AI identity security in 2026 demands a new playbook

At a glance

What this is: This is Delinea’s 2026 outlook on how AI is reshaping identity security, with the central finding that identity has become the control plane for AI risk.

Why it matters: It matters because IAM, NHI, and human identity programmes now have to govern machine-speed access, AI sprawl, and continuous validation instead of assuming stable, reviewable privilege.

👉 Read Delinea's analysis of how AI is reshaping identity security in 2026

Context

AI identity security is no longer just a question of who can sign in. The harder problem is how to govern systems that discover tools, move data, and make access decisions at machine speed while security teams still depend on static identity models. In Delinea’s framing, identity has become the primary control surface for AI-driven risk.

That shift matters because AI systems, machine identities, and autonomous agents are already expanding the enterprise attack surface faster than governance can keep up. When AI tools can store privileged credentials, connect into internal workflows, or operate outside direct human oversight, existing IAM and NHI controls stop being enough on their own.

Key questions

Q: How should security teams govern AI tools that connect to internal systems?

A: Security teams should govern AI tools as identity-bearing systems, not just software features. That means inventorying the identities they use, limiting tool and data scope, separating high-risk workflows, and monitoring the credentials or tokens they can reach. The right control target is the AI-linked identity path, not the model alone.

Q: Why do shadow AI deployments create IAM and NHI risk?

A: Shadow AI creates IAM and NHI risk because it often appears before governance, then quietly inherits access to data, APIs, and secrets. Once the tool is connected, the issue is no longer experimentation. It is unmanaged identity expansion. That makes discovery, ownership, and access scope the critical controls.

Q: What breaks when machine identity sprawl is left unmanaged?

A: When machine identity sprawl is unmanaged, privilege accumulates faster than review or retirement can keep up. Orphaned service accounts, stale tokens, and over-scoped workloads become persistent attack paths. The programme failure is not simply too many identities. It is too many identities with no clear lifecycle, purpose, or blast-radius limit.

Q: How do organisations know if continuous identity validation is working?

A: Continuous identity validation is working when access decisions are being checked against actual runtime behaviour, not just periodic review records. Teams should look for fewer standing permissions, faster revocation when context changes, and clear evidence that AI and machine identities are being monitored while they execute. If governance only appears at recertification time, it is too late.

Technical breakdown

Identity as the control plane for AI risk

The article treats identity as the control plane because AI systems increasingly need access to data, tools, and execution paths rather than just login events. In practice, this means security cannot be limited to authentication at the front door. It has to govern entitlement, scope, and ongoing verification across humans, service accounts, workloads, and AI-driven automation. Once AI can act on behalf of the business, identity is where trust, authorisation, and auditability all converge. That is why control-plane thinking is more useful than perimeter thinking for AI risk management.

Practical implication: map AI-enabled access paths to the identities that actually execute them, not just to the application they touch.

Shadow AI visibility and credential exposure

Shadow AI is not just unsanctioned model use. It becomes an identity problem when employees connect AI tools to internal systems, data stores, or credentials without security oversight. The article notes that these tools can store or transmit privileged credentials, API keys, and service tokens, which turns a usage problem into a secrets and governance problem. The visibility challenge is therefore broader than software discovery. Teams need to see where AI is being introduced, what identities it inherits, and whether those identities have already been over-scoped before the AI layer arrives.

Practical implication: inventory sanctioned and unsanctioned AI connections together with the credentials and service identities they can reach.

Machine identity lifecycle management at machine speed

The article argues that machine identities already outnumber human identities and often carry more privilege. That makes lifecycle management, not just creation or rotation, the core discipline. Workloads, service accounts, IoT devices, and AI agents can all persist with excessive privileges if no one owns their review, retirement, or monitoring. In an AI-heavy environment, lifecycle control must cover discovery, entitlement, revocation, and continuous validation. Otherwise, identity sprawl becomes a standing source of risk that grows even when the business thinks it is only adopting new automation.

Practical implication: extend lifecycle governance to every non-human identity, including discovery, ownership, access review, and offboarding.

NHI Mgmt Group analysis

Identity has become the control plane because AI changes where trust is placed. Traditional IAM assumed humans, service accounts, and applications would request access in predictable ways. That assumption breaks when AI systems can initiate actions, call tools, and move data continuously. The implication is that identity governance must now account for runtime behaviour, not just assigned entitlements.

Shadow AI is an identity blind spot before it is an application problem. The article is right to treat discovery as the first challenge, because an unsanctioned AI tool becomes dangerous the moment it inherits credentials, tokens, or data access. Visibility gaps are not just about software inventory, they are about unmanaged identity connections hidden inside normal business workflows. Practitioners should treat discovery of AI-linked identities as a governance priority, not an asset-management side task.

Machine identity sprawl is now a privilege problem, not just a scaling problem. Delinea’s warning that machine identities already outnumber humans aligns with a broader NHI reality: most risk comes from excessive privilege persisting across service accounts, workloads, and AI agents. The named concept here is identity blast radius: how far one compromised or over-scoped non-human identity can move across systems. Practitioners need to measure blast radius, not just identity count.

Continuous validation is the only control model that matches machine-speed execution. The article’s call for continuous identity validation reflects a basic governance truth. If AI-driven actions happen faster than review cycles, then access certification and periodic attestation only document yesterday’s risk. That does not replace lifecycle governance. It means the control model has to be continuous enough to observe, constrain, and audit behaviour while it is happening.

From our research:

• 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
• Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, according to The State of Non-Human Identity Security.
• For a deeper governance model, see Ultimate Guide to NHIs for lifecycle, visibility, and least-privilege controls.

What this signals

Identity blast radius will become the most useful planning metric for AI governance. AI adoption is not just increasing the number of identities in play, it is increasing how far each identity can reach across workflows, secrets, and systems. Teams should shift from counting identities to measuring how much damage any one over-scoped identity could cause. That is the programme lens that turns discovery into risk reduction.

With more than half of organisations already dealing with shadow AI monthly, according to Delinea, the first control failure is often untracked connectivity rather than advanced attack technique. That means governance teams need to align discovery, secrets management, and lifecycle ownership before they can credibly talk about AI control.

The next maturity step is to connect identity governance to runtime evidence. In practice, that means pairing IAM and NHI controls with verification signals from NIST Cybersecurity Framework 2.0 and the access assurance mindset of NIST SP 800-63 Digital Identity Guidelines.

For practitioners

• Build an AI identity inventory before expanding adoption Track sanctioned and unsanctioned AI tools, the service accounts they use, the APIs they call, and the data sets they can reach. Treat this as a living identity map, not a one-time discovery exercise.
• Bind AI access to specific non-human identities Do not let AI platforms inherit broad shared credentials by default. Assign scoped identities, separate sensitive workflows, and keep ownership explicit so privilege can be reviewed and revoked.
• Extend lifecycle controls to machine identities Apply onboarding, review, rotation, and offboarding discipline to workloads, service accounts, tokens, and AI agents. If an identity cannot be named, owned, and retired, it is already outside governance.
• Measure blast radius for every privileged non-human identity Assess how far each identity can reach across environments, secrets, and business workflows. Use that exposure map to prioritise remediation where one compromised identity could trigger the largest downstream effect.

Key takeaways

• AI is turning identity into the main control surface for risk because access, data, and action now converge inside machine-speed systems.
• The scale problem is already visible in shadow AI, machine identity sprawl, and persistent over-privilege across non-human identities.
• Governance now depends on discovery, scoped identities, lifecycle ownership, and continuous validation rather than periodic review alone.

Key terms

• Shadow AI: AI tools, models, or integrations used inside an organisation without security approval or visibility. The risk is not merely unsanctioned usage. It is the hidden identity and data exposure that appears when those tools inherit credentials, tokens, or internal workflow access without governance.
• Machine identity: A non-human identity used by software, workloads, devices, or AI systems to authenticate and act. In practice, machine identities often carry more privilege than expected, which makes discovery, ownership, lifecycle management, and revocation essential to reducing blast radius.
• Continuous identity validation: A governance model that checks identity trust throughout execution rather than only at login or periodic review. For AI and machine identities, this means verifying access, scope, and behaviour in real time so actions can be constrained while they are happening.

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.

This post draws on content published by Delinea: 2026: The year AI breaks security and forces a new identity security playbook. Read the original.