IAM trends for 2025 point to consolidation, zero trust and AI

At a glance

What this is: This is a year-in-review on enterprise IAM trends for 2025, highlighting consolidation, zero trust, AI in identity, ITDR, compliance automation, third-party governance, and IoT identity as the main themes.

Why it matters: It matters because IAM teams are being pushed to govern more identity types, tighten lifecycle controls, and connect identity decisions to broader security operations across human, NHI, and machine contexts.

By the numbers:

• Nearly 60% of enterprises prioritized consolidating their IAM tools to reduce complexity and improve efficiency.
• 70% of enterprises adopted automated IAM tools to streamline access reviews and ensure audit readiness.
• 62% of breaches involved third-party credentials, highlighting gaps in onboarding, monitoring, and offboarding processes.
• 45% of enterprises lacked visibility into IoT device identities, creating significant security gaps.

👉 Read Linx Security's 2024 IAM trends review for 2025 planning

Context

IAM is the control plane that decides who and what can access enterprise systems, data, and workflows. In 2024, the pressure point was not just authentication, but the growing gap between identity sprawl and the ability to govern it consistently across hybrid environments, third parties, APIs, and connected devices.

The article frames 2025 as a year when identity programmes will be judged less by isolated control features and more by how well they unify visibility, lifecycle governance, zero trust, and response. That is directly relevant to NHI, workload identity, and human IAM because the same governance failures now show up across all three.

Key questions

Q: How should security teams reduce identity sprawl without losing control?

A: Security teams should first map where identity data, policy enforcement, and access logging live, then collapse redundant tools that create fragmented governance. The aim is not fewer products for its own sake. It is a single operational view of entitlements, lifecycle state, and high-risk access so reviews, detection, and response all use the same identity truth.

Q: Why do third-party identities create such persistent access risk?

A: Third-party identities often receive access for a specific task, but the access is not always removed when the task ends. That turns temporary business relationships into standing trust relationships. The risk rises when onboarding, monitoring, and offboarding are handled manually or in separate systems, because expired access tends to survive longer than intended.

Q: How can organisations tell whether zero trust is really covering non-human identities?

A: A strong signal is whether APIs, service accounts, and devices are subject to the same continuous verification logic as human users. If machine identities are still governed by static credentials, broad trust zones, or periodic review only, then zero trust is only partially implemented. Coverage should be visible in policy, logging, and enforcement, not just in strategy documents.

Q: What should teams do when AI starts helping with access reviews?

A: Teams should verify the identity data first, then define which decisions AI may support and which remain human-approved. AI can speed up review cycles and flag anomalies, but it cannot correct missing ownership, stale entitlements, or unclear privilege boundaries. If the underlying records are poor, the automation will scale the error instead of reducing it.

Technical breakdown

Why unified identity platforms are replacing point IAM tools

Unified identity platforms centralise policy, visibility, and administration across SaaS, cloud, and legacy systems. The technical driver is identity sprawl: multiple directories, inconsistent entitlements, and disconnected logging make it difficult to enforce least privilege or detect drift. Integration with SIEM, SOAR, and ITDR matters because identity events increasingly need to feed security operations in near real time. Without that linkage, identity controls remain static while the threat surface is dynamic.

Practical implication: inventory duplicated IAM controls, map identity data flows, and reduce point integrations that hide entitlements.

How zero trust extends beyond human sign-in events

Zero trust is not a single product or policy. It is a continuous verification model that requires ongoing trust decisions based on context, behaviour, and privilege. The article’s emphasis on APIs, IoT, and non-human identities reflects a technical shift: authentication alone is insufficient when access is persistent, automated, or machine-originated. Continuous validation only works when identity, device, and workload signals are available to the policy engine at runtime.

Practical implication: extend zero-trust policy design to machine and workload identities, not just employee login flows.

Why AI is reshaping identity threat detection and access reviews

AI in IAM is increasingly used for anomaly detection, access review automation, and policy tuning. Technically, the value is pattern recognition at scale: large identity datasets can surface unusual privilege use, impossible travel, or access paths that manual review misses. The limit is governance quality. If the underlying identity data is incomplete, AI can accelerate bad decisions just as easily as good ones. AI does not replace identity policy, it amplifies it.

Practical implication: validate identity data quality before relying on AI to make or recommend access decisions.

Threat narrative

Attacker objective: The attacker seeks durable, low-friction access that can be used to reach data, systems, or trust relationships without repeatedly breaking in.

1. Entry begins when third-party credentials, IoT identities, or over-permissioned accounts create access paths that are difficult to monitor consistently.
2. Escalation follows when standing privilege, weak lifecycle controls, or incomplete monitoring lets an attacker move from valid access to broader system reach.
3. Impact occurs when the identity layer fails to detect misuse quickly enough, enabling data exposure, compliance failure, or lateral movement across connected systems.

Breaches seen in the wild

• LiteLLM PyPI package breach — LiteLLM PyPI supply chain attack, credentials stolen from users.
• Shai Hulud npm malware campaign — Shai Hulud campaign: npm malware exposed secrets on GitHub.

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

NHI Mgmt Group analysis

Identity consolidation is now a governance problem, not just an efficiency decision. The article correctly shows that scattered IAM tooling creates operational drag, but the deeper issue is control fragmentation. When identity data, access policy, and response tooling sit in separate systems, nobody has a complete view of entitlement risk. That is why consolidation keeps surfacing as a security requirement, not a procurement preference. Practitioners should treat platform sprawl as a governance defect.

Zero trust is no longer credible if it stops at human logins. The article’s point about APIs and IoT devices is directionally right, but the field still treats many machine identities as secondary actors. That assumption fails once workloads, devices, and service accounts become first-class access subjects. Continuous verification must apply to non-human identities because the same access paths that protect employees do not naturally govern machines. Practitioners should expand zero-trust scope across every identity class that can reach production systems.

AI-assisted identity management only works when the identity record is trustworthy. Automation can accelerate reviews and detection, but it cannot repair incomplete entitlements, stale ownership data, or poorly defined privilege models. The article’s optimism about predictive IAM is useful only if organisations first solve data hygiene and lifecycle discipline. Otherwise, AI will simply help teams process bad identity data faster. Practitioners should treat AI as an amplifier of IAM maturity, not a substitute for it.

Third-party access without lifecycle control remains one of the most durable identity failure modes. The article’s emphasis on onboarding and offboarding is the right signal because external access often survives relationship changes long after it should be removed. That creates a standing trust problem across vendors, contractors, and managed service relationships. The field still underestimates how often third-party identities outlive their business purpose. Practitioners should govern external access as a lifecycle process, not a one-time approval.

From our research:

• From our research: 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage. That is why secret governance has to sit inside the identity programme, not beside it, according to Ultimate Guide to NHIs.
• Our research also shows that only 5.7% of organisations have full visibility into their service accounts, which explains why identity sprawl keeps turning into governance blind spots.
• For teams extending IAM into machine and workload identity, the Ultimate Guide to NHIs is the right next step for visibility, rotation, and offboarding patterns.

What this signals

Identity consolidation will increasingly be judged by whether it improves governance outcomes, not whether it simplifies administration. Teams that only merge consoles without normalising lifecycle, logging, and entitlement data will still struggle to govern privileged access. The practical question for 2025 is whether the programme can prove that a given identity has the right access, for the right reason, at the right time.

Third-party and machine identities are converging as a single governance problem. External vendors, APIs, and devices all create access that often outlives the original business need, so lifecycle control becomes the common control plane. Teams should expect audit pressure to move from policy statements to evidence of revocation, expiry, and behavioural monitoring across all non-employee access.

For practitioners

• Rationalise identity tooling around control coverage Map every IAM, IGA, ITDR, and directory function to the controls it actually performs, then remove overlapping tools that create blind spots between policy, logging, and enforcement.
• Extend zero trust to machine and workload identities Apply continuous verification to APIs, IoT devices, service accounts, and other non-human identities using the same policy discipline you apply to high-risk human access.
• Validate identity data before automating decisions Check that ownership, entitlements, and privilege mappings are accurate enough for AI-supported access review, anomaly detection, and policy tuning to be reliable.
• Formalise third-party joiner, mover, leaver workflows Require named owners, expiry logic, and offboarding checkpoints for every external identity so access cannot persist once the relationship or task ends.

Key takeaways

• The article’s main message is that IAM is moving from a support function to the control layer for hybrid security and compliance.
• Its strongest evidence is the growing pressure to consolidate tools, extend zero trust, automate reviews, and govern third-party and IoT identities.
• For practitioners, the work now is to unify identity data, expand lifecycle control, and make machine and external access subject to the same governance standards as employees.

Key terms

• Identity Sprawl: Identity sprawl is the accumulation of identities, entitlements, directories, and access paths across too many systems to govern cleanly. It creates blind spots in ownership, review, and enforcement, especially when human, non-human, and third-party identities are managed in separate toolchains.
• Identity Threat Detection and Response: Identity Threat Detection and Response is the set of controls used to identify, investigate, and contain malicious identity behaviour. It focuses on unusual privilege use, credential abuse, and risky access paths, then connects those signals to response actions that limit damage across hybrid environments.
• Third-Party Identity: A third-party identity is access granted to an external organisation, contractor, or vendor that needs to reach enterprise systems for a defined business purpose. The governance challenge is lifecycle control, because access can remain active after the business need ends unless ownership, expiry, and revocation are enforced.
• Continuous Verification: Continuous verification means access is not trusted once and forgotten. Instead, identity, context, and behaviour are reassessed during the session or workflow so policy decisions can reflect current risk, especially for high-value human, machine, and service identities.

What's in the full article

Linx Security's full review covers the operational detail this post intentionally leaves for the source:

• The vendor's full trend-by-trend breakdown of 2024 IAM shifts and 2025 expectations
• The specific action lists tied to each theme, including consolidation, zero trust, AI, and ITDR
• The original external references that support the vendor's claims and benchmarks
• The company perspective on how to prioritise identity investments across the 2025 programme

👉 Linx Security's full review adds the trend-by-trend detail behind its IAM guidance for 2025

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 programme governance, it is worth exploring.