The hidden identity challenges of deploying AI agents across hybrid environments – Source: securityboulevard.com

As AI agents rapidly move from proof-of-concept to production, enterprises are running headfirst into a new set of challenges — ones that traditional identity and access management (IAM) systems simply weren’t built to solve.

These agents don’t live in a single cloud. They span on-prem infrastructure, edge networks, and public clouds. They interact with both sensitive data and critical infrastructure. And yet, in many organizations, they’re running without any identity governance at all.

In this post, we unpack the core problems enterprises face when deploying AI agents in real-world hybrid environments — problems that demand a new architectural model for identity.

Problem #1: Identity systems aren’t built for where agents actually run

Most IAM tools were designed around two assumptions: cloud-first workloads and human users. But AI agents don’t follow those rules.

They’re deployed across a messy, multi-environment landscape, including:

• Multiple public clouds like Azure, AWS, and GCP

• On-premises systems such as factories, ships, or private data centers

• Disconnected or air-gapped environments that can’t call out to SaaS identity providers

When there’s no unified identity fabric across these domains, teams are left cobbling together insecure workarounds. That often means hardcoded secrets, brittle service accounts, or — worse — unauthenticated agents operating in production.

Problem #2: Cloud-native identity falls apart in air-gapped or regulated environments

In high-stakes industries, cloud adoption isn’t always an option. Some workloads must stay local — permanently.

This includes scenarios where organizations are bound by:

• Regulatory constraints (e.g., PCI DSS, ITAR, NIST 800-171)

• Latency-sensitive applications (e.g., real-time trading, robotics)

• Uptime guarantees (e.g., shipboard systems, safety-critical operations)

When agents have to run on-prem, most modern identity tooling can’t go with them. That breaks everything from token issuance to access enforcement, creating identity gaps that regulators and attackers alike can exploit.

Problem #3: There’s no identity layer for AI agents in hybrid workflows

AI agents now participate in core business workflows, but the identity model hasn’t evolved to support them.

Today’s agent deployments often look like this:

• Operate with over-permissioned OAuth credentials

• Lack an authenticated session linked to a human user

• Execute actions with no runtime policy enforcement or identity guardrails

That leaves organizations flying blind. It also makes it incredibly difficult to detect abuse, enforce policy, or prove compliance when things go wrong — especially when agents move fluidly across hybrid environments.

Problem #4: Enterprises are blind to agent behavior and permissions

Even basic visibility into agent behavior is rare today. Security teams know the risks, but they can’t monitor what they can’t see.

Most agent platforms still don’t provide insight into:

• The full chain of delegation (e.g., which user initiated an agent workflow)

• What scopes or tokens the agent had access to

• Whether the agent operated within policy or went out of bounds

This lack of provenance and observability is a critical failure. It hampers incident response, complicates audits, and leaves organizations exposed — especially as agents gain more autonomy.

Problem #5: No consistent policy or control across hybrid boundaries

Hybrid environments demand coordination. But when identity and policy enforcement are fragmented, control quickly unravels.

Without orchestration, teams are stuck managing identity like this:

• Use brittle static credentials (e.g., hardcoded tokens)

• Duplicate identity and policy configs across environments

• Manually sync logs and enforcement logic across clouds and sites

The result? Trust zones break down. Agents operate outside governance. And security teams are left trying to piece together a consistent access model after the fact — often too late.

Problem #6: Agents will soon outnumber humans… and we’re not ready

The scale of what’s coming is staggering. AI agents are multiplying fast, and they’re not waiting for IAM systems to catch up.

Forecasts suggest enterprises will soon manage:

• 80x more agent identities than human users

• Identity workloads that far exceed current system capacity

• Governance demands that don’t fit login-driven, human-centric models

Without a shift in strategy, enterprises will find themselves buried under a volume of unmanaged machine identities they can’t secure or control.

What’s needed next: A new foundation for agent-first identity

Solving these problems isn’t just about patching IAM gaps. It requires a new approach — one that recognizes AI agents as first-class actors in the identity ecosystem.

What’s needed is a hybrid Identity Orchestration platform that works across:

• Public clouds and private infrastructure

• Agent platforms and legacy systems

• Disconnected and connected environments

• Human and machine identities alike

With orchestration in place, enterprises can regain control — enforcing policy, managing access, and monitoring behavior across every environment agents touch. Without it, the risk surface will only grow with every new deployment.