Identity, access management, and non-human identities in the AI era

Identity Security & Non‑Human Access

The AI era continues to revolutionize Identity and Access Management (IAM), driving a paradigm shift that extends far beyond traditional human-centric models. As artificial intelligence evolves into autonomous, agentic entities capable of self-directed actions, organizations face unprecedented challenges in governing both human and non-human identities (NHIs). Recent market developments, security incidents, and emerging technologies underscore the urgency for enterprises to adopt zero standing privilege, continuous behavioral monitoring, and secure AI-native IAM frameworks.

Evolving Challenges in AI-Driven Identity and Access Management

The foundational shift in IAM is driven by the rise of agentic AI—autonomous AI agents that perform complex tasks such as reconnaissance, lateral movement, privilege escalation, and even decision-making without human intervention. Traditional IAM models, designed around static human identities and fixed permissions, struggle to keep pace with these dynamic, machine-speed actors.

Key Developments Shaping the AI Era IAM Landscape

Agentic AI as First-Class Identities: Modern IAM frameworks now fully recognize AI agents as distinct identity types requiring equivalent lifecycle governance to humans. This includes authentication, authorization, and adaptive access policies that dynamically adjust based on real-time agent context and behavior.
Zero Standing Privilege as a Standard: To mitigate risks from persistent AI agents, IAM and Privileged Access Management (PAM) solutions increasingly enforce zero standing privilege (ZSP). Instead of granting persistent elevated privileges, access is provisioned transiently and revoked immediately after task completion. Companies like Venice Security are pioneering real-time ZSP architectures tailored for autonomous AI identities, dramatically reducing attack surfaces.
Continuous Behavioral Monitoring: Platforms such as Hush Security and LayerX Security have advanced continuous anomaly detection that monitors AI agent behaviors at runtime. This machine-speed monitoring is critical to detect unusual privilege escalations or lateral movements that static policies alone cannot catch.
Automated NHI Lifecycle and Secrets Management: The complexity of managing AI agents at scale demands automation in onboarding, credentialing, rotation, and deprovisioning. Tools demonstrated by Evolveum at recent IAM summits highlight AI-driven acceleration of Identity Governance and Administration (IGA) workflows, reducing manual errors and operational friction.
API Security and Prompt-Level Controls: Given the pervasive use of APIs by AI systems, especially for cloud services and AI model interactions, IAM strategies now embed granular API gateway policies and cloud-native Web Application Firewalls (WAFs) with prompt-level restrictions. This approach mitigates emerging attack vectors such as prompt injection and AI-driven file upload exploits. Solutions like Imperva’s cloud WAF exemplify this next-generation API defense.
Secure AI Development Pipelines: Security now extends into AI model development environments. Tools like Cloud Range’s AI Validation Range and Checkmarx’s AI-powered code security enforce access controls and vulnerability scanning to protect against adversarial attacks such as model poisoning or unauthorized autonomous code execution.
Hybrid Endpoint and Unified Endpoint Management (UEM): The proliferation of hybrid human-AI devices—ranging from AI assistants to autonomous bots—necessitates integrated IAM and endpoint protection frameworks. UEM solutions are evolving to secure these composite endpoints against advanced infostealer malware and unauthorized access.

Recent Market and Security Incidents Highlighting IAM Imperatives

Several recent events and research findings have spotlighted the fragility and complexity of AI-era identity security:

Claude Code Security Market Impact

On February 20, 2026, Anthropic’s release of Claude Code Security sent ripples through the cybersecurity landscape by emphasizing that the current security focus on perimeter defense is insufficient. Claude Code Security advocates shifting attention toward protecting codebases and AI workloads themselves, rather than just network or endpoint layers. This approach aligns with the emerging consensus that code-level vulnerabilities and misconfigurations are primary attack vectors in AI deployments.

“We’re defending the wrong thing,” said an industry insider, underscoring the need for IAM to integrate secure CI/CD and AI validation pipelines that continuously verify the integrity of AI models and their operational environments.

‘Silent’ Google API Key Change Exposed Gemini AI Data

In a notable incident, a silent Google Cloud API key rotation triggered unintentional exposure of sensitive data related to Google’s Gemini AI. Normally used as simple billing identifiers, these API keys were scraped from websites and exploited by attackers.

This event exposed a new dimension of risk in API key and credential management, especially as AI agents rely heavily on APIs for data exchange, orchestration, and function invocation.
It underscores the critical need for IAM to incorporate robust secrets management, API key lifecycle controls, and real-time monitoring to prevent credential leakage and unauthorized access.

Application Security Must Start at the Load Balancer

Security researchers and practitioners emphasize that permissive load balancers remain a top attack vector for breaches, particularly in cloud-native AI applications.

Many organizations optimize load balancers for speed, inadvertently creating trust zones that attackers exploit.
Effective IAM strategies now extend to infrastructure-layer protections, including advanced load balancer configurations and cloud-native WAFs that enforce zero trust policies at the network ingress point.
This infrastructure-level control is essential to prevent AI-driven exploitation attempts that bypass traditional endpoint defenses.

Strategic Recommendations for AI-Native IAM in 2026 and Beyond

To navigate the evolving AI landscape, organizations should adopt a holistic, AI-native IAM strategy encompassing the following pillars:

Extend Zero Trust Governance to Non-Human Identities: Enforce dynamic, context-aware access policies that cover both human users and agentic AI entities, eliminating standing privileges and minimizing lateral movement opportunities.
Deploy Unified IAM/PAM Platforms with AI-Native Capabilities: Leverage platforms designed for zero standing privilege, continuous behavioral monitoring, and automated NHI lifecycle management to streamline governance and reduce risks.
Automate NHI Credentialing and Secrets Rotation: Use AI-driven tools to manage credentials and secrets at scale, preventing stale or leaked secrets from becoming attack vectors.
Integrate API Gateways and Prompt-Level Controls: Implement granular API security measures and prompt injection defenses to safeguard AI workflows and data exchanges.
Embed Secure AI Development Pipelines: Adopt continuous validation and hardened CI/CD pipelines that include AI model integrity checks, vulnerability scanning, and access controls to prevent adversarial manipulations.
Leverage AI-Powered SOC Analytics: Equip Security Operations Centers with AI-enhanced telemetry to detect anomalous AI identity behaviors, privilege escalations, and lateral movements in real time.
Adopt Adaptive Multi-Factor Authentication (MFA) and Continuous Access Validation: Defend against AI-enhanced credential stuffing and brute force attacks by layering adaptive, context-driven MFA with ongoing access reassessment.

Conclusion: IAM as a Strategic Enabler in the AI Era

The confluence of AI’s rapid maturation and the rise of autonomous, agentic identities demands a fundamental rethinking of identity and access management. Organizations can no longer afford to treat AI agents as mere extensions of human users or overlook the operational velocity and complexity they bring.

By embracing converged IAM frameworks that treat non-human identities as first-class citizens, enforce zero standing privilege, and integrate continuous behavioral analytics, enterprises position themselves not only to defend against sophisticated AI-driven threats but also to unlock new levels of innovation and agility.

As the AI-native cybersecurity landscape unfolds through 2026 and beyond, mastering IAM—including robust privileged access management, automated lifecycle controls, and secure AI development pipelines—will be a critical strategic differentiator for future-ready enterprises.

Notable Vendors and Market Movers

Venice Security: Leading the charge on real-time zero standing privilege for autonomous AI identities with innovative PAM solutions.
Hush Security: Pioneering unified access management platforms that dynamically govern AI-driven NHIs.
Evolveum: Showcasing AI-accelerated Identity Governance and Administration workflows to automate NHI lifecycle management.
Claude Code Security: Driving a market shift toward securing AI codebases and workloads.
Imperva: Delivering cloud-native WAFs with prompt-level API controls to defend AI application layers.
Gambit Security and Glow: Benefitting from significant venture capital inflows, signaling robust confidence in AI-native identity security innovation.

In this dynamic environment, the organizations that proactively evolve their IAM strategies to fully integrate and secure agentic AI and non-human identities will emerge resilient and competitive, transforming identity security from a defensive necessity into a strategic asset powering tomorrow’s digital ecosystems.

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