Notifications
Clear all
Topic starter
25/06/2026 2:33 pm
TL;DR: The growing need to manage machine identities alongside workforce access is reflected in identity platforms that govern human and non-human access, including Non-Human Identity and AI-agent use cases, according to Saviynt. That shift matters because identity programmes now have to govern autonomous access paths, not just human accounts, across applications, data, and business processes.
NHIMG editorial — based on content published by Saviynt: the newsroom overview of AI identity, NHI, and platform developments
Questions worth separating out
Q: How should security teams govern AI agents and non-human identities in the same programme?
A: Start by separating the actor model from the control model.
Q: Why do AI agents complicate traditional identity governance?
A: AI agents complicate governance because they can select actions and consume tools at runtime, which makes static entitlement assumptions weaker.
Q: What is the difference between NHI governance and AI-agent governance?
A: NHI governance usually focuses on static credentials, service accounts, secrets, and workload identities.
Practitioner guidance
- Define separate policy classes for human, NHI, and AI-agent access Do not collapse these identities into one entitlement model.
- Apply just-in-time access to all privileged machine paths Use time-bounded access for administrative APIs, production consoles, and automation accounts that can alter business-critical systems.
- Continuously inventory AI-agent tool access and downstream entitlements Track which applications, data sources, and privileged actions each agent can reach, then remove stale authorisations when workflows change.
What's in the full article
Saviynt's full newsroom page covers the product and platform detail this post intentionally leaves for the source:
- The exact product and capability naming used across Saviynt's identity cloud portfolio.
- The platform areas tied to Non-Human Identity, just-in-time access, and AI-agent governance.
- The vendor's own positioning around machine identities, external identity, and application access governance.
- The broader newsroom context for current announcements and solution updates.
👉 Read Saviynt's newsroom page on AI identity, NHI, and platform governance →
AI agent governance in identity security: what teams need to know?
Explore further
View Full Forum → | NHI Foundation Course → | Our Services →
25/06/2026 3:42 pm
AI-agent governance is now an identity problem, not an AI side issue. Saviynt’s framing shows how quickly agentic access gets pulled into the same control plane as workforce identity and machine identity. That matters because the governance failure is not just missing tooling, but treating agent behaviour as if it were ordinary service-account execution. Practitioners should expect identity teams, not only AI teams, to own the control model.
A few things that frame the scale:
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to The State of Secrets in AppSec.
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities.
A question worth separating out:
Q: What should IAM teams do when identity platforms bundle human, NHI, and AI controls?
A: They should still design policies by actor behaviour, not by product menu. Bundled platforms can simplify administration, but they do not eliminate the need for separate controls on workforce access, machine entitlements, and autonomous or semi-autonomous agent paths. Clear ownership and review boundaries remain essential.
👉 Read our full editorial: Saviynt's identity platform highlights the rise of AI agent governance
