Akeyless frames unified runtime identity security for AI agents

At a glance

What this is: This is Akeyless's argument for unifying identity security controls around runtime enforcement so human, machine, workload, and AI agent access can be governed at execution time rather than through disconnected point tools.

Why it matters: It matters because IAM, PAM, and NHI teams are being pushed toward one governance model for ephemeral access, policy enforcement, and auditability across identities that now act at runtime.

👉 Read Akeyless's analysis of unified runtime identity security for AI agents

Context

Runtime identity security is the idea that access decisions follow execution, not just provisioning. That matters because modern environments now mix human users, workloads, machines, containers, and AI agents, and each of them can need access to secrets, keys, certificates, and target systems at different moments.

The governance gap is fragmentation. When secrets management, PAM, certificate management, and key management sit in separate tools, teams lose the ability to apply one policy model across identities that increasingly share the same infrastructure and the same blast radius. For AI agents in particular, that gap becomes harder to manage because access is often dynamic, session-bound, and tied to runtime context.

Akeyless frames its approach as a unified SaaS control plane for those identity types, with ephemeral access and continuous enforcement replacing long-lived credentials. The underlying problem is not simply scale, but the assumption that identities can be secured correctly if access is handled only at issuance.

Key questions

Q: How should security teams implement runtime access decisions in identity governance?

A: Start with the highest-risk decisions, such as privileged access and sensitive application requests, and wire in live signals from security and device telemetry. The goal is not to automate every request, but to make the most dangerous ones responsive to current conditions before access is granted or continued.

Q: Why do long-lived secrets create more risk for workloads and agentic AI systems?

A: Long-lived secrets increase blast radius because any exposed key can be reused until it is rotated or revoked. In workload and AI environments, those credentials are often embedded in automation, shared across services, or retrieved repeatedly, which weakens accountability. Short-lived, identity-based access reduces persistence, limits reuse, and makes compromise easier to contain.

Q: What breaks when secrets, PAM, and certificate management stay in separate tools?

A: The main failure is inconsistent governance. Separate tools often produce different lifecycle rules, different audit records, and different revocation paths, so the organisation cannot prove that access was controlled end to end. That gap becomes more serious when the same workload or AI agent can touch secrets, keys, and privileged systems through different interfaces.

Q: How can organisations tell whether runtime identity controls are actually working?

A: Look for evidence that unsafe access attempts are being blocked, stepped up, or constrained at the point of use. If the programme only produces reports, alerts, or post-event findings, then it is measuring risk rather than controlling it. The key signal is whether the access path changes in response to policy.

How it works in practice

Why runtime identity security changes the access model

Runtime identity security shifts enforcement from pre-provisioned credentials to moment-of-use authorization. In practice, that means the system brokers access when a workload, machine, or AI agent needs it, then reduces or removes the standing credential footprint after the action is complete. This is different from traditional secrets distribution, where a long-lived secret is handed out and later rotated. The architectural change matters because policy decisions can be tied to execution context, not just identity enrollment. For AI agents, that context may include task scope, target system, and request timing, which is why dynamic authorization becomes more relevant than static role assignment.

Practical implication: move control points from credential issuance to runtime policy evaluation for identities that act during execution.

Ephemeral identities versus long-lived secrets

Ephemeral identities are short-lived access constructs created for a specific task, session, or workflow. They reduce the persistence of reusable credentials, which is important when the same identity may need access to cloud services, databases, or AI toolchains across different trust zones. Long-lived secrets create a wider exposure window because compromise can outlast the business action that justified the access. In a unified identity model, the key question is no longer only whether a secret is stored securely, but whether the secret needed to exist at all outside the moment of use. That is the operational difference between static access and runtime access control.

Practical implication: prefer short-lived, policy-controlled access for workloads and AI agents that do not need persistent credentials.

Unified governance across secrets, PAM, certificates, and keys

A unified identity security platform tries to govern multiple control domains through one policy and audit layer. That matters because secrets, privileged access, certificate lifecycle, and key management often fail together when they are treated as separate operational silos. A certificate can outlive its intended owner, a privileged session can be granted outside the secrets workflow, and a machine identity can remain active after the workload changes. The technical issue is not just tooling sprawl, but inconsistent lifecycle control across identity artifacts that should be governed as one system. Once runtime access enters the picture, those silos become harder to defend because each one can become a separate path to the same target system.

Practical implication: align lifecycle, policy, and audit across credentials, certificates, keys, and privileged sessions instead of managing each in isolation.

NHI Mgmt Group analysis

Identity security is converging on runtime governance because provisioning-time control no longer matches operational reality. Enterprises are now attaching access to workloads, machines, containers, and AI agents that execute continuously and often independently of human timing. That means the old separation between identity issuance and identity use is collapsing into a single control problem. The practitioner conclusion is straightforward: governance has to evaluate access in motion, not only at setup.

Long-lived credential distribution is becoming the wrong default for AI agents and modern workloads. When identities can request access dynamically, persistent secrets create more exposure than they solve. The value of static credentials falls because the business action is often shorter than the credential lifetime. The practitioner conclusion is to treat persistence as a liability unless a use case truly requires it.

Runtime identity security exposes the cost of IAM, PAM, and secrets management silos. Separate products can each do part of the job, but they do not automatically produce a coherent control plane. The result is duplicated policy logic, inconsistent audit trails, and gaps between secrets issuance, privileged session control, and certificate lifecycle. The practitioner conclusion is that governance quality depends on lifecycle consistency, not tool count.

Access review processes assume access lasts long enough to be reviewed; AI agents can acquire and release access inside a single execution path. That assumption was designed for persistent human or machine entitlements, not for actors whose access exists only momentarily at runtime. The implication is not simply that reviews need to be faster, but that review is the wrong primary control for some AI agent access patterns. The practitioner conclusion is to rethink where assurance lives when the access window is shorter than the review cycle.

Runtime identity security is becoming the baseline for cross-domain identity governance. Human identity, NHI, and agentic AI are now overlapping inside the same operational environments, so policy models need to describe who or what is acting, what it can touch, and for how long. That makes unified governance more important than isolated product coverage. The practitioner conclusion is to design one identity control model that can stretch across actor types without losing lifecycle discipline.

From our research library:

• 88% of organisations have embedded AI agents in their workflows, according to KPMG's 2026 report.
• Read next: Zero Trust for AI Agents

What this signals

Access review cycles are losing value as the primary assurance control for AI agent and workload access. When an identity can be granted access, act, and release it within one execution path, review becomes descriptive rather than preventive.

Runtime identity boundary: the practical dividing line between static entitlement management and policy enforcement at execution time. That boundary now matters because organisations cannot assume that every identity will remain observable long enough for traditional governance to catch up.

As AI agents move from experiments into workflows, practitioners should expect more pressure to unify secrets, PAM, and machine identity governance under one operating model.

For practitioners

• Map runtime access paths Inventory where human users, workloads, machines, and AI agents obtain access only at execution time, then trace which of those paths still depend on long-lived credentials.
• Separate persistent access from task-scoped access Classify which identities truly require standing privileges and which can operate with ephemeral, policy-controlled access tied to a single workflow or session.
• Unify policy across identity silos Align secrets, PAM, certificate, and key lifecycle rules so that one entitlement model governs issuance, use, audit, and revocation across the stack.
• Define controls for AI agent execution Set explicit guardrails for what an AI agent can access, when it can request access, and how its actions are recorded for audit and review.

Key takeaways

• The article's core risk is fragmentation across secrets, PAM, certificates, and keys, which leaves identity governance inconsistent just as more actor types need access.
• Its main architectural signal is that runtime enforcement and ephemeral access are becoming more important than provisioning-time credential distribution.
• Practitioners should treat unified lifecycle control as the issue to solve, because separate tools do not automatically create coherent governance.

Key terms

• Runtime Identity Security: Runtime Identity Security is the practice of protecting identities while they are actively being used by software, services, devices, or agents. It focuses on controlling authentication, authorization, secrets, and session behavior at execution time, so compromised credentials, excessive privileges, or abnormal identity actions can be detected and contained quickly.
• Dynamic Ephemeral Identity: Dynamic Ephemeral Identity is a model in which credentials or authority exist only for a short operational window and are generated at runtime. It reduces the value of exposed secrets, but only if the environment can also limit what the identity is allowed to do while active.
• Standing Privilege: Standing privilege is access that remains active even when no immediate task requires it. For NHI programmes, it is a common failure mode because long-lived credentials and persistent roles create unnecessary exposure. Reducing standing privilege usually means tighter expiry, on-demand access, and clearer review of who or what still needs access.
• Unified Control Plane: A unified control plane is an identity architecture where discovery, access governance, audit, and response operate across humans, machines, and AI agents together. It reduces blind spots caused by siloed tooling and gives security teams context for decisions about permissions, data, and containment.

What's in the full announcement

Akeyless's full article covers the operational detail this post intentionally leaves for the source:

• The vendor's side-by-side comparison tables for secrets management, PAM, certificate lifecycle management, and AI agent security
• The specific runtime identity features it claims for ephemeral access, secretless connectivity, and policy enforcement
• The product-level distinctions it draws between its control plane and CyberArk-linked capabilities across multiple identity domains
• The FAQ section that expands the platform claims into implementation-oriented answers

👉 Akeyless's full article covers the runtime access model, product comparisons, and AI agent security claims in more detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.