Higher education should treat IAM and PAM as a single governance problem, not separate tools. Start with a holistic inventory of people, service accounts, groups, and systems, then remove orphaned access and policy exceptions. As AI identities emerge, apply provisioning, deprovisioning, and privileged access controls with the same discipline used for human users and sensitive institutional systems.
Why IAM and PAM Need to Change in Higher Education
Higher education environments are unusually mixed: faculty, students, researchers, contractors, cloud services, lab systems, and now AI-driven workloads often share the same identity plane. That makes a split between IAM and PAM less useful than a single view of who or what can access institutional data, infrastructure, and sensitive research. Cloud migration increases the number of privileged paths, while compliance requirements raise the cost of letting access drift, linger, or go unaudited.
The practical issue is not only theft of human credentials. It is also the unmanaged growth of service accounts, API keys, automation identities, and AI identities that can act faster than humans can review. NHIMG research has found that only 19.6% of security professionals express strong confidence in securely managing non-human workload identities, which is a useful warning sign for universities moving quickly into cloud and AI adoption. In practice, institutions usually discover the weakness when audit evidence is incomplete or when a cloud permission problem has already spread across multiple systems.
How IAM and PAM Work in Practice for AI, Cloud, and Compliance
For higher education, the right design starts with identity inventory, not with tool selection. Universities need a complete picture of human users, privileged administrators, service accounts, machine identities, research automation, and AI identities that can call systems or make changes. Once that inventory exists, IAM should manage lifecycle, group membership, authentication strength, and recertification, while PAM should govern elevation, session control, and just-in-time privileged access for the highest-risk actions.
Cloud migration changes the control surface because access is no longer anchored to a single campus directory or network boundary. Access policies must follow the workload and the data, whether the target is a SaaS platform, a cloud subscription, or a research pipeline. That means short-lived credentials, scoped roles, and exception handling that is documented and reviewable. The NIST Cybersecurity Framework 2.0 remains useful here because it helps institutions organise governance, protection, detection, and recovery around identity-driven risk, and the NIST Cybersecurity Framework 2.0 is a practical anchor for aligning those functions.
AI identities add a new requirement: access must be bounded by intent and context, not by static role assumptions alone. If an AI agent can open tickets, change configurations, or query student or research records, then its privileges should be treated as operationally sensitive and reviewed on a lifecycle basis. Many institutions are also finding that 67% still rely heavily on static credentials despite the risks they pose to agentic AI deployments, which shows why dynamic secrets and ephemeral access are becoming necessary rather than optional. A concise policy set should distinguish routine access, privileged access, and autonomous access, because each one has different approval, logging, and revocation requirements.
- Use IAM to prove identity and establish baseline entitlement.
- Use PAM to limit elevation, capture sessions, and expire privilege quickly.
- Use cloud-native controls to scope access to the workload, tenant, or subscription.
- Use AI-specific governance to constrain agent actions, especially when tools can write or delete data.
These controls tend to break down when cloud teams, security teams, and academic IT groups maintain separate exception processes, because the same identity can then accumulate inconsistent privileges across environments.
Common Variations and Edge Cases in Universities
Tighter control often increases friction, so institutions have to balance research speed, administrative usability, and auditability. That trade-off is especially visible in labs and grant-funded projects, where temporary collaborators may need elevated access for a short period and AI systems may need constrained write access to support automation. Best practice is evolving, but current guidance suggests that the answer is not to exempt these groups from governance; it is to make time bounds, scope limits, and approval paths explicit.
One common edge case is shared research infrastructure that supports many projects. If PAM is too coarse, teams create standing exceptions that survive beyond the project; if PAM is too rigid, researchers bypass central controls. Another edge case is federated identity, where campus SSO, partner institutions, and external cloud tenants all interact. In that model, compliance teams should verify not just authentication strength but also entitlement provenance, because audit pressure often focuses on who had access at a point in time and whether that access was appropriate for the data involved.
AI identities deserve special handling when they can trigger downstream effects without a human in the loop. If the system can change cloud configuration, approve workflows, or touch regulated data, it should not be governed as a normal application account. The stronger pattern is to assign narrowly scoped workload identity, require ephemeral credentials where possible, and make every autonomous action traceable to a policy decision.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, CIS Controls v8, NIST Zero Trust (SP 800-207) and NIST AI RMF set the technical controls, while ISO/IEC 42001:2023 define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OT-01 — Organizational Context | Identity governance must reflect campus, cloud, and AI operating context. |
| Recommendation — Define identity governance boundaries across campus, cloud, and AI services. | ||
| CIS Controls v8 | 6 — Access Control Management | Covers account lifecycle, privilege restriction, and access review needs. |
| 5 — Account Management | Universities need complete inventory and lifecycle control for accounts and service identities. | |
| Recommendation — Enforce least privilege, access reviews, and timely removal of unused accounts. Track all human, service, and AI identities through one authoritative account process. | ||
| NIST Zero Trust (SP 800-207) | 4 — Policy Decision Point and Policy Enforcement Point | Cloud and AI access needs real-time policy enforcement beyond static perimeter trust. |
| Recommendation — Apply continuous policy enforcement to every privileged request and AI action. | ||
| NIST AI RMF | Map — Map the AI Context | AI identities require context-aware governance tied to intended use and impact. |
| Recommendation — Map AI identity use cases to their access, risk, and governance context. | ||
| ISO/IEC 42001:2023 | 8.2 — AI risk treatment | AI identities introduce governance duties for controlled operation and oversight. |
| Recommendation — Treat AI identity access as a governed AI risk requiring defined controls. | ||
Practitioner Guidance
What to prioritise: Establish one authoritative inventory that covers human, machine, and AI identities before tightening policy. If the inventory is incomplete, PAM controls will usually protect the wrong accounts while leaving the real privilege paths untouched.
Decision rule: If an identity can reach cloud administration, regulated data, or research systems, treat it as privileged even when it is not a human admin account. That includes service accounts and agentic systems that appear operationally routine but can make material changes.
What to verify: Confirm that every exception has an owner, expiry date, and review trigger, and that revocation works across campus, cloud, and SaaS boundaries. If any of those three elements is missing, the control is not ready for audit reliance.
Practitioner takeaway: The institutions that succeed will not be the ones that add the most IAM features; they will be the ones that make identity scope, privilege duration, and accountability consistent across humans, workloads, and AI systems.
Related resources from NHI Mgmt Group
- How should higher education institutions evaluate IAM platforms for unique campus requirements?
- How should security teams govern non-human identities for compliance?
- How should security teams govern non-human identities in cloud environments?
- How should security teams govern non-human identities for SOC 2 compliance?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
