The Question at Hand
Your security team manages user accounts with established rigor: provisioning workflows, periodic access reviews, multi-factor authentication, and privileged session monitoring. Now you're deploying AI agents that query databases, call APIs, and make decisions across your environment. Do these machine identities deserve the same controls you apply to human users, or do they need something entirely different?
This isn't just an academic question. AI agents, cloud workloads, and automated processes are proliferating faster than most identity governance programs can track. Each one carries credentials, accesses systems, and creates audit trails. The debate centers on whether your existing Identity and Access Management (IAM) framework scales to this new reality, or whether AI identities demand a fundamentally different approach.
Treating AI Identities as a Distinct Category
Many security architects argue that AI and machine identities operate under different constraints than human users, and forcing them into traditional IAM frameworks creates more problems than it solves.
Machine identities don't have managers to approve access requests. They don't take vacations or change roles. An AI agent that processes invoices doesn't need a quarterly access review; it needs runtime policy enforcement that validates every action against its intended function. Traditional Role-Based Access Control assumes relatively static job functions, while AI agents shift workloads dynamically based on demand, context, and task complexity.
You also can't apply the same authentication patterns. Multi-factor authentication is designed for verifying a human's intent to access a system. An AI agent calling an API thousands of times per hour needs cryptographic attestation, not push notifications. The Principle of Least Privilege still applies, but the implementation looks different: you're scoping permissions to specific API endpoints and data sets, not broad departmental access.
Practitioners in this camp point out that machine identities outnumber human identities in most enterprises already. Treating them as special cases within your user directory creates noise in access reviews, bloats your privileged account inventory, and obscures the signals you need to detect anomalous human behavior. They argue for separate identity planes: one for people, governed by HR processes and user lifecycle management, and another for machines, governed by application deployment pipelines and runtime security policies.
Unified Identity Governance
The opposing view holds that creating separate identity frameworks for humans and machines fractures your security control plane exactly when you need integration most. Identity has become the common thread across users, systems, and AI because attackers don't distinguish between credential types when they're moving laterally through your environment.
If you govern AI identities outside your core IAM platform, you lose centralized visibility. Your Security Operations Center can't correlate a compromised service account with the AI agent that's using it. Your audit team can't trace a data access event back to the business process that triggered it. Zero Trust Architecture depends on continuous verification of every identity, and that verification model breaks down when half your identities live in shadow registries that don't feed your policy engine.
Practitioners who favor unified governance point out that the controls themselves aren't that different. Yes, an AI agent needs different authentication methods than a human user, but your IAM platform already handles service accounts, API keys, and certificate-based authentication. The governance questions remain identical: Who provisioned this identity? What can it access? When was it last validated? Has its behavior deviated from baseline?
The real risk, they argue, is that treating AI identities as special cases exempts them from the governance rigor you've built around human accounts. You end up with AI agents running with standing privileges that would never pass a SOC 2 Type II audit if they were assigned to a person. Your Privileged Access Management program monitors admin sessions but ignores the machine identity that has write access to your customer database.
This camp also notes that your compliance obligations don't distinguish between human and machine access. When you're demonstrating compliance with ISO/IEC 27001 control A.9.2.1 (user registration and de-registration), your auditor will ask about all identities with system access. If your AI identities live in a separate governance framework, you're explaining why half your attack surface isn't covered by your certified controls.
Where Practitioners Actually Land
In practice, most security teams are converging on a hybrid model: unified governance with identity-type-specific controls.
You maintain a single source of truth for all identities, whether human or machine. Every AI agent, service account, and automated process gets registered in your identity platform with clear ownership, business justification, and scope. This gives you the centralized visibility and audit trail that compliance frameworks require.
But within that unified framework, you apply different control patterns. Human identities flow through HR-driven provisioning and periodic recertification. Machine identities flow through application deployment pipelines with automated lifecycle management. An AI agent's "access review" might be a runtime policy check that validates its actions against its declared purpose, not a quarterly attestation from a business owner.
The key insight is that the governance questions stay consistent even when the implementation varies. For any identity, you need to answer: What is this identity's purpose? What's the minimum access required to fulfill that purpose? How do we verify it's being used as intended? How do we detect misuse? Your IAM platform provides the framework; your identity-specific controls provide the enforcement mechanisms.
Our Take
Treat AI identities as first-class citizens in your identity governance program, but don't force them into user-centric workflows that don't fit their operational model.
Your existing IAM framework already handles the governance layer: identity registration, entitlement management, audit logging, and policy enforcement. Extending that framework to AI identities gives you the visibility and control you need without fragmenting your security architecture. When you're demonstrating compliance with NIST Cybersecurity Framework (CSF) 2.0 or responding to an incident, you can't afford to have half your identities managed in a separate system.
But recognize that AI identities need runtime controls, not periodic reviews. Implement Just-in-Time Access for machine credentials. Use cryptographic attestation instead of interactive authentication. Build policy enforcement into your deployment pipelines so that an AI agent's permissions are validated every time it's instantiated, not once a quarter when someone remembers to check.
The tradeoff is operational complexity. You're maintaining multiple control patterns within a single governance framework, and that requires tooling that can handle both human and machine identity lifecycles without creating manual bottlenecks. But the alternative, managing AI identities outside your core security controls, creates blind spots that attackers will exploit. Identity has become the enterprise security control plane because it's the one layer that touches every access decision. Don't exempt your fastest-growing identity population from the governance that makes that control plane effective.
