10 Identity Security Vendors for AI Agents: Strengths, Tradeoffs and How They Fit

2026-07-21 · Source: Aembit · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

This guide compares 10 prominent identity security vendors addressing the unique challenges posed by AI agents, which strain traditional human-centric Identity and Access Management (IAM) systems. Vendors approach this problem through workload IAM, non-human identity (NHI) governance, privileged access management (PAM), secrets management, and extensions to human IAM. The article details offerings from Aembit, Astrix Security, Entro Security, Oasis Security, Veza from ServiceNow, Idira (formerly CyberArk), HashiCorp Vault, Microsoft Entra, Okta, and SailPoint. It emphasizes that buyers must evaluate how products authenticate workloads, handle credentials, preserve human-agent context, enforce policy at runtime, and maintain audit records, noting that most enterprises will require a combination of these specialized solutions rather than a single platform.

Key takeaway

For AI Architects designing identity and access management for agentic AI systems, you must move beyond human-centric IAM, recognizing that a single vendor solution is unlikely to suffice. Prioritize solutions that authenticate active workloads, manage dynamic credentials, preserve human-agent context, and enforce policies at runtime. Your strategy should integrate specialized workload IAM, NHI governance, and secrets management to cover the full access path. Failing to integrate these diverse controls will leave critical security gaps.

Key insights

AI agents necessitate a multi-faceted identity security approach, integrating workload IAM, NHI governance, and runtime enforcement beyond traditional human-centric systems.

Principles

• Traditional human IAM is insufficient for autonomous agents.
• Agent identity security requires a multi-vendor stack.
• Runtime policy enforcement is distinct from governance.

Method

Evaluate AI agent identity solutions by asking: how is the workload authenticated, how are credentials handled, is human-agent context preserved, where is policy enforced, and what audit records remain.

In practice

• Implement workload IAM for runtime access control.
• Utilize secrets managers for dynamic credential issuance.
• Integrate IGA for agent ownership and entitlement reviews.

Topics

• AI Agent Security
• Identity and Access Management
• Workload Identity
• Non-Human Identity Governance
• Runtime Authorization
• Secrets Management

Best for: CTO, VP of Engineering/Data, AI Security Engineer, AI Architect, Director of AI/ML

Related on AIssential

• Identiverse 2026 Recap: Identity Security For Agentic AI Dominates — Identity security must rapidly adapt to govern autonomous AI agents and nonhuman identities with real-time, context-aware decisions.
• Squares and Rectangles: Why AI Agents Can’t Be Treated Like Regular Workloads — AI agents require a distinct "blended identity" security model, not traditional workload or user identity, due to their dynamic, non-deterministic behavior.
• Agentic Runtime Security Explained: Securing Non‑Human Identities — Agentic AI security demands dynamic, non-human identity management to prevent widespread vulnerabilities.
• Your identity stack was built for two kinds of actor. Agents are a third. — Existing IAM systems fail to secure AI agents due to their non-deterministic nature and dynamic runtime behavior.
• Congratulations to Astrix Security on Their Anticipated Acquisition by Cisco — Non-human identities and AI agents represent a critical, expanding security perimeter often overlooked by traditional IAM.
• AIMS: A Model for AI Agent Identity — AI agents need first-class identities, treated as workloads, utilizing existing identity standards for scalable security.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Aembit.