A governance model that focuses on the behaviour of AI systems themselves, not just the humans or accounts around them. It defines what the AI may request, infer, or do, and it becomes necessary when AI moves from assisting governance to participating in identity-related decisions or actions.
Expanded Definition
IGA for AI is the extension of identity governance and administration to autonomous or semi-autonomous AI systems that can request access, infer context, and trigger actions. Unlike traditional IGA, which governs human users and service accounts, this model treats the AI agent or model workflow as a governed actor with defined boundaries. That matters when an AI can call tools, read sensitive data, or initiate identity-related changes in systems such as IAM, PAM, or ticketing workflows.
In practice, IGA for AI sits between policy design and runtime enforcement. It asks what the AI is allowed to see, which identities or secrets it may request, and which actions require human approval or step-up controls. Definitions vary across vendors, and no single standard governs this yet, so organisations often combine principles from the NIST Cybersecurity Framework 2.0 with internal AI governance rules. NHI Management Group treats this as an operational control problem, not a conceptual one.
The most common misapplication is treating AI agents like ordinary application integrations, which occurs when teams assign broad service-account privileges without reviewing the AI’s decision pathways.
Examples and Use Cases
Implementing IGA for AI rigorously often introduces approval latency and policy complexity, requiring organisations to weigh automation speed against the risk of uncontrolled AI actions.
- An AI support agent requests customer identity attributes to resolve account recovery, but must be limited to approved fields and logged for review.
- An internal compliance copilot drafts access reviews, yet cannot approve its own recommendations or alter RBAC assignments without human sign-off.
- An AI workflow connected to secrets scanning identifies a leaked token, but is only permitted to open a remediation ticket, not rotate production credentials directly.
- An identity operations agent proposes JIT elevation for a developer, but policy requires context checks before the request is sent to PAM.
- For threat context, NHI teams tracking the patterns described in the DeepSeek breach use IGA rules to prevent an AI from surfacing or reusing exposed secrets during incident handling, a concern reinforced by NIST Cybersecurity Framework 2.0 guidance on governed access.
In maturing environments, IGA for AI also governs whether an agent can infer identity confidence scores, recommend privilege changes, or escalate cases into human queues. Those decisions should be bounded by policy because inference can become action when the model is wired into downstream automation.
Why It Matters in NHI Security
IGA for AI matters because AI systems can become high-speed intermediaries between identity data, secrets, and privileged workflows. If the AI is over-permissioned, it can expose credentials, recommend excessive access, or trigger administrative changes without the friction that usually slows human misuse. That risk is amplified when AI tools are connected to ticketing, code repositories, and cloud control planes, where one weak policy can scale into broad identity compromise.
This is not a theoretical concern. In NHIMG research, the LLMjacking report shows attackers moving quickly once credentials are exposed, and the State of Secrets in AppSec research highlights how fragile secrets handling remains across organisations. One relevant stat from that research is that only 44% of developers are reported to follow security best practices for secrets management, which increases the odds that an AI system will ingest or mishandle sensitive material.
For NHI governance, the core issue is not whether the model is intelligent but whether its authority is constrained, observable, and revocable. Organisations typically encounter the need for IGA for AI only after an agent has already exposed data, overreached on permissions, or created a remediation incident that identity teams must unwind.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Agentic AI guidance covers autonomy, tool use, and human approval boundaries. | |
| OWASP Non-Human Identity Top 10 | NHI-04 | AI governance depends on controlling how machine identities request and use access. |
| NIST CSF 2.0 | PR.AC-4 | Access permissions management aligns with governing what AI systems may access or trigger. |
Treat AI agents as governed identities and enforce least privilege, logging, and revocation.
