JumpCloud proposes an Agentic IAM lifecycle intended to bring AI agents into the same identity governance processes used for human employees. The framework sets out four practical stages: discover agents, register them as formal identities, manage access with least privilege and without standing credentials, and continuously govern agent behavior.
Why this matters
AI agents now operate inside corporate systems: they access CRM systems such as Salesforce, create tickets in Jira, provision infrastructure, process financial transactions, and act on behalf of teams. Despite this, most organizations have not enrolled these agents in standard onboarding, ownership, and offboarding processes. They often have no named owner and no formal deprovisioning workflow.
JumpCloud’s Q3 2026 research (n=800 IT leaders, US and UK) found that non-human identities outnumber human users in 83% of organizations, yet only 21% have governance controls specifically for non-human identities. The framework below is intended to close that governance gap.
Stage 1 — Discover every agent in the environment
Good governance begins with an accurate inventory, but many organizations lack a complete one. Product teams, operations leaders, and individual contributors can deploy agents across environments, and IT frequently inherits responsibility after the fact without a full view of what’s deployed.
The practical result is "Shadow AI": agents running in production without records, owners, or straightforward ways to stop them if problems occur. Discovery needs to be ongoing: map every environment where agents could run (cloud platforms, managed devices, SaaS integrations, on-prem systems) and document for each agent what it can access, which workflows it influences, and what triggers its actions. That inventory underpins the rest of the lifecycle.
Stage 2 — Register each agent as a formal identity with a named owner
Every agent should exist as a directory identity with attributes similar to an employee: a defined purpose, an authorized scope of actions, and a named human owner accountable for its behavior. This architectural decision separates organizations that can govern agents from those that cannot.
Agents that only exist as service-account workarounds or API keys in environment variables cannot be governed systematically. Registration also addresses "Zombie Agents": agents that continue running and accumulating permissions after their original purpose ends. When each agent has a named owner responsible for renewal, access naturally lapses when ownership does not persist — making offboarding a process outcome rather than a reactive cleanup.
Stage 3 — Manage agent access with least privilege and no standing credentials
Registered agents require access, but the guiding principle is least privilege: entitlements precise to the agent’s purpose, time-bounded where possible, and immediately revocable if behavior changes.
Standing credentials in environment variables and static API keys that never rotate are persistent liabilities. Practically, secure agent access management means issuing just-in-time credentials for privileged operations, building approval workflows that require human sign-off before agents reach sensitive systems, and maintaining emergency shutdown mechanisms that operate at required speed.
For agents accessing privileged web applications, SSH, or databases, credential shielding is required: the agent should perform tasks without the underlying credentials ever being exposed to the model. Every privileged session should be recorded and available for audit.
Stage 4 — Continuously govern agent behavior
The first three stages establish controls; governance keeps them current. Continuous governance verifies that agents’ actual actions match their authorized scope and corrects course when they diverge.
All agent actions must be logged. Regular access reviews should evaluate whether an agent’s entitlements remain appropriate. Anomalous behavior should be detectable before it becomes an incident. When an agent’s purpose ends, access revocation should be a procedural step rather than a reactive response to failure.
Governance also requires preserving the audit trail needed to answer accountability questions: what did the agent access, what actions did it take, who authorized it, and what were the outcomes? Organizations that cannot reconstruct this chain for any given agent are not governing them meaningfully; they have merely deployed the agents and hoped for the best.
The foundation: a unified IT control plane
Each stage is significantly harder when the IT environment is fragmented. Identity, access, device management, and security controls split across disconnected systems create the gaps where agent governance fails and policies end up inconsistent.
JumpCloud’s research found organizations operating in fully unified IT environments are five times more likely to deploy agents in business-critical workflows than those running fragmented stacks. Whether a control layer can apply consistent policies across humans, devices, and agents determines whether governance scales with AI adoption or falls behind it.
Agentic IAM’s core premise is that governing humans, devices, and agents through a single coherent control layer makes the framework executable at scale rather than merely aspirational.
Conclusion
Securing every identity — human or not — is the operational foundation that enables safe AI scaling. Organizations that implement these practices now will reduce risk and be able to expand AI into more workflows faster and with greater confidence that every identity in their environment is known, governed, and accountable.
Greg Keller is CTO and Co-founder at JumpCloud. JumpCloud’s Q3 2026 IT Trends Research report (n=800 IT leaders, US + UK) is available; the Agentic IAM lifecycle framework referenced here was developed by JumpCloud.
Note: this is sponsored content by JumpCloud. For information on VentureBeat’s sponsored posts, contact sales@venturebeat.com.
