Security Engineer
- Cybersecurity Engineer
- Privileged Access Consultant
- Identity Security Consultant
For professionals planning to enter this field, CyberArk training can provide a strong foundation in privileged access and identity security concepts.
The professionals who continue learning beyond traditional PAM and understand emerging areas like machine identities and AI security can have an additional advantage.
The Future of Identity Security Is Becoming More Automated
AI agents are still developing, and organizations are only beginning to understand how they will be used at scale.
But one thing is already becoming clear: the number of non-human identities will continue to grow.
Applications, automation bots, cloud workloads, APIs, and AI agents will all need secure identities and controlled access.
This means identity security professionals will have an increasingly important role in making sure these systems can work without creating unnecessary security risks.
CyberArk professionals are well positioned for this change because privileged access management is already closely connected to the problem of controlling high-risk identities.
Conclusion
AI agents are changing the way organizations think about identity security.
These systems can perform tasks, communicate with applications, access data, and make decisions with limited human involvement. To do all this, they need identities and permissions.
That creates a new challenge for security teams.
CyberArk professionals will need to think beyond traditional human privileged accounts and understand how machine identities, secrets, cloud workloads, automation, and AI agents should be secured.
As AI adoption continues, organizations will need stronger identity controls, better visibility, and carefully managed privileged access.
For cybersecurity professionals, this is also an opportunity. Building skills in CyberArk, PAM, machine identity security, and AI-era identity protection can help prepare them for the next stage of enterprise cybersecurity.