NHI and the Rise of AI agents: The Security Risks Enterprises Can’t Ignore

AI Comes with Automatic Risks

Enterprises are rapidly adopting AI and LLM technologies to enhance efficiency, automation, and decision-making. More organizations are integrating AI workflows, AI agents, and agentic AI into their daily corporate operations and production environments. We see that AI agents are already being used in customer support, where chatbots handle customer inquiries, access relevant and sensitive data, and reduce response times, and in enterprise security, where AI-driven security agents detect and respond to threats in real time. This is just the start as AI is becoming an integral part of modern business operations.

Looking ahead, the way enterprise code is built is changing. More and more applications are being designed to integrate AI agents directly into business processes and decision-making. The enterprise workforce is evolving as well, with a significant portion soon consisting of AI agents operating alongside human employees and reshaping workflows at scale. Enterprise leadership has set clear strategic goals to leverage AI more extensively, and AI transformation, making AI agents a core part of innovation and efficiency efforts. However, this transformation raises a key question: What must we consider as security teams when asked to support this change?

Non-human identities (NHI) are intrinsically linked to AI agents. As the identity fabric of enterprises shifts, organizations must reevaluate their NHIs to ensure they are governed, secured, and integrated into identity management frameworks.

In this blog, we will examine the different types of AI that enterprises are using in production, the security risks associated with their NHIs, and how organizations can address these risks while enabling AI-driven innovation.

Understanding AI Workflows, AI Agents, and Agentic AI

AI-driven automation and decision-making fall into three main categories: AI workflows, AI agents, and agentic AI. Each serves a different purpose within an enterprise but all three share a common thread—they all require non-human identity governance and security. AI workflows automate tasks, AI agents make decisions within defined boundaries, and agentic AI adapts its objectives dynamically. Because AI interacts with business-critical systems, it introduces new identity challenges, including tracking, securing, and governing these non-human identities (NHI) properly. Let’s break down these three main categories.

AI Workflows

AI workflows are structured automation sequences that follow predefined rules to streamline business processes. They typically use AI for data processing, pattern recognition, or classification but do not make independent decisions. Instead, they execute if-then logic based on programmed conditions.

📌 Enterprise Use Cases:

• IT Ticketing Systems – AI automates ticket routing based on issue type and severity.
• HR Onboarding – AI assigns system access based on predefined employee roles.
• Fraud Detection Pipelines – AI flags anomalies but does not take action without human oversight.

AI Agents

AI agents take automation further by making real-time decisions within a predefined scope. Unlike workflows, they can adjust behavior based on data inputs, allowing them to operate independently while still respecting established parameters.

📌 Enterprise Use Cases:

• AI Security Agents – Detects threats and autonomously blocks suspicious activities.
• Financial Risk Analysis AI – Adjusts fraud detection thresholds dynamically.
• Supply Chain AI – Modifies logistics routes based on external factors like weather and demand.

Agentic AI

Agentic AI represents the most advanced level of AI, where systems not only make decisions but also set their own goals and evolve dynamically. These AI systems operate with minimal human intervention, learning from new data and optimizing their own processes.

📌 Enterprise Use Cases:

• AI-Driven DevOps Automation – Adjusts infrastructure deployments without human input.
• Self-Learning Risk Management AI – Modifies compliance policies based on evolving regulatory requirements.
• Autonomous Business Process AI – Optimizes workflows by redefining how different teams interact.

Comparison Table: AI Workflows vs. AI Agents vs. Agentic AI

Security Risks of AI-Driven Non-Human Identities

Non-human identities make identity governance more complex and less secure in countless ways. Since all AI agents rely on NHIs, they come with those liabilities baked in, along with many that are unique to NHIs assigned to specific types of AI.Before breaking down the unique risks of each type, here are the key challenges that affect all AI-driven identities (NHI):

Common Risks Across All AI Types

1. Discovery & Inventory Management - Many organizations don’t have visibility into how many AI identities exist, where they operate, or what they have access to which creates a dynamic, complex web of access permissions that is hard to track and audit.
2. Identity Governance & Lifecycle Management — AI-driven identities often lack the same oversight as human accounts, leading to over privileged identities and orphaned accounts.
3. Threat Detection - AI-driven identities are prime targets for attackers. Compromised AI accounts can be used to manipulate workflows, bypass security controls, or exfiltrate data.
4. Identifying Human Ownership - Many AI-driven identities lack clear ownership, making it difficult to determine accountability when issues arise.
5. Remediation Challenges - Unlike human accounts, AI-driven identities operate at machine speed. A security incident involving an AI agent could escalate in seconds, requiring real-time response capabilities.
6. Lack of Documentation - Enterprises often fail to document what an AI identity was originally designed to do, leading to confusion when auditing or troubleshooting.

Risks Unique to Each AI Type

AI Workflows - Low Risk but Poor Visibility

• Identity Governance Issue: We often see an AI workflow that operates under shared service accounts, making it difficult to trace accountability.
• Access Control Weakness: If misconfigured, a workflow may grant excessive permissions, exposing sensitive data that unintentionally.
• Lifecycle Management Gap: Organizations often forget to update or retire old workflows, leaving security gaps.

AI Agents - Medium to High Risk

• Overprivileged AI Identities: AI agents are often granted broad access to enterprise systems, leading to privilege creep.
• Compromised AI Agents: Attackers can manipulate AI-driven security tools to ignore threats or alter decision-making logic.
• Unmonitored Autonomous Activity: Without identity monitoring, AI agents can make critical system changes without human oversight.

Agentic AI - High to Critical Risk

• Autonomous Policy Bypass: Agentic AI sometimes can redefine security policies without explicit approval, creating compliance risks.
• Identity Sprawl & Unpredictability: Enterprises may lose track of what agentic AI systems are doing and how their identities evolve.
• Threat Amplification: If an attacker compromises an agentic AI, they could use it to reroute enterprise-wide decision-making or even disable security controls.

What Security Leaders Should Ask Before Adopting AI

AI agents bring efficiency, but they also introduce new identity-related risks that organizations must address before deploying them at scale. Ask these  key questions, grouped into critical security categories, before integrating AI into enterprise environments

Identity Management & Access Control

• Which types of AI-driven identities are we planning to implement (Workflow, AI Agents, Agentic AI), and in which environments will they operate?
• How are AI-driven identities created, authenticated, and monitored?
• Are AI entities following least-privilege principles, or do they have excessive access?
• Who is responsible for managing and maintaining AI-driven identities?

Threat Detection & Risk Mitigation

• Can we detect and respond to compromised AI accounts in real time?
• How do we monitor AI-driven identities for unusual or malicious behavior?

Lifecycle & Ownership

• Do we have a process for decommissioning AI identities when they are no longer needed?
• Is there clear ownership and accountability assigned to AI-driven identities?
• Do we have documentation outlining what each AI identity was designed to do and how it interacts with enterprise systems?

Compliance & Governance

• How do we maintain audit logs and visibility into AI identity activities for compliance and governance?
• Are AI-driven decisions traceable and explainable to meet transparency requirements?
• Are our AI-driven identities aligned with regulatory requirements such as GDPR, HIPAA, and emerging AI governance frameworks?

Security teams must strike a balance between enabling AI-driven innovation and ensuring these new identities do not introduce unmanaged risks. Establishing robust governance, access control, and threat monitoring is essential to adopting AI securely.

How Token Security Solves These Issues

Token Security provides a comprehensive solution designed to address the risks associated with AI-driven identities and help security teams answer the critical questions raised when adopting AI workflows, AI agents, and agentic AI. Our platform ensures that enterprises can fully leverage AI capabilities without exposing themselves to identity-related threats.

At Token, we provide enterprise-grade security for non-human identities, ensuring AI-driven identities are secure, compliant, and properly managed. Our solution includes:

✅ AI Identity Discovery & Visibility – Token automatically maps and categorizes all AI identities across environments, providing full visibility into where they exist, what they can access, and how they are used. This enables security teams to answer critical questions about AI identity ownership and purpose.
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✅ Zero-Trust Access Controls – Token enforces least-privilege principles to ensure AI agents and workflows only have the access necessary to perform their roles. Automated policy enforcement prevents privilege creep, ensuring AI entities do not gain unauthorized access to sensitive resources.
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✅ Behavioral Monitoring & Threat Detection – We continuously monitor AI behavior, using advanced AI-driven anomaly detection to identify unusual access patterns, detect compromised AI accounts, and trigger immediate response actions.
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✅ Automated AI Identity Lifecycle Management – Token automates the entire AI identity lifecycle, ensuring that AI accounts are properly provisioned, tracked, and decommissioned when no longer needed. Security teams can enforce policies to prevent AI identities from lingering beyond their operational necessity.
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✅ Compliance & Governance Support – Providing audit logs, access tracking, and policy enforcement to help enterprises meet governance requirements such as GDPR, HIPAA, and emerging AI regulations. Our platform ensures that AI-driven decisions remain traceable and explainable, supporting security and compliance mandates.

With Token Security, enterprises can embrace AI without introducing identity-related security risks.

Securing AI-Driven Identities is Essential for Innovation

AI is reshaping how enterprises operate, offering unprecedented efficiency and automation. As AI agents become an integral part of the workforce, security teams must adapt to this evolving landscape. The increasing presence of NHIs demands a proactive approach to identity governance, threat detection, and compliance.

Organizations that fail to secure AI-driven identities risk privilege misuse, security breaches, and compliance violations. However, when managed properly, AI adoption can drive innovation without compromising security.

At Token Security, we believe that security should empower AI adoption, not hinder it. By implementing AI identity discovery, zero-trust access controls, continuous monitoring, and automated lifecycle management, enterprises can confidently embrace AI without increasing t

The shift towards AI-driven operations is already happening, and the time to secure AI identities is now— starting with rethinking identity strategies.

Let’s talk about how Token Security can help your enterprise navigate the future of AI identity security. 🚀

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