Akeyless frames unified runtime identity security for AI agents

At a glance

What this is: Akeyless presents a unified SaaS identity security model that spans secrets, PAM, certificates, keys, and AI agents, with runtime identity enforcement as the central design point.

Why it matters: This matters because IAM teams now have to govern human, machine, workload, and agent identities with one operational model instead of separate control planes.

By the numbers:

• Only 44% of organisations are currently using a dedicated secrets management system.

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

Context

Enterprises now manage identity for people, workloads, containers, pipelines, machines, and AI agents at the same time, but many still rely on separate tools for secrets, PAM, certificates, and key management. That creates a governance gap because the access path is fragmented even when the risk model is not.

The primary issue is not whether each tool works in isolation. It is whether the programme can enforce consistent policy, audit, and revocation across identities that change form at runtime, especially as AI agents begin to act like active consumers of secrets and target systems.

Key questions

Q: How should security teams govern AI agents that need access to secrets and internal systems?

A: Treat AI agents as runtime identities, not as enhanced service accounts. Govern them with ephemeral access, narrow task scope, continuous audit, and explicit policy checks at execution time. The key question is whether the agent can reach only the systems it needs for the current task, and whether that access disappears immediately after use.

Q: Why do fragmented secrets tools create more risk than a single platform view?

A: Fragmentation hides privilege paths, slows revocation, and makes it harder to prove who had access to what and when. When secrets, keys, certificates, and privileged access live in different systems, governance becomes inconsistent and incident response becomes slower. A single operational view does not remove risk, but it makes control and accountability far clearer.

Q: What do teams get wrong about just-in-time access for machines and workloads?

A: They often treat just-in-time access as a point feature instead of a lifecycle control. JIT only reduces risk when it is tied to policy, identity type, and revocation discipline. If long-lived credentials still exist alongside JIT workflows, the standing-risk problem remains and the programme gains complexity without reducing exposure.

Q: How can organisations tell whether their identity governance is keeping pace with runtime access?

A: Look for evidence that policy, audit, and revocation operate at the same speed as execution. If access decisions are still made after the task is complete, or if reviewers cannot reconstruct who or what used a credential, the governance model is behind the environment. Runtime access must be observable while it is still actionable.

How it works in practice

Why fragmented secrets and identity tooling creates blind spots

When secrets management, PAM, certificate management, and key management sit in different products, the control plane becomes harder to reason about. Each tool may enforce policy locally, but the organisation loses a single view of who or what can reach a secret, when that access is valid, and how it is revoked. In practice, fragmentation increases the chance that standing credentials survive longer than intended, especially across cloud, SaaS, hybrid, and on-prem environments.

Practical implication: map every secret-bearing system to one governance owner and one revocation process.

Runtime identity security for machines and AI agents

Runtime identity security shifts the control point from provisioning to execution. Instead of issuing long-lived credentials and hoping they are used correctly, the platform brokers access at the moment of need, often with ephemeral identities and policy checks tied to context. For machines and AI agents, that means the identity is not just authenticated once. It is continuously governed as it interacts with databases, cloud services, SaaS tools, or internal systems.

Practical implication: treat access approval as an execution-time control, not only a joiner-mover-leaver activity.

Zero-knowledge architecture and ephemeral credentials

A zero-knowledge design reduces provider visibility into customer secrets by splitting or abstracting the cryptographic material so no single party holds the full secret in usable form. Combined with ephemeral credentials, this lowers the value of stolen secrets because the access window is short and the credential is policy-bound. The architectural trade-off is operational discipline: teams must know exactly which workflows can tolerate short-lived identities and which integrations still depend on persistent credentials.

Practical implication: classify which systems can move to ephemeral access first and retire long-lived secrets where possible.

NHI Mgmt Group analysis

Fragmented identity security is now a governance failure, not just an architecture issue. When secrets, PAM, certificates, and keys are split across products, the organisation loses a coherent control surface for access approval, audit, and revocation. The problem is not tool count alone. The problem is that identity evidence becomes distributed across systems that do not share one operational truth. Practitioners should treat fragmentation as a measurable governance risk, not a nuisance.

Runtime identity security is the right lens for machine and AI agent access. Static authentication assumes the identity is known before execution begins and remains stable long enough for review cycles to matter. That model works poorly when workloads and agents request access at execution time and may only need it for a narrow task window. The implication is that programme design has to follow runtime behaviour, not just provisioning records.

Ephemeral access changes the economics of secret exposure. Long-lived credentials create a persistence model that favours attackers and complicates incident response. Short-lived, policy-controlled identities reduce the dwell time of exposed access and make stolen material less reusable. The named concept here is ephemeral credential trust debt: the longer an organisation relies on persistent secrets, the more hidden exposure it accumulates across systems, owners, and workflows. Practitioners should measure where that debt is highest.

Unified governance matters most where humans, machines, and AI agents share the same backend systems. The market is moving toward platforms that try to cover all three identity classes because the same applications and data stores now serve them all. That does not make the identities interchangeable. It means governance must distinguish actor type, runtime behaviour, and privilege scope in one model. Teams should redesign policies so the access path is governed once, not separately for every identity category.

AI agent security is forcing identity teams to confront runtime intent, not just runtime access. An agent can have valid credentials and still behave in ways that exceed the original governance assumption. The field is moving toward controls that inspect the action path, not simply the login event. Practitioners should prepare for policies that govern what an agent may do at execution time, not only what it may authenticate to.

From our research:

• The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.
• That gap makes the case for stronger lifecycle governance in the Ultimate Guide to NHIs, especially where secrets, workloads, and agents share the same runtime path.

What this signals

Ephemeral credential trust debt: the longer organisations keep long-lived secrets in circulation, the more exposure they accumulate across pipelines, workloads, and agentic workflows. That debt is hard to see until a leak forces remediation, which is why runtime governance has to sit alongside secrets inventory and revocation discipline.

A programme that still treats secrets, PAM, certificates, and workload identity as separate projects will struggle to produce consistent control evidence. The next maturity step is not another point tool. It is a governance model that can explain access across actor types and prove when access actually ended.

For teams building that model, the practical reference point is the Ultimate Guide to NHIs, with policy patterns that align better to OWASP Agentic AI Top 10 where runtime decision-making enters the access path.

For practitioners

• Inventory identity control-plane fragmentation Map where secrets, PAM, certificate, and key management are operating as separate governance islands. Assign one owner to define how access is approved, logged, and revoked across those islands.
• Prioritise runtime access for high-risk workflows Move the most sensitive workload, pipeline, and AI agent integrations to ephemeral, policy-controlled access first. Focus on systems that currently depend on long-lived credentials or manual handoffs.
• Separate human, workload, and agent policy logic Do not reuse the same entitlement rules for people and non-human identities. Build policy boundaries that reflect actor type, task scope, and credential lifetime.
• Audit where standing privilege still exists Identify accounts, tokens, and certificates that survive beyond the task that created them. Tie those findings to a remediation queue with explicit revocation owners and deadlines.
• Use 52 NHI Breaches Analysis for pattern review Compare your current exposure points against repeated failure modes in real incidents to see whether the same credential persistence pattern exists in your environment.

Key takeaways

• Fragmented identity security leaves organisations with multiple control planes and weak visibility into who or what can reach secrets, keys, and certificates.
• Runtime identity security matters because static credentials create persistent exposure while ephemeral access shortens the window for abuse.
• IAM teams should redesign governance around actor type, execution-time policy, and revocation evidence instead of relying on separate tools to close the gap.

Key terms

• Runtime Identity Security: Runtime identity security governs access at the moment an identity acts, not only when it authenticates. It combines policy checks, contextual approval, audit, and revocation so access is tied to execution. This is especially relevant for workloads and AI agents that may request or consume secrets dynamically.
• Ephemeral Credentials: Ephemeral credentials are short-lived access tokens, certificates, or keys issued for a specific task or session. They reduce exposure by limiting how long a credential can be reused if it is stolen, but they only help when revocation, scope, and audit are managed consistently across the full identity lifecycle.
• Zero-knowledge architecture: Zero-knowledge architecture is a design in which the service provider cannot directly recover customer secrets in usable form. It reduces trust concentration by ensuring no single system or operator can assemble the full secret. For security teams, the practical question is whether that design also supports auditability, rotation, and operational recovery.
• Ephemeral credential trust debt: Ephemeral credential trust debt is the accumulated risk created when organisations keep relying on long-lived secrets instead of short-lived, task-scoped access. The longer that pattern persists, the more hidden access paths exist across pipelines, workloads, and agents. It is a useful way to describe how technical convenience turns into governance exposure.

What's in the full announcement

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

• Side-by-side product scope across secrets, PAM, certificate management, key management, and AI agent security.
• Platform and deployment details for SaaS-native control, hybrid gateways, and runtime identity enforcement.
• Specific integration claims across cloud, SaaS, DevOps, and legacy environments that shape implementation decisions.
• Comparison tables that break down feature coverage across secrets rotation, JIT access, and certificate lifecycle management.

👉 Akeyless's full post covers platform scope, runtime enforcement, and AI agent access details.

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.