Identity-first enterprise security needs ZSP and AI governance

At a glance

What this is: This webinar frames identity security as a board-level operating issue, linking zero standing privilege, just-in-time access, and AI governance into one identity-first control model.

Why it matters: For IAM, PAM, and NHI teams, the message is that access governance now has to cover human, machine, and agentic identities together or the blast radius remains too wide.

👉 Watch Delinea and NCC Group's webinar on identity-first enterprise security

Context

Identity-first security is the idea that access control becomes the primary security boundary when cloud services, machine identities, and AI agents can act with real execution authority. That matters for non-human identity governance because privilege is no longer limited to employees and admins, and unmanaged service accounts or agents can inherit broad access without the same oversight as human users.

The webinar’s angle is familiar to many security teams: boards want risk translated into business terms, while operators need practical steps to remove standing privilege and govern AI-driven workflows. That tension is typical, not exceptional. The real issue is whether identity, privilege, and access controls are managed as one lifecycle or as separate programs that create gaps between them.

Key questions

Q: How should security teams reduce standing privilege in identity-first environments?

A: Start by classifying privileged access as temporary unless there is a documented operational reason for persistence. Then use just-in-time access, approval workflows, and revocation logging to remove always-on permissions from humans and non-human identities alike. The key is to make elevation predictable, time-bounded, and auditable.

Q: Why do non-human identities complicate zero-trust architecture?

A: Non-human identities complicate zero-trust architecture because they often operate continuously, hold machine-readable secrets, and access systems at high speed without interactive user checks. Zero trust can still apply, but the policy must account for workload purpose, token lifetime, and runtime verification rather than assuming a person is behind every request.

Q: What is the difference between just-in-time access and standing privilege?

A: Just-in-time access grants privilege only for a defined task window, while standing privilege remains active until someone removes it. JIT reduces exposure by shrinking the time an identity can be abused, but standing privilege creates a constant attack surface. For NHI programs, the difference is often the difference between contained risk and persistent exposure.

Q: How can organisations govern AI agents without slowing operations?

A: Give AI agents narrowly scoped permissions, define which tools they may use, and log every privileged action with clear ownership. Governance should focus on action boundaries and exception handling, not on blocking all automation. The goal is to keep autonomy useful while preventing agents from inheriting broad, persistent access.

Background and context

Why standing privilege creates avoidable identity blast radius

Standing privilege means access that remains active all the time instead of being granted only when needed. In practice, persistent privilege expands the window for misuse, credential theft, and accidental overreach, especially where service accounts, scripts, and automation hold more access than any human operator would need. Zero Standing Privilege reduces exposure by making access task-scoped and time-bound, but it only works if entitlement review, approval, and revocation are reliable enough to keep pace with operations. For NHI programs, the hard part is not the policy language, it is making ephemeral access operational across tools, teams, and environments.

Practical implication: Treat standing privilege as a control defect and map every persistent entitlement to a removal or JIT path.

How just-in-time access changes NHI and PAM governance

Just-in-time access is a provisioning pattern where credentials or privileges are granted for a narrow task window and then revoked. For non-human identities, that model works best when access is tied to workload purpose, environment, and duration rather than a static role inherited for convenience. The architectural challenge is that JIT can fail quietly if systems still rely on long-lived secrets, manual approvals, or shared service accounts. It also shifts the control point from issuance to verification, so identity proofing, policy evaluation, and audit logging must happen at request time, not only during periodic review.

Practical implication: Use JIT to constrain high-risk access, but pair it with secret lifecycle controls and strong approval logic.

What AI governance changes when agents become identities

Agentic AI and shadow AI introduce identities that can reason, call tools, and trigger downstream actions without a human in the loop for every step. That changes the governance model because access is no longer just about authentication and entitlement, it also includes tool authorization, action scope, and runtime oversight. Machine identities in this context can be ephemeral or dynamically created, which makes static policy and one-time onboarding insufficient. The practical risk is that organizations may trust the model prompt while ignoring the identity attached to the action. Effective governance needs identity, policy, and telemetry to operate together.

Practical implication: Extend PAM and IAM controls to AI agents, including tool scoping, approval boundaries, and runtime monitoring.

NHI Mgmt Group analysis

Identity-first security is becoming the operating model for NHI governance, not just an architecture preference. Once cloud automation and AI workflows can execute actions directly, identity becomes the control plane that determines reach, duration, and auditability. Security teams that still treat identity as an access-admin function will miss the fact that identity is now where policy enforcement happens. The practitioner conclusion is simple: if identity is the perimeter, governance must be continuous rather than periodic.

Zero Standing Privilege is the right target, but it is not a complete control strategy on its own. Removing persistent access narrows exposure, yet it does not solve secrets sprawl, unmanaged service accounts, or agentic tool usage. JIT helps most when it is paired with inventory, approvals, revocation, and exception tracking. The field should stop treating ZSP as a slogan and start treating it as an operating discipline. The practitioner conclusion is to design the full lifecycle, not just the request flow.

AI governance is now an NHI problem because agents behave like identities with delegated authority. That means the same governance failures that affect service accounts, API keys, and certificates will reappear in agentic workflows if teams keep static assumptions. Identity blast radius: the range of systems and data a compromised identity can reach before controls intervene. Narrowing that blast radius is the core design objective. The practitioner conclusion is to bind every agent action to explicit identity and policy context.

Board communication will increasingly determine whether identity programs get funded and enforced. Security leaders need metrics that show how privilege concentration, standing access, and unmanaged non-human identities translate into business risk. That does not mean over-simplifying the problem, it means tying it to outage potential, lateral movement, and control failure. The field is moving toward measurable identity governance, and teams that cannot quantify exposure will struggle to get the remediation priority they need. The practitioner conclusion is to make identity risk reportable, not anecdotal.

The market is converging on unified identity governance across human, machine, and agentic access. Point solutions for PAM, secrets, and AI oversight are no longer enough when the same workflow can involve a person, a workload, and an autonomous agent. That convergence does not remove complexity, it exposes where fragmented ownership creates blind spots. The practitioner conclusion is to align IAM, PAM, and NHI controls under one governance model.

From our research:

• 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
• For a broader control baseline, review the Ultimate Guide to NHIs for lifecycle, visibility, and privilege management patterns.

What this signals

Identity governance is moving from control hygiene to programme architecture. As automation and agentic workflows take on more work, teams will need one operating model for human, workload, and AI access instead of separate approval paths. The practical signal is that entitlement review, exception handling, and audit logging have to become continuous capabilities, not quarterly exercises.

With 70% of organisations granting AI systems more access than human employees in equivalent roles, per the 2026 Infrastructure Identity Survey, the governance challenge is already operational rather than theoretical. Teams should expect pressure to show where agent access is bounded, how it is revoked, and who owns each autonomous workflow.

Privilege drift is the next governance debt. If standing access is not systematically removed, JIT and ZSP programmes will be diluted by exceptions and long-lived credentials. The right response is to pair policy with inventory, so the team can see where access has drifted beyond intended task scope.

For practitioners

• Define an identity risk metric the board can use Translate standing privilege, privileged account count, and unmanaged NHI exposure into a small set of business-facing metrics that map to outage, breach, and change-risk scenarios.
• Inventory all non-human identities with privilege Build a current inventory of service accounts, API keys, certificates, tokens, and AI agents, then classify each by owner, purpose, lifespan, and access scope.
• Replace persistent access with task-scoped approvals Use just-in-time access for high-risk operations and require time-bound approval, logging, and revocation for elevated actions taken by humans or agents.
• Extend governance to shadow AI and agent tools Identify autonomous workflows, tool-using agents, and embedded AI features that can perform actions without clear governance, then bring them under policy, audit, and exception review.
• Tie secrets handling to lifecycle controls Require rotation, offboarding, and revocation workflows for every NHI secret so expired credentials do not remain usable after ownership or purpose changes.

Key takeaways

• Identity-first security shifts the control boundary from infrastructure to access decisions, which makes NHI governance a core security function.
• Standing privilege and unmanaged AI access create persistent exposure that JIT and ZSP are designed to narrow.
• Security teams need board-readable identity metrics, or they will struggle to prioritise remediation across humans, workloads, and agents.

Key terms

• Zero Standing Privilege: Zero Standing Privilege is an access model where privileges are not left active by default. Access is granted only when needed, for a specific task, and removed afterward. For NHI governance, it reduces the time credentials and permissions remain available for abuse or accidental misuse.
• Just-in-Time Access: Just-in-Time Access is a provisioning pattern that issues elevated access only for a limited window and only when a request is approved. It is especially useful for privileged operations and non-human identities because it reduces persistent exposure while preserving operational speed.
• Identity Blast Radius: Identity blast radius is the amount of system access, data exposure, and downstream movement possible if an identity is compromised. In NHI environments, blast radius is often amplified by long-lived credentials, broad roles, and weak lifecycle controls across services and agents.
• Shadow AI: Shadow AI refers to AI systems, agents, or embedded workflows operating without clear governance, inventory, or approval. These systems may hold credentials or invoke tools outside normal review paths, which makes them a growing non-human identity risk.

Deepen your knowledge

Identity-first security, zero standing privilege, and NHI lifecycle governance are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If your team is trying to operationalise these controls across humans, workloads, and AI agents, it is worth exploring.

This post draws on content published by Delinea: identity-first enterprise security and governance for boards, privilege, and AI. Read the original.