Your Identity and Access Management (IAM) program wasn't built for this. When you designed your IAM controls around human users, service accounts were a manageable exception. Now AI agents are requesting API access, machine learning pipelines need privileged credentials, and automated workloads are proliferating faster than you can document them.
This checklist helps you audit your current identity controls and build a governance framework that covers human, machine, and AI identities without creating a compliance bottleneck.
Checklist Overview
You'll assess whether your Identity Security program can handle three identity classes: human users (employees, contractors, third parties), machine identities (service accounts, API keys, certificates), and AI agents (autonomous systems, ML workloads, agentic processes). Each checklist item maps to Zero Trust Architecture principles and connects to Privileged Access Management requirements.
Use this to identify gaps before your auditor does, or before an AI agent with excessive privileges becomes your next incident root cause.
Prerequisites
Before you start, gather:
- Your current IAM and Privileged Access Management policies
- Inventory of service accounts and API keys (if you have one)
- List of AI tools, agents, or automated decision-making systems in production
- Role-Based Access Control matrix
- Privileged account documentation
If you don't have an AI agent inventory, that's your first gap.
Identity Security Checklist
1. Identity Discovery and Classification
☐ You maintain a complete inventory of all identity types
This means a single source of truth that includes employees, contractors, service accounts, API keys, certificates, and AI agents. Each identity has a documented owner, purpose, and lifecycle stage.
Good practice: An identity register that auto-updates from your directory services, cloud platforms, and CI/CD pipelines, with weekly reconciliation against actual access grants. When an auditor asks "how many machine identities do you have?", you answer in seconds, not days.
2. Lifecycle Management for Non-Human Identities
☐ Machine and AI identities follow the same joiner-mover-leaver controls as human users
Every service account, API key, and AI agent has a provisioning approval, periodic access review, and deprovisioning trigger. No identity persists indefinitely without revalidation.
Good practice: When a development team decommissions a microservice, the associated service accounts are automatically flagged for removal within 24 hours. Your access review process includes machine identities, not just people.
3. Privileged Access Governance
☐ You enforce the Principle of Least Privilege across all identity types
AI agents don't get admin rights by default. Machine identities can't access production databases unless there's a documented business justification. Every privileged credential has time-boxed access and audit logging.
Good practice: An AI agent that processes customer data has read-only access scoped to specific tables, with credentials that rotate every 12 hours. Your Privileged Access Management system treats bot accounts the same way it treats your database administrators.
4. Zero Trust Architecture Alignment
☐ Every identity authenticates and authorizes for every access request
No "trusted" network zones where machine identities bypass authentication. AI agents don't inherit the permissions of the user who launched them. Every API call requires a valid token, even from internal systems.
Good practice: Your API gateway enforces mutual TLS for all service-to-service communication. Machine identities authenticate using short-lived certificates, not static passwords. When an AI agent requests data, it proves its identity and receives the minimum necessary access.
5. Credential Security for Machine Identities
☐ No hard-coded credentials in code repositories, containers, or configuration files
API keys, database passwords, and service account credentials are stored in a secrets management system with encryption at rest and in transit. Credentials rotate automatically without manual intervention.
Good practice: Developers retrieve secrets from a vault at runtime using workload identity federation. Your CI/CD pipeline scans for exposed credentials and blocks deployments that fail the check. When you audit last quarter's commits, you find zero plaintext passwords.
6. Continuous Monitoring and Anomaly Detection
☐ You monitor machine and AI identity behavior for deviations from baseline
Unusual API call volumes, unexpected privilege escalation, or off-hours access by automated systems trigger alerts. Your Security Operations team can distinguish between legitimate automation and compromised credentials.
Good practice: When a service account that normally queries 100 records per hour suddenly requests 10,000, your SIEM generates an alert within minutes. AI agent activity appears in the same audit logs as human user activity, with the same retention and review requirements.
7. Segregation of Duties for AI Operations
☐ The team deploying AI agents doesn't control the privileged access those agents receive
Access requests for AI workloads go through the same approval workflow as requests for human users. No single administrator can create an AI agent and grant it database admin rights without oversight.
Good practice: Your data science team submits access requests through a ticketing system. The Identity Security team validates business justification before provisioning credentials. Audit logs show who approved what access for which AI agent.
8. Identity Governance Reporting
☐ Your compliance reports include machine and AI identities, not just human users
When you certify access for ISO/IEC 27001 or SOC 2 Type II, the evidence includes all identity types. Your risk register accounts for machine identity compromise, not just phishing and password attacks.
Good practice: Your quarterly access review presents three separate tabs: human identities, machine identities, AI agents. Each shows access grants, last authentication, and risk score. Your auditor doesn't ask "but what about service accounts?" because the answer is already in the documentation.
Common Mistakes
Treating service accounts as exceptions. If your IAM policy says "these controls apply to users" and stops there, you've created a gap. Rewrite policies to say "these controls apply to all identities."
Assuming AI agents are just another application. An autonomous agent that makes decisions without human intervention needs tighter governance than a static application. Don't map it to your legacy "system account" category.
Skipping machine identity access reviews. "It's automated, so we don't need to review it" is how you end up with 400 orphaned service accounts holding privileged access. Review everything.
Ignoring credential sprawl in development environments. Developers create API keys for testing, then promote them to production. If your secrets management doesn't cover dev and staging, you're missing the source of most credential leaks.
Next Steps
Run this checklist quarterly, not once. As your organization adopts more AI tools and automated workflows, your identity attack surface expands. The controls that worked when you had 500 human users and 50 service accounts won't scale when you're governing 500 human users, 2,000 machine identities, and 75 AI agents.
If you checked fewer than six items, start with identity discovery. You can't govern what you can't see. If you checked six or seven, focus on lifecycle management and privileged access controls. If you checked all eight, document your process and prepare to defend it during your next audit.
Your IAM program needs to evolve as fast as your AI adoption. This checklist gives you a measurable way to track that evolution.
