Artificial Intelligence (AI) is rapidly transforming how organizations operate, make decisions, and interact with customers. The emergence of Generative AI, Large Language Models (LLMs), and more recently Agentic AI has introduced a new category of digital entities capable of performing tasks autonomously, accessing enterprise data, making decisions, and interacting with systems on behalf of humans.
While traditional cybersecurity programs have focused on protecting human users, applications, and infrastructure, organizations must now address a new challenge: Who governs AI identities and what access should AI agents have?
Identity and Access Management (IAM) and Identity Governance and Administration (IGA) are becoming foundational components of AI security. Just as human identities require authentication, authorization, lifecycle management, and governance, AI systems and autonomous agents require similar controls to ensure secure, compliant, and accountable operations.
Understanding AI and Agentic AI
Traditional AI systems generally operate within predefined boundaries. They analyze data, generate predictions, or provide recommendations but typically do not act independently.
Agentic AI represents the next evolution. These systems can:
Plan and execute multi-step tasks
Make autonomous decisions
Interact with multiple applications
Access enterprise resources
Initiate workflows
Collaborate with other AI agents
Perform actions on behalf of users
Examples include:
AI-powered service desk agents
Autonomous procurement assistants
HR onboarding agents
Security investigation assistants
Financial reconciliation agents
IT operations copilots
As these agents gain greater autonomy, they effectively become a new category of enterprise identity.
Why AI Needs Identity Management
Every AI system interacts with enterprise resources through some form of identity.
Consider an AI assistant that:
Reads employee records
Accesses HR systems
Creates tickets in ITSM platforms
Sends emails
Updates databases
Without proper identity controls, the AI may receive excessive permissions, creating significant security and compliance risks.
Organizations must answer critical questions:
What identity represents the AI agent?
Who approved its access?
What data can it access?
How are permissions reviewed?
Who is accountable for its actions?
When should access be revoked?
These are traditional IAM and governance questions applied to a new digital workforce.
AI Identities: The New Non-Human Identity
Historically, IAM programs focused on:
Human identities
Service accounts
Application identities
Privileged accounts
AI introduces a rapidly growing category known as Non-Human Identities (NHIs).
Examples include:
AI assistants
Autonomous agents
Machine learning models
RPA bots
API-based agents
LLM-integrated applications
Industry analysts predict that non-human identities will eventually outnumber human identities by several orders of magnitude.
Each AI identity requires:
Unique identification
Authentication
Authorization
Monitoring
Governance
Lifecycle management
Key IAM Challenges in Agentic AI
1. Excessive Privileges
Many organizations grant AI agents broad access to simplify implementation.
Examples:
Full database access instead of filtered access
Administrative API permissions
Shared service accounts
Unrestricted cloud permissions
This violates the principle of least privilege and increases the potential impact of compromise.
2. Identity Sprawl
As organizations deploy hundreds or thousands of AI agents, identity sprawl becomes inevitable.
Challenges include:
Untracked AI accounts
Orphaned agents
Duplicate identities
Unused credentials
Shadow AI deployments
Without governance, AI identities can become the next generation of unmanaged service accounts.
3. Accountability and Attribution
When an AI agent performs an action:
Who initiated it?
Who approved it?
Which data influenced the decision?
Who is responsible for errors?
Strong identity correlation is necessary to establish accountability.
4. Dynamic Decision-Making
Unlike traditional applications, Agentic AI can adapt its behavior based on context.
Static access models become insufficient because:
Permissions may need to change dynamically.
Risk levels may vary by task.
Access decisions may require contextual evaluation.
5. Third-Party AI Integrations
Organizations increasingly integrate external AI platforms.
Examples include:
SaaS copilots
Cloud AI assistants
External LLM providers
These integrations create additional risks around:
Data exposure
Credential management
Access control
Regulatory compliance
Identity Governance for AI
Identity Governance and Administration (IGA) provides the framework needed to manage AI identities throughout their lifecycle.
AI Identity Lifecycle
The lifecycle should include:
Request
Business owners request deployment of an AI agent.
Approval
Appropriate stakeholders review:
Purpose
Data requirements
Risk level
Compliance impact
Provisioning
The AI receives:
Unique identity
Approved roles
API credentials
Secrets and certificates
Monitoring
Activities are continuously tracked and analyzed.
Certification
Managers periodically review:
Access levels
Usage patterns
Business justification
Deprovisioning
When the AI is retired:
Access is removed
Credentials are revoked
Secrets are rotated
Audit records are preserved
Applying Least Privilege to Agentic AI
The principle of least privilege is critical for AI security.
AI agents should receive only the permissions necessary to perform assigned tasks.
For example:
An HR onboarding agent may require:
✓ Create user accounts
✓ Assign standard roles
✓ Generate welcome emails
But should not receive:
✗ Payroll administration access
✗ Executive HR records access
✗ Domain administrator privileges
Modern IAM platforms should support:
Fine-grained authorization
Role-based access control (RBAC)
Attribute-based access control (ABAC)
Just-in-time access
Risk-based access controls
Privileged Access Management (PAM) for AI
Some AI agents require elevated permissions.
Examples include:
Infrastructure automation agents
Security response agents
Cloud operations assistants
These agents should be managed through Privileged Access Management (PAM) controls.
Recommended practices include:
Credential vaulting
Secret rotation
Session monitoring
Just-in-time privilege elevation
Privileged session recording
Approval workflows
AI should never have permanently assigned privileged credentials whenever temporary elevation is possible.
AI Governance and Compliance
Regulators increasingly focus on AI accountability.
Organizations must demonstrate:
Who authorized AI access
What data AI can access
How decisions are monitored
Whether controls prevent misuse
Key governance requirements include:
Auditability
Every AI action should be traceable.
Transparency
Access decisions should be explainable.
Segregation of Duties
AI agents should not bypass established controls.
Data Minimization
Agents should access only necessary information.
Regulatory Compliance
Support for regulations such as:
GDPR
HIPAA
PCI-DSS
AI governance frameworks
Emerging AI regulations
Zero Trust for AI Agents
The traditional model of implicit trust is incompatible with Agentic AI.
Organizations should adopt Zero Trust principles:
Verify Explicitly
Authenticate every AI interaction.
Use Least Privilege
Grant minimum required permissions.
Assume Breach
Continuously monitor AI activities.
Continuous Validation
Evaluate identity, context, and risk before granting access.
Every AI request should be treated as potentially risky until verified.
Future of IAM in the AI Era
The future IAM landscape will extend beyond human users to include millions of machine and AI identities.
Emerging capabilities will include:
AI identity discovery
Autonomous access reviews
Risk-based AI authorization
Behavioral monitoring for AI agents
AI-specific governance workflows
Machine identity lifecycle management
Autonomous policy enforcement
Organizations that establish strong identity governance foundations today will be better positioned to leverage AI safely and at scale.
Conclusion
AI and Agentic AI are creating a new digital workforce capable of interacting with enterprise systems, making decisions, and executing actions autonomously. As these systems gain access to critical business resources, identity becomes the primary control plane for security and governance.
The same principles that govern human access—authentication, authorization, least privilege, lifecycle management, privileged access controls, auditing, and compliance—must now be extended to AI identities.
Successful organizations will treat AI agents not merely as applications but as governed digital identities with clearly defined responsibilities, monitored activities, and controlled access. In the era of autonomous systems, robust Identity and Access Management will be the cornerstone of trustworthy and secure AI adoption.
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