IAM trends are pushing NHI governance into the center

At a glance

What this is: This analysis argues that Gartner’s IAM trends are pulling non-human identities, AI, hybrid access, and standards into a single governance problem.

Why it matters: For IAM and NHI practitioners, the implication is that identity programs now need one control model for service accounts, AI pipelines, and hybrid workloads.

By the numbers:

• 12.8 million secrets were publicly exposed on GitHub in 2023 alone, a 28% increase from 2022.
• 17 to 25% of organizations have experienced security incidents related to machine identities.
• 36% of organizations have a hybrid approach to identity and access decision rights in the last 2 years.

👉 Read One Identity’s analysis of IAM trends, machine identities, and AI governance

Context

Non-human identity governance has moved from an operational niche to a core IAM concern because APIs, DevOps pipelines, service accounts, and AI systems now drive access decisions at machine speed. The central issue is not whether these identities exist, but whether organizations can govern their privilege, lifecycle, and auditability with enough consistency to prevent exposure.

The article uses Gartner’s 2024 IAM trends to argue that machine IAM, AI IAM, hybrid models, and identity standards are converging into one security planning problem. That framing is typical of mature IAM discussions, but the article is more vendor-forward than analytically strict, so the underlying governance gap needs to be separated from the platform claims.

Gartner’s figure that 12.8 million secrets were publicly exposed on GitHub in 2023 makes the risk concrete because secrets are the operational fuel for many NHIs. The key lesson is that visibility without lifecycle control still leaves machine identities overprivileged and hard to audit.

Key questions

Q: How should security teams govern non-human identities across cloud and DevOps environments?

A: Start with a unified inventory, then assign every non-human identity an owner, a purpose, and a renewal path. Apply least privilege, short-lived credentials where possible, and recurring access review. The goal is to make service accounts, tokens, and API keys auditable as a managed population, not as isolated technical artifacts.

Q: What is the difference between machine IAM and human IAM?

A: Human IAM focuses on people, interactive authentication, and user lifecycle events. Machine IAM governs service accounts, tokens, certificates, APIs, and workloads that authenticate programmatically and often run continuously. Because machines can copy credentials into code or pipelines, machine IAM needs tighter lifecycle control, faster revocation, and stronger privilege scoping.

Q: When does AI-assisted identity management become a security risk?

A: AI-assisted identity management becomes risky when systems can change access, approve entitlements, or recommend remediation without bounded policy and review. The danger is not AI itself, but unreviewed automation around high-impact identity decisions. Use AI for detection and assistance first, then constrain any access-changing action with approval and logging.

Q: Why do hybrid identity environments make NHI governance harder?

A: Hybrid environments split identity enforcement across different control planes, which makes consistent lifecycle, logging, and revocation harder to maintain. Machine identities then accumulate exceptions as they move between cloud, SaaS, and on-premises systems. A common policy model matters more than any single product feature because the risk comes from inconsistency.

Technical breakdown

Machine IAM and the identity surface area problem

Machine IAM covers APIs, DevOps pipelines, AI workflows, robotic process automation, and service accounts. These identities do not behave like human users: they authenticate programmatically, often run continuously, and can be duplicated or embedded across environments. That creates a larger identity surface area than most IAM teams expect. The technical failure mode is not just too many accounts, but too many credential paths, too much inherited privilege, and too little lifecycle enforcement. Once secrets are copied into code, images, or pipelines, revocation becomes slow and incomplete.

Practical implication: Treat machine identities as a governed population, not as technical leftovers attached to application delivery.

AI IAM and autonomous privilege decisions

AI IAM adds a decision layer where models, assistants, or agents can influence access workflows, detect anomalies, or trigger remediation. That changes trust assumptions because the system can act with confidence even when its recommendation is wrong. In practice, AI IAM is safest when it operates inside a bounded identity fabric with explicit approval paths, logging, and policy constraints. Without those controls, AI-assisted administration can accelerate mistakes as efficiently as it accelerates detection.

Practical implication: Bound AI-driven access actions with policy and human review for high-risk changes.

Hybrid identity models and standards-based portability

Hybrid IAM spreads identity control across on-premises, cloud, SaaS, and containerized workloads, which makes a single enforcement plane difficult. Standards such as SPIFFE, CAEP, Authzen, and verifiable credentials matter because they reduce custom integration and make authentication and authorization more portable across systems. But standards do not solve governance on their own. They help only when organizations also define issuance, rotation, revocation, and audit requirements for every machine identity that crosses an environment boundary.

Practical implication: Use standards to reduce integration friction, then apply the same lifecycle rules across every deployment model.

Threat narrative

Attacker objective: The attacker’s objective is to exploit machine identity trust to gain durable, low-friction access that blends into legitimate automation.

1. Entry occurs when hardcoded secrets, service account tokens, or API keys are exposed in repositories, CI/CD systems, or logs.
2. Escalation follows when those credentials carry broader permissions than the workload actually needs, allowing privilege expansion or unauthorized automation.
3. Impact occurs when attackers reuse machine identities to move through cloud, DevOps, or AI-linked systems without triggering user-centric controls.

Breaches seen in the wild

• CI/CD pipeline exploitation case study — full server takeover via exposed .git directory and mismanaged CI/CD pipeline secrets.
• Reviewdog GitHub Action supply chain attack — reviewdog/action-setup GitHub Action supply chain attack exposed secrets.

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

NHI Mgmt Group analysis

Machine IAM is now the core NHI governance problem, not a side topic. Once APIs, pipelines, RPA, and service accounts are treated as one population, the old boundary between application security and identity governance disappears. The operational risk is privilege sprawl across systems that were never designed to be reviewed like human accounts. Practitioners should manage machine identities as a first-class identity class.

AI IAM creates a trust problem before it creates an efficiency gain. AI can accelerate risk detection and administration, but it also expands the number of automated decisions that influence access. That means organizations need a stronger policy layer, not just better models or more telemetry. The governance test is whether AI is constrained enough to improve decisions without becoming an unreviewed access path.

Hybrid identity has made control consistency the real differentiator. When identity decisions span cloud, SaaS, and on-premises environments, fragmented enforcement becomes a recurring source of exceptions and audit noise. The market is moving toward unified identity fabrics because point controls do not scale across heterogeneous workloads. The practical conclusion is that consistency matters more than feature breadth.

Standards will matter most where they reduce custom trust translation. SPIFFE, CAEP, Authzen, and verifiable credentials are part of a broader shift toward portable identity assertions and continuous evaluation. That shift helps NHI governance only if lifecycle ownership stays clear. Organizations that adopt standards without credential governance will still accumulate risk, just with cleaner interfaces.

Identity blast radius is the right framing for machine identity risk. The critical issue is not whether a machine identity exists, but how far it can move if misused. Overprivilege, static credentials, and weak revocation all expand blast radius across connected systems. Security teams should measure whether each machine identity can be contained to a task, not simply whether it can authenticate.

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.
• 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, which means access governance must scale before autonomy does.
• For the governance model behind that shift, see the NHI Lifecycle Management Guide for practical lifecycle controls that can be adapted to machine and agent identities.

What this signals

Identity blast radius: the programme risk is no longer just exposure, but how much damage a single machine identity can do once it is misused. With 19% of organisations giving AI systems dramatically more access than human employees, per the 2026 Infrastructure Identity Survey, governance has to shift from inventory to containment.

The most durable response is to standardise issuance, rotation, and revocation across cloud, SaaS, and on-premises systems. Where organizations adopt standards such as NIST Cybersecurity Framework 2.0 or SPIFFE-style workload identity patterns, the real win is not compliance language but repeatable control.

For identity teams, the signal is that AI, machine identities, and hybrid access are converging into the same control plane. That makes lifecycle processes for managing NHIs more relevant, not less, because unmanaged exception handling will become the bottleneck.

For practitioners

• Inventory every machine identity class Build a single inventory that includes APIs, service accounts, RPA bots, AI pipelines, certificates, and tokens. Classify each identity by owner, workload, privilege level, and renewal path so audit teams can trace who can use it and why.
• Reduce standing privilege for machine accounts Replace persistent access with task-scoped credentials wherever the workload allows. Prioritize privileged service accounts first, then set minimum permissions and short credential lifetimes for systems that cannot yet be fully ephemeral.
• Adopt standards for cross-environment identity trust Use SPIFFE, CAEP, Authzen, or verifiable credentials where they reduce custom identity translation between cloud, SaaS, and on-premises environments. Pair those standards with explicit issuance and revocation rules so portable identity does not become portable risk.
• Tie AI-assisted administration to approval controls Require human approval for high-risk access changes recommended by AI systems, and log the model output that triggered each action. That preserves speed for low-risk tasks while keeping escalated privilege changes reviewable.

Key takeaways

• Machine identities are becoming the dominant governance challenge because they now span APIs, pipelines, AI systems, and service accounts.
• The scale of exposure is already measurable, with Gartner citing 12.8 million secrets publicly exposed on GitHub in 2023 and 17% to 25% of organizations seeing machine-identity incidents.
• Practitioners should respond by tightening lifecycle control, reducing standing privilege, and enforcing consistent identity policy across hybrid environments.

Key terms

• Machine IAM: Machine IAM is the discipline of governing non-human identities such as APIs, service accounts, tokens, certificates, and automation workloads. It extends identity control to entities that authenticate programmatically and often operate continuously, which makes lifecycle management, revocation, and privilege scoping more important than login experience.
• Identity Blast Radius: Identity blast radius is the amount of damage a compromised identity can cause across systems and workflows. For NHIs, it depends on how far a token, certificate, or service account can move, what it can access, and how quickly it can be revoked when misuse is detected.
• Hybrid IAM: Hybrid IAM is an identity operating model that spans on-premises, cloud, SaaS, and containerized environments. It requires consistent policy, auditability, and access lifecycle controls across different control planes, because fragmentation creates exceptions that attackers and auditors both exploit.
• AI IAM: AI IAM is the use of identity and access controls to govern AI systems that assist, recommend, or execute security and administration actions. The core challenge is limiting autonomous influence over access decisions while preserving the speed and scale benefits of automation.

Deepen your knowledge

AI IAM, machine identity governance, and lifecycle control are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building controls for hybrid environments and autonomous systems, it is worth exploring.

This post draws on content published by One Identity: Unify your identity platform: Address the IAM trends. Read the original.