TL;DR
AI systems don’t operate anonymously.
Every AI tool, integration, and automation relies on an identity that determines what it can access, what actions it can take, and how long that access persists. In modern SaaS environments, these identities are rarely human. They take the form of service accounts, API keys, OAuth tokens, delegated permissions, and application identities that exist quietly in the background.
AI identity security focuses on securing those non-human AI identities.
As organizations expand their use of generative AI, embedded SaaS features, and AI-powered integrations, non-human identities are becoming one of the most powerful and least governed access paths in the enterprise.
What is Shadow AI?
Shadow AI refers to AI tools, features, agents, or integrations that operate outside formal approval, inventory, or governance processes.Shadow AI can include:
- Standalone AI tools adopted by employees
- Built-in AI features enabled by default in SaaS platforms
- AI-powered integrations and workflows
- AI agents and automations created without security review
- API-driven AI services embedded into applications
Unlike traditional shadow IT, shadow AI often has direct access to sensitive data and operates continuously rather than on demand.
What is AI Identity Security?
AI identity security refers to the controls and governance practices used to manage how AI systems authenticate, inherit access, and operate across SaaS applications.It includes securing:
- Service accounts used by AI tools and integrations
- OAuth tokens and delegated permissions
- API keys tied to AI workloads
- Application identities created by SaaS platforms
- Ownership and identity lifecycle management for AI access
AI identity security is a core component of AI IAM and machine identity security, extending traditional identity programs to cover non-human actors that operate continuously.
Why Does AI Create a New Identity Problem?
Traditional identity programs were designed around people.
They assume:
- Interactive login
- Explicit user intent
- Periodic access reviews
- Ownership tied to an employee
AI breaks these assumptions.
AI identities:
- Authenticate programmatically
- Operate without user interaction
- Inherit access indirectly through integrations
- Are frequently created outside security-owned workflows
- Rarely expire unless explicitly revoked
As a result, AI identity risk grows quietly through service account sprawl, stale credentials, and delegated access that no longer reflects business intent.
What are the Common Types of Non-Human AI Identities?
Service Accounts
Many AI systems rely on service accounts with broad access to SaaS applications and data. These accounts are often shared across workflows and rarely reviewed after creation.
OAuth Tokens and Delegated Permissions
AI integrations frequently use OAuth to access SaaS platforms on behalf of users or applications. These tokens can persist long after the original use case changes.
API Keys
AI workloads often depend on API keys embedded in scripts, applications, or automation tools. These keys are typically long-lived and difficult to inventory.
Application and Integration Identities
SaaS platforms may automatically create application identities when AI features or integrations are enabled. These identities can span multiple services with limited visibility.
Where Does AI Identity Risk Emerge?
Over-Permissioned Access
AI identities are commonly granted broad permissions upfront to avoid breaking functionality. Over time, this creates standing access that exceeds what is actually required.
Unclear Ownership
When AI identities are created by developers, operations teams, or SaaS defaults, accountability is often missing. Without an owner, access is not reviewed or adjusted.
Cross-SaaS Reach
A single AI identity may interact with email, documents, CRM platforms, ticketing systems, and file storage at the same time. This significantly increases blast radius.
Stale and Forgotten Credentials
API keys and tokens tied to AI workloads often outlive the projects they were created for, creating long-term exposure.
Why Does Traditional IAM Fall Short for AI Identities?
Most identity and access management programs focus on users.
They enforce controls such as:
- SSO
- MFA
- Role-based access
- User lifecycle workflows
These controls do not map cleanly to non-human AI identities, which do not authenticate interactively and do not fit traditional role models.
Without explicit AI identity governance, organizations struggle to answer:
- Which AI identities exist today?
- What access do they have?
- Why was that access granted?
- Who is responsible for reviewing it?
How Do You Secure Non-Human AI Identities?
Effective AI identity security starts by treating AI as an identity rather than a feature.
Core practices include:
- Maintaining continuous visibility into non-human identities used by AI systems
- Enforcing least-privilege access for service accounts, tokens, and integrations
- Establishing clear ownership for every AI identity
- Reviewing access as workflows and integrations evolve
- Aligning AI identity governance with broader SaaS security programs
AI identity security focuses on who AI is and how it authenticates, while AI monitoring focuses on what AI does over time. Both are necessary and complementary.
How Does AI Identity Security Support Broader AI Governance?
Strong AI identity controls enable:
- Safer deployment of AI agents and automations
- More effective AI monitoring and anomaly detection
- Reduced AI data leakage risk
- Improved compliance and audit readiness
- Faster remediation when access drifts beyond intent
Without identity governance, other AI security controls become reactive.
Why Does AI Identity Security Matter Now?
AI systems are becoming permanent actors inside SaaS environments. As their reach expands, understanding how they authenticate and what they can access becomes critical.
AI identity security provides the structure needed to govern non-human identities intentionally rather than discovering risk after exposure occurs.
To see how teams gain visibility into AI identities, understand inherited access, and address risk using a variety of remediation options including automated workflows, book a personalized demo today.
Frequently Asked Questions
1
What is a non-human AI identity?
2
How is AI identity security different from securing AI agents?
3
Why are AI identities difficult to track?
4
Do non-human AI identities create compliance risk?
5
Can AI identity security be enforced without disrupting workflows?
