What Are the Emerging Trends in Identity and Access Management in the Age of AI?

Identity and access management was designed around a simple assumption: a person sits down, logs in, and uses an app. That’s no longer true. Service accounts, API keys, workloads, and now autonomous AI agents make up the overwhelming majority of the identities inside a modern enterprise.

CyberArk's Identity Security Landscape research puts the ratio at roughly 82 machine identities for every human one, and notes that a large share of them hold sensitive or privileged access nobody is watching.

That imbalance is why the emerging trends in identity and access management all point in the same direction: identity is becoming a machine-first discipline. Gartner made the same call in its 2026 cybersecurity trends list, where agentic AI oversight is the number one trend and "IAM adapts to AI agents" appears as a top-six trend in its own right.

Trend 1: Agentic AI Turns Identity Into a Machine-First Problem

AI agents don't just hold credentials; they request them, chain them together, and act on them without a human in the loop. That breaks most of the assumptions your IAM policies were written on.

What "agentic AI" actually means for identity teams

Strip away the marketing and an AI agent is software that can decide what to do next. It reasons about a goal, works out which systems it needs, requests access to those systems, takes action, and often hands work off to another agent. In identity terms, it's an actor that can initiate access requests rather than only receive permissions.

That's a genuine break from the non-human identities you already manage.

Why this is different from a service account

A traditional service account is boring by design. It's provisioned once, given a fixed scope, and does the same thing every day. Its behaviour is predictable, which is exactly what makes it easy to govern.

An AI agent is the opposite:

• It acquires credentials at runtime, often based on what it decides it needs mid-task.
• It orchestrates other agents, creating access paths nobody explicitly designed.
• Its permissions creep upward as it inherits the entitlements of whoever or whatever invoked it.
• Its behaviour changes between runs, so "normal" is a moving target.

Put another way: a service account has a job description. An agent has a goal, and works out the job description itself.

The risk is already priced in by analysts.

Forrester's Predictions 2026: Cybersecurity and Risk report forecasts that an agentic AI deployment would cause a publicly disclosed breach during the year, with staff losing their jobs over it, and its analysts were careful to frame the likely cause as a cascade of small governance failures rather than one person's mistake. Chained agents amplify errors; one bad authorization decision propagates through every downstream step.

IBM's 2026 Cost of a Data Breach research adds the uncomfortable detail: among organisations that suffered AI-related incidents, the vast majority were missing basic controls, role-based access and multi-factor authentication, on the AI models and applications themselves.

This is why AI agent authentication and authorization is becoming its own discipline rather than a footnote in your existing IAM policy. The question has moved from "can this agent log in?" to "what is this agent allowed to do, on whose behalf, for how long, and who answers for it?"

Trend 2: Non-Human Identity Governance Moves Into the Core of IAM

Non-human identities can no longer sit outside your governance perimeter. They need owners, lifecycles, and access reviews, the same as employees.

A non-human identity (NHI) is any identity that isn't a person: service accounts, OAuth tokens and client credentials, API keys, certificates, workload identities in Kubernetes or your cloud provider, RPA bots, and now AI agents.

For years, these lived in a governance blind spot. Human identities went through identity governance and administration, onboarding workflows, quarterly reviews, and offboarding checklists. NHIs got created by a developer on a Tuesday afternoon and quietly outlived the project, the team, and sometimes the developer.

The four things NHI governance has to solve

1. Discovery. You cannot govern identities you cannot see. Most organisations underestimate their NHI count by a wide margin, because these identities are created across cloud consoles, CI/CD pipelines, SaaS integrations, and now agent frameworks.

2. Ownership. Every non-human identity needs a named human owner who is accountable for it. No owner, no justification for its existence.

3. Lifecycle. Machines need joiner-mover-leaver handling too. Created for a purpose, adjusted when that purpose changes, decommissioned when the workload retires.

4. Certification. NHIs belong in your access review cycle, with a reviewer who actually understands what the identity does, not a manager clicking "approve" on a name they've never seen.

The investment is following the risk. Gartner's information security forecast has been revised upward repeatedly through 2026, with the securing-AI segment accounting for a disproportionate share of new spending, an explicit signal that budgets are shifting toward exactly this problem.

Trend 3: Ephemeral, Purpose-Bound Credentials Replace Standing Access

Long-lived credentials are being replaced by short-lived, task-scoped ones that expire on their own. The mental model shifts from granting access to authorising actions.

Standing access, a credential that exists permanently and works whenever it's used, is the single most attacker-friendly pattern in enterprise IT. It's also the default almost everywhere.

The emerging alternative is just-in-time, purpose-bound credentialing. An identity gets a credential at the moment it needs one, scoped to the specific task, valid for minutes rather than months, and revoked automatically when the task completes.

The building blocks

You'll see these named a lot as the pattern matures:

• OAuth 2.1 and scoped tokens: narrow, explicit permissions instead of blanket API access.
• Short-lived tokens with automatic expiry: the credential dies with the task.
• mTLS (mutual TLS): both sides of a connection prove who they are, not just the server.
• SPIFFE and SPIRE: an open standard and implementation for giving workloads cryptographic identities that don't rely on stored secrets.

From "access" to "actions"

The bigger change is philosophical. Traditional IAM answers a question once: is this identity allowed in? AI-era IAM answers a question continuously: is this specific action, by this identity, on this resource, right now, within policy?

That's a meaningful distinction when the identity in question is an agent that might attempt a thousand different actions in an afternoon.

Trend 4: AI-Driven ITDR and Behavioral Anomaly Detection

Identity threat detection and response is growing up. It's moving from generating alerts to closing the loop: detect, contain, find the root cause, and remove the exposure so it can't recur.

Identity threat detection and response (ITDR) is the practice of monitoring identity systems for signs of compromise: unusual logins, token theft, privilege escalation, suspicious changes to your directory.

Early ITDR was largely an alerting exercise, which meant it inherited the alerting problem: too much volume, not enough context, and analysts triaging noise. AI is what's changing the economics.

What AI actually adds on the defensive side

• Behavioural baselines per identity. Instead of static rules, the system learns what normal looks like for a specific user, service account, or agent, which resources, what times, what volumes.
• Anomaly detection that works for machines. "Impossible travel" was a human-centric signal. For an NHI, the equivalents are a workload authenticating from a region it has never used, a service account suddenly touching a database outside its scope, or a token being replayed from a different network.
• Token misuse detection. Spotting a credential being used in a way that doesn't match the context it was issued in.
• Closed-loop response. Automatically revoking a session, disabling an identity, or forcing re-authentication, then feeding the root cause back so the underlying misconfiguration gets fixed.

The cost case is straightforward. IBM's 2026 research put the global average breach cost at an all-time high of roughly $4.99 million, with AI-enabled attacks running about $1 million above that average. Cutting detection and containment time is where most of that money is saved.

Trend 5: AI-Assisted Governance, Access Reviews, Role Mining, and JML Automation

AI is fixing the least popular part of IAM: the reviews, the role sprawl, and the manual joiner-mover-leaver work that nobody has time to do properly.

Anyone who has run an access certification campaign knows the failure mode. A manager receives 300 entitlements to review, understands maybe 40 of them, and approves everything to clear the queue. The review technically happened. The risk didn't go anywhere.

Where AI genuinely helps

• Smarter access reviews

Rather than presenting a flat list, the system highlights what deserves attention: entitlements nobody has used in six months, access that's unusual compared to peers in the same role, permissions that combine into a risky pairing. Reviewers get 15 decisions that matter instead of 300 that don't.

• Role mining

Most organisations accumulate hundreds of overlapping roles over a decade. AI can analyse actual entitlement and usage data to propose a cleaner role model based on how people really work, turning a nine-month consulting project into an analysis you can iterate on.

• Joiner-mover-leaver automation

Provisioning on day one is usually solved. The mover case is where organisations bleed risk, because people change teams and keep their old access. AI-assisted governance flags the entitlements that should have been removed when the role changed.

• Separation of duties (SoD) conflict detection

Instead of a static conflict matrix that ages badly, the system surfaces emerging combinations, such as the person who can now both create a vendor and approve a payment to it.

For compliance and GRC teams, this is the difference between an audit trail that says "a review occurred" and one that shows a defensible, risk-based decision. Pairing it with well-structured employee access management means the day-to-day access people need is standardised, so the exceptions genuinely stand out.

Trend 6: Shadow AI Agents Become the New Shadow IT

Business teams are spinning up AI agents with borrowed credentials and no identity owner. It's shadow IT again, except this time the unmanaged asset can take actions on its own.

A shadow AI agent is an autonomous AI workflow running inside your environment that your security team doesn't know about. Someone in marketing connects an agent framework to the CRM. A finance analyst builds an agent that reads invoices from a shared mailbox. Neither went through review, and neither agent has a registered identity, an owner, or an expiry date.

Low-code and no-code platforms make this trivially easy, which is precisely why Gartner flagged unmanaged AI agent proliferation as a defining feature of its top 2026 cybersecurity trend, and recommended that leaders inventory both sanctioned and unsanctioned agents.

Why shadow AI is harder than shadow IT was

Shadow IT was mostly about data sitting somewhere it shouldn't. A rogue Dropbox folder is a storage problem. A rogue AI agent is an action problem: it holds credentials, calls APIs, modifies records, and can trigger other systems.

IBM's 2026 breach research found the share of security incidents involving shadow AI more than doubled year over year, reaching 43%, while roughly two-thirds of organisations still had no governance process capable of detecting it.

Discovery is the first control, not the last.

You can't apply policy to something you haven't found. Practical starting points:

• Audit OAuth consent grants across your SaaS estate; this is how most agents get their access.
• Review API key and token issuance for credentials with no registered owner.
• Watch for service accounts with unusual, bursty access patterns that don't match a scheduled job.
• Give teams a fast, sanctioned path to register an agent. If the approved route takes three weeks, shadow agents win by default.

Trend 7: Phishing-Resistant Authentication vs AI-Powered Attacks

In short: Attackers now have AI too. Passwordless, phishing-resistant authentication has quietly become the baseline expectation rather than a competitive advantage.

What attackers gained

Generative AI removed the friction from social engineering. Phishing emails that once read like obvious translations now match a company's internal tone. Voice cloning makes a call from "the CFO" plausible. Credential stuffing runs at machine speed against reused passwords.

Gartner has projected that AI agents will cut the time needed to exploit account exposures by half by 2027, by automating steps of account takeover that previously needed a human, and that by 2028, a significant share of social engineering attacks will target executives specifically. IBM's 2026 data supports the shift on the ground: roughly one in four malicious breaches were AI-enabled, a sharp year-over-year jump, with deepfake impersonation among the most common techniques.

What should defenders do about it?

The answer is unglamorous and well established:

• FIDO2 passkeys and hardware security keys. These bind authentication to the legitimate domain, so a convincing fake login page has nothing to steal. This is the single highest-leverage control against AI-crafted phishing.
• Phishing-resistant MFA. Retire SMS and voice one-time passcodes where you can. Push-notification approvals are better, but still vulnerable to fatigue attacks.
• Verified device signals. Factor device posture, management status, and certificate health into the access decision.
• Out-of-band verification for high-value actions. For payment changes or privileged access, verify through a second channel that a deepfaked voice can't reach.

A note on positioning: passwordless is table stakes now, not a differentiator. If your roadmap still treats it as a future project, that's the gap worth closing first.

Trend 8: Delegation Chains, Accountability, and the AI Kill Switch

Every AI agent needs a traceable line back to an accountable human, and a way to shut it down instantly when something goes wrong.

When an agent takes an action, someone has to be answerable for it. That's not just an ethics point; it's an audit requirement, an incident response requirement, and increasingly a regulatory one.

The delegation chain

A delegation chain records the authority behind an action: this agent acted on behalf of this workflow, which was authorised by this human owner, under this policy, with this scope, at this time. Where agents call other agents, the chain has to survive each hop; otherwise, you end up with an action that's fully logged and completely unattributable.

Getting this right means:

• Registered ownership for every agent, with a named human, and a rule that ownership transfers when people change roles.
• Full audit trails capturing not just authentication events but the actions taken and the authority they were taken under.
• Propagated context, so the original requester's identity isn't lost when agent A hands off to agent B.

Why the kill switch matters

Automation that can act at machine speed can also cause damage at machine speed. You need the ability to stop it in seconds, not after a change-approval meeting:

• Immediate token revocation, including tokens already in flight.
• Emergency deprovisioning that disables an agent's identity across every connected system at once.
• Blast-radius containment, so shutting down one agent doesn't require taking down the platform.
• A tested procedure. A kill switch nobody has rehearsed is a hypothesis.

For a deeper look at implementation patterns, see our guides on AI kill switch architecture and fine-grained authorization for RAG pipelines, which cover how to keep an agent's retrieval scope inside the requesting user's permissions.

How to Prepare Your IAM Stack for the AI Era

None of this requires ripping out what you have. It requires extending it to identities you weren't previously counting. Here's what that looks like by role.

If you're a CISO

Your job is visibility and a defensible narrative.

• Get an accurate count of non-human identities, and how many hold privileged access. This is usually the number that changes the conversation with your board.
• Add AI agent exposure to your risk register as a named line item, not a subsection of "AI."
• Establish who owns agent governance across security, IT, legal, and the business. IBM's 2026 research found that only about one in five organisations had coordinated governance and security efforts at all.
• Set a policy position on what agents may never do without human approval: move money, change access, contact customers, deploy code.

If you're an IT Admin

Your job is inventory and enforcement.

• Discover and register every non-human identity. Assign a named owner. Delete anything that can't be justified.
• Rotate or eliminate long-lived secrets. Prioritise anything with privileged scope.
• Move privileged access to just-in-time issuance, starting with your highest-risk systems.
• Audit OAuth consent grants; this is where shadow agents hide.
• Enforce phishing-resistant MFA for all administrative access, without exception.

If you're in SecOps

Your job is detection and response for identity.

• Build ITDR playbooks specifically for non-human identities. "Contact the user" doesn't work when the identity is a container.
• Baseline normal behaviour for your top agents and service accounts, and alert on deviation.
• Rehearse the agent kill switch as a tabletop exercise. Time it.
• Instrument agent action logs into your SIEM, not just authentication events.

If you're in Compliance or GRC

Your job is provable control.

• Extend access certification campaigns to cover non-human identities, with reviewers who understand what they're certifying.
• Require a documented business justification and owner for every agent before it reaches production.
• Map agent activity to your existing SoD controls; an agent inherits whatever conflicts its permissions allow.
• Confirm your audit trail can answer the regulator's question: who authorised this action, and on whose behalf was it taken?

Where to start if you can only do one thing: inventory. Every other control depends on knowing what exists. Consolidating onto centralized identity management makes that inventory dramatically easier to keep accurate, because identities stop being created in a dozen places you don't monitor.

How miniOrange Secures Human and AI Agent Identities

miniOrange has spent years securing workforce and customer identities for organisations across regulated and complex environments, single sign-on, multi-factor authentication, privileged access, and identity governance, deployed at scale. That existing foundation is what agent identity controls build on, rather than replace.

AI agent identity capabilities

• OAuth 2.0 token-based authentication for agents and machine workloads, so agents authenticate as first-class registered identities rather than borrowing a human's credentials.
• Scoped access, restricting each agent to the specific resources and operations required by its task.
• Audit trails covering agent authentication and access events, to support delegation-chain accountability.
• Token revocation, enabling rapid shutdown of an agent's access when behaviour looks wrong.

Deployment flexibility

This is where miniOrange differs from most of the market. Our identity platform deploys in the cloud, on-premise, including fully air-gapped environments, or in a hybrid model that spans both. Cloud-first competitors such as Okta and Microsoft Entra ID are cloud-only, which is a hard constraint for defence, government, healthcare, banking, and industrial organisations that cannot send identity data off-premise. If your environment includes systems that will never touch the public internet, your identity layer needs to work there too.

FAQs

What is agentic AI in the context of IAM?

Agentic AI refers to systems that pursue goals autonomously, deciding which systems to access, requesting permissions, and chaining actions together. For IAM, that means treating AI agents as identities that initiate access requests.

What is a non-human identity?

A non-human identity is any identity that doesn't belong to a person: service accounts, API keys, OAuth tokens, certificates, workload identities, RPA bots, and AI agents.

How does AI improve identity threat detection and response?

AI builds behavioural baselines for each identity, then flags deviations that static rules would miss: a workload authenticating from a new region, a token used out of context, a service account reaching outside its normal scope.

What is an ephemeral credential?

An ephemeral credential is a short-lived, task-scoped credential issued when it's needed and automatically revoked when the task ends. Because it doesn't persist, there's nothing durable for an attacker to steal, reuse, or find in a code repository months later.

How do you govern AI agents?

Start with discovery, then assign a named human owner to every agent. From there: scope permissions to the specific task, issue short-lived credentials, log all actions with delegation context, include agents in access reviews, and maintain a tested revocation path.

Can IAM still run on-premise in the AI era?

Yes. Regulated, air-gapped, and sovereign environments still require on-premise identity and modern controls, scoped tokens, just-in-time access, and agent governance in those deployments. Some cloud-only vendors can't support it, so verify deployment options against your compliance obligations.