The Rise of AI Agents and NHI Security Risks Organizations Must Tackle

Read full article here: https://www.token.security/blog/nhi-and-the-rise-of-ai-agents-the-security-risks-enterprises-cant-ignore/?source=nhimg

Enterprises are rapidly adopting AI and LLM technologies to enhance efficiency, automation, and decision-making. AI is no longer confined to research labs, it’s being embedded directly into production environments.

• Customer support - AI chatbots handle inquiries, access sensitive data, and reduce response times.
• Enterprise security - AI-driven agents detect and respond to threats in real time.
• Business processes - AI is being integrated into finance, supply chain, and DevOps automation.

The enterprise workforce itself is shifting. Soon, AI agents will operate alongside humans as digital coworkers, reshaping workflows, making decisions, and interacting with core systems. Leadership has set clear goals to leverage AI for efficiency and innovation, but this transformation raises a critical question:

How do we secure AI-driven non-human identities (NHIs) that now form the backbone of these agents?

Understanding AI Workflows, AI Agents, and Agentic AI

All enterprise AI activity relies on NHIs, identities that authenticate, access, and interact with systems. These fall into three categories:

AI Workflows

Structured automation sequences that follow predefined rules.
Examples: IT ticket routing, HR onboarding, fraud detection pipelines.

• Risk profile - Low adaptability, often use shared accounts, poor visibility into ownership.

AI Agents

Operate independently within defined boundaries, making real-time decisions.
Examples: AI security agents blocking threats, fraud models adjusting thresholds, supply chain rerouting.

• Risk profile - Medium to high risk, prone to over-privileged access and unmonitored activity.

Agentic AI

Autonomous systems that set their own goals, learn, and adapt dynamically.
Examples: Self-learning compliance engines, autonomous DevOps, AI-optimized business processes.

• Risk profile - High to critical risk, unpredictable behavior, policy bypass, and identity sprawl.

The Security Risks of AI-Driven NHIs

• Discovery & Inventory - Lack of visibility into how many NHIs exist and what they access.
• Governance Gaps - Orphaned and over-privileged accounts with no clear owners.
• Threat Detection - Compromised AI accounts can act at machine speed.
• Remediation Challenges - Without lifecycle controls, AI accounts linger far beyond necessity.
• Documentation Failures - Many AI identities lack clear records of purpose or scope.

Unique Risks

• AI Workflows - Low risk, but shared service accounts limit traceability.
• AI Agents - Often over-provisioned, vulnerable to manipulation, and can act autonomously without oversight.
• Agentic AI - The most unpredictable, capable of bypassing policies, amplifying risks, and evolving without governance.

What Security Leaders Should Ask Before Adopting AI

1. Identity Management & Access Control
   • How are AI identities created, authenticated, and monitored?
   • Do they follow least-privilege principles?

2. Threat Detection & Risk Mitigation
   • Can we detect compromised AI accounts in real time?
   • Are we monitoring unusual AI-driven behavior?

3. Lifecycle & Ownership
   • Do we have clear ownership and decommissioning processes for AI identities?
   • Is documentation in place for every AI account?

4. Compliance & Governance
   • Are AI-driven decisions traceable and explainable?
   • Do they align with GDPR, HIPAA, and emerging AI governance frameworks?

How Token Security Addresses The Challange

Token Security provides enterprise-grade protection for AI-driven NHIs, giving organizations the visibility and control they need to embrace AI securely.

AI Identity Discovery & Visibility – Continuous mapping of AI accounts, owners, and access scopes.
Zero-Trust Access Controls – Automated enforcement of least-privilege policies to prevent privilege creep.
Behavioral Monitoring & Threat Detection – AI-driven anomaly detection to flag compromised or unusual AI activity.
Automated Lifecycle Management – Full provisioning-to-decommissioning workflows for AI identities.
Compliance & Governance Support – Audit logs, policy enforcement, and regulatory alignment for explainable AI activity.

Bottom Line

AI is reshaping enterprise operations, but unmanaged NHIs introduce risks that could outweigh the benefits. Organizations that fail to secure AI-driven identities risk privilege abuse, compliance failures, and large-scale breaches.

The solution is clear: treat AI-driven identities with the same rigor as human accounts, discovered, governed, monitored, and secured.

With Token Security, enterprises can scale AI adoption while keeping identity risk under control.