Delinea’s AI identity expansion signals a broader PAM shift

At a glance

What this is: Delinea's first-half 2025 update combines ARR growth with a sharper focus on AI-enabled identity security, including shadow AI detection and risk-aware authorization.

Why it matters: For IAM teams, it shows how PAM is broadening into continuous governance for human, machine, and AI identities, which raises the bar for access control, visibility, and lifecycle oversight.

By the numbers:

• The company’s annual recurring revenue has now surpassed $400 million.
• Delinea is the only identity security provider to commit to a 99.995% uptime.

👉 Read Delinea's update on first-half 2025 growth and AI identity capabilities

Context

The core issue is no longer just privileged session control. Identity programmes now have to govern access for people, service accounts, and AI-driven workflows at the same time, which means access decisions increasingly need to be contextual, continuous, and tied to the full identity lifecycle.

Delinea's update matters because it reflects where the market is heading: broader identity security platforms are moving beyond classic PAM into discovery, entitlement control, and runtime visibility for machine and AI usage. That shift does not remove the need for least privilege, but it does change how practitioners define, monitor, and enforce it.

Key questions

Q: How should security teams govern AI-enabled access that uses existing credentials?

A: Treat AI-enabled access as part of the privileged identity surface, not as a separate automation layer. Teams should know which credentials, tokens, and delegated permissions the workflow can use, who owns them, and how they are revoked. If access cannot be tied to a clear owner and a live policy, it is already outside governance.

Q: Why do shadow AI tools create identity risk for IAM programmes?

A: Shadow AI creates identity risk because hidden tools often inherit access through secrets, service accounts, or delegated APIs without review. That breaks ownership, obscures entitlement scope, and makes revocation slow. IAM teams should treat discovery, ownership, and entitlement mapping as one workflow, not three separate tasks.

Q: What breaks when privilege decisions stay static in hybrid environments?

A: Static privilege decisions fail when the environment changes faster than the approval record. A session that looked acceptable at grant time may become risky after a role change, a new API connection, or an AI workflow starting to call additional tools. The control gap is not approval itself, but the absence of runtime re-evaluation.

Q: How can organisations tell whether access governance is keeping up with AI adoption?

A: Look for evidence that every AI-enabled access path has an owner, a policy, and a revocation process. If teams can only describe the platform and not the identities behind it, governance is lagging. Metrics such as uncovered tools, orphaned permissions, and stale delegated access show whether control is real or merely documented.

Technical breakdown

How centralized authorization extends beyond privileged sessions

Centralized authorization means the access decision sits in one control plane rather than being scattered across apps, scripts, and administrators. In practice, that lets teams evaluate context such as role, target system, session risk, and identity type before access is granted. For human identities, this supports just-in-time privilege. For machine identities and AI-enabled workflows, it becomes a way to enforce scope at runtime instead of relying on static entitlements that drift over time.

Practical implication: map privileged and non-human access requests to a single authorization layer so policy is enforced consistently.

Why shadow AI detection matters for identity governance

Shadow AI is not just an application discovery problem. It is an identity problem because unmanaged AI tools often inherit credentials, tokens, or delegated access without a clear owner. Once those identities can read data, call APIs, or trigger workflows, they become part of the privileged surface. Discovery is therefore the first governance step, but the real control point is deciding who owns the identity, what it can touch, and when it must be revoked.

Practical implication: inventory AI tools and their credentials together so discovery leads directly into ownership and entitlement review.

What real-time risk-aware access decisions change operationally

Risk-aware access decisions use signals from the current session or request instead of assuming yesterday's approval still applies today. That matters in hybrid environments where administrators, workloads, and automation all interact with sensitive systems. The control closes the gap between access approval and use, which is where many privilege failures occur. It also gives security teams a better chance of detecting unusual use patterns before they turn into lateral movement or data exposure.

Practical implication: feed session telemetry and identity context into approval logic so access can be adjusted before misuse escalates.

Threat narrative

Attacker objective: The objective is to turn legitimate identity access into durable reach across data, systems, and administrative workflows.

1. Entry occurs when unmanaged credentials, over-permissive service access, or shadow AI tooling creates a valid path into sensitive systems.
2. Escalation follows when that access is reused beyond its intended scope, especially where authorization is static and ownership is unclear.
3. Impact arrives when attackers or rogue workflows can move from identity access into data exposure, workflow abuse, or administrative control.

Breaches seen in the wild

• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.
• DeepSeek breach — DeepSeek breach exposed 1M+ log lines and sensitive secret keys.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI identity governance is now part of the PAM conversation, not a side issue. Delinea's update reflects a market reality that identity security is widening from privileged humans to machine identities and AI-enabled workflows. Once AI tools inherit credentials or call systems on behalf of users, privilege can no longer be treated as a human-only problem. Practitioners should read this as a signal to govern all access paths through one policy model.

Shadow AI discovery is a control, not a visibility feature. If an organisation cannot find unmanaged AI tools, it cannot assign ownership, define entitlements, or revoke access when conditions change. That makes discovery the prerequisite for governance rather than a reporting add-on. The practical conclusion is that identity teams need to treat AI tooling inventory with the same seriousness as service account inventory.

Risk-aware authorization is where static least privilege starts to become operationally useful again. Traditional entitlement models assume access can be defined once and safely reused. That assumption weakens in hybrid environments where access is session-bound, conditional, and increasingly machine-mediated. The implication is that identity programmes should stop relying on provisioning alone and start measuring how access decisions respond to live context.

Continuous identity governance is replacing point-in-time privilege control. Delinea's first-half results point to a broader industry shift toward platforms that can discover identities, assign access, detect irregularities, and respond in real time. That matters because the boundary between human, machine, and AI access is already blurred in production environments. Practitioners should expect governance models to be judged on runtime effectiveness, not policy documentation alone.

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.
• Organisations maintain an average of 6 distinct secrets manager instances, creating fragmentation that undermines centralised control, according to The State of Secrets in AppSec.
• That fragmentation becomes harder to absorb as AI and machine identities expand, so teams should pair access governance with lifecycle control and review their machine identity architecture now.

What this signals

With 75% of organisations expressing strong confidence in their secrets management capabilities while the average leaked secret still takes 27 days to remediate, the governance gap is clearly operational rather than theoretical. Identity teams should expect AI-enabled workflows to expose that gap faster, because access paths can be created and reused without the review cadence human IAM assumes.

Delegated-access drift: as AI tools and automation inherit more existing permissions, the real risk is not just new identities but old authorizations being reused in new ways. That makes unified ownership, entitlement mapping, and revocation workflows the controls that determine whether AI adoption stays governable.

For programmes already using centralised authorisation, the next question is whether that control plane can absorb machine identity, shadow AI, and human admin access together. If it cannot, then the organisation has policy on paper but not yet a runtime governance model.

For practitioners

• Inventory AI tools alongside privileged identities Build a single inventory that includes human admins, service accounts, API keys, and AI tools with delegated access. This prevents shadow AI from sitting outside ownership, review, and revocation processes.
• Bind access approvals to session context Use current risk signals, identity type, and target system sensitivity when making access decisions. Static approval records should not be treated as sufficient evidence that access is still appropriate.
• Review entitlement paths used by AI-enabled workflows Trace which APIs, SaaS tools, and administration consoles AI-assisted processes can reach, then remove unnecessary standing access. The goal is to keep delegated access narrow enough that it can be owned and revoked.
• Unify lifecycle checks across human and machine access Extend joiner-mover-leaver reviews to service accounts and AI identities so access ownership does not fragment across teams. Recertification should confirm that each identity still has a named owner and a current business purpose.

Key takeaways

• Delinea's update shows PAM is expanding into broader identity governance for humans, machines, and AI-enabled workflows.
• The main operational risk is unmanaged access paths, especially where shadow AI or delegated credentials sit outside ownership and review.
• Identity teams should respond by unifying inventory, authorization, and lifecycle checks so runtime access can be governed, not just approved.

Key terms

• Centralized Authorization: A control model where access decisions are made from a shared policy layer rather than inside each system. It helps teams apply the same rules to humans, service accounts, and AI-driven workflows while keeping approvals, logging, and revocation consistent across environments.
• Shadow AI: AI tools or agent-like workflows that operate without clear ownership, approval, or inventory coverage. In practice, shadow AI becomes an identity issue when it can inherit credentials, access APIs, or read data without being governed like any other privileged actor.
• Risk-Aware Authorization: An access model that uses current context, such as session risk, identity type, and target sensitivity, before allowing or continuing access. It is more resilient than one-time approval because it can respond when the environment or behaviour changes after the initial grant.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle 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.

This post draws on content published by Delinea: Delinea surpasses $400M in ARR and expands global momentum with strong first-half 2025 performance. Read the original.