Many engineering teams treat zero trust as a simple MFA checkbox. They invest in advanced identity providers but still leave environments exposed, with permanent admin roles and manual ticket queues that frustrate developers. 

Most teams have adopted the language of zero trust without changing how access actually works. They verify identity at login, then leave broad permissions in place long after the task is done.

Yet 59% of organizations now rank insecure identities and risky permissions as their number one cloud security risk. When you rely on standing permissions, you’re just waiting for a single compromised credential to turn into a full-scale breach. 

To survive modern infrastructure, it’s vital to stop treating identity as a static perimeter and start treating every access request as a high-risk, time-bound event. 

What is zero trust identity and access management?

Zero trust Identity and Access Management (IAM) is a security framework based on the principle of “never trust, always verify.” The goal is simple: never trust by default. In this framework, being on the network doesn’t grant you a free pass. 

Every access request should be authenticated and authorized in real time, whether it’s a developer requesting temporary access to a production Kubernetes cluster or a CI/CD runner assuming a cloud role. Decisions should reflect the current context, including device posture, workload identity, approval status, and resource sensitivity.

At the center of zero trust IAM is least privilege, a core principle of identity governance. Users and services should have only the permissions they need to complete a specific task, and nothing more. 

Rather than leaving elevated permissions in place indefinitely, teams use Just-In-Time (JIT) access to grant them only when needed and revoke them automatically when the task is done. This turns access into a time-bound, context-aware control instead of a persistent entitlement.

In practice, zero trust IAM has to work across the full engineering stack: cloud consoles, Kubernetes clusters, databases, internal apps, SSH workflows, and CI/CD pipelines. It also has to apply the same policy logic to human users, service accounts, runners, and third-party access paths, rather than treating each environment as a separate exception. 

Why Zero Trust Identity and Access Management Matters for Cloud-Native Teams

Cloud-native teams need a security model that verifies every connection in real time based on current context. Traditional IP-based security is outdated because a fixed perimeter cannot be relied on, given the constantly changing infrastructure across clouds, Kubernetes, and CI/CD pipelines.

From a security standpoint, zero trust IAM helps contain permission sprawl by replacing broad, static access with fine-grained, identity-based policies. The attack surface is significantly reduced when short-lived tokens are used in place of standing privileges. Even if a credential is leaked, its limited scope and duration prevent attackers from moving laterally across your infrastructure.

Beyond security, there is a massive operational advantage for developers. Cloud-native teams do not just manage employee logins. They manage ephemeral workloads, short-lived build jobs, service accounts, database roles, cross-cloud identities, and emergency production access under pressure. All these factors affect IT service continuity during outages and high-impact incidents. 

Traditional IAM often relies on ticket-based security, where a developer might wait days for a manual approval to access a production database. Zero trust IAM supports automated self-service through JIT access, so engineers can request the exact permissions they need for a specific task and receive them quickly if policy conditions are met.

Zero trust IAM solves the headache of compliance and auditing. When access requests, approvals, grants, and revocations are routed through centralized workflows and policy enforcement, teams can build a more complete audit trail across environments. That makes it easier to show who accessed what, when, why, and under what approval path. 

7 Principles of Zero Trust Identity and Access Management

Principle 1: Verify Every Identity, Request, and Session

Trust is never granted by default to any user, device, or service. Whether an access attempt comes from inside or outside the network, it must be authenticated and authorized every time. 

Relying on “network trust” from a VPN or office Wi-Fi is a dangerous fallacy that doesn’t hold up in a cloud-native world. Once an attacker has gained access to one entry point, they can move laterally across a network by using the “hidden” trust that is eliminated by verifying each session.

That verification mindset is especially important in cloud-native environments, where a single compromised identity can quickly lead to lateral movement and weaken brand protection strategies.

Actionable Implementation Tips

Move away from long-lived session cookies and static passwords. Instead, implement phishing-resistant Multi-Factor Authentication (MFA) and use short-lived, scoped credentials over long-lived secrets or static sessions. These should require re-authentication based on the specific risk or duration of the session.

Principle 2: Enforce Least Privilege by Default

Least privilege ensures that every identity, whether a human developer or a background service, is granted only the absolute minimum permissions necessary to complete a specific task. Least privilege is how you limit the “blast radius” of a breach. If a developer’s credentials are stolen, but their access is restricted to a single non-production database, the impact is minimal. Without these limits, one compromised account can give an attacker keys to everything.

Actionable Implementation Tips

Conduct a comprehensive permissions audit to identify “shadow admins” and overprovisioned roles. You need to move away from generic “Cloud-Admin” roles in favor of granular permissions. By mapping these to documented job functions, you ensure that access is limited to the specific resources a role actually requires.

Principle 3: Replace Standing Permissions With Just-In-Time (JIT) Access

JIT access replaces persistent privilege with temporary elevation. Instead of assigning admin access around the clock, teams grant it only for a defined task, with approvals, scope limits, and automatic expiration built in.

Actionable Implementation Tips

Deploy a JIT access broker that integrates with your SSO. For example, you could use 60-minute temporary tokens for production Kubernetes access. Setting a hard expiration means that stale access doesn’t stay active after the task is finished.

This is where a cloud native access management platform helps operationalize this model by replacing standing permissions with automated, time-bound access workflows across cloud, Kubernetes, databases, and internal systems.

Principle 4: Use Context-Aware Access Policies

Access decisions should reflect real-time signals such as device posture, location, time of day, resource sensitivity, and insights from threat intelligence tools. Identity alone is no longer enough. A valid username and password coming from an unmanaged, malware-infected laptop in a different country should be flagged as high-risk. Context-aware policies allow the IAM system to adapt its strictness based on the current threat level. 

Actionable Implementation Tips

Integrate your IAM provider with your Endpoint Management (MDM) software. Create a policy that automatically denies access to production environments if the requesting device is not encrypted or is running an outdated operating system.

Principle 5: Secure Human and Non-Human Identities

Zero trust IAM should apply to non-human identities just as strictly as it applies to human users. Automated infrastructure means non-human identities now have more permissions and higher numbers than the people using them. Because these identities often use static, plaintext secrets, they pose a significant risk. Zero trust must extend to every machine-to-machine interaction.

Actionable Implementation Tips

Eliminate static secrets by using Workload Identity Federation. This allows your CI/CD pipeline or cloud services to exchange a short-lived identity token for temporary cloud permissions, removing the need to store sensitive “secret keys” in your code or configuration files.

Principle 6: Apply Zero Trust Across Cloud, Kubernetes, Databases, and CI/CD

To apply zero trust across the board, a unified identity layer must govern the entire technical stack rather than relying on siloed security for each platform, while complementary practices like continuous threat exposure management help teams validate that cloud-native systems remain secure as environments change. Security is only as strong as its weakest link. If you secure the cloud console but leave backend databases accessible via a shared password, your framework has already failed. Consistency across environments prevents “backdoor” access and ensures that security policies are applied universally. 

Actionable Implementation Tips

For internal apps and legacy systems, route access through centralized identity-aware controls where possible. For databases and infrastructure, use short-lived credentials, approval-based elevation, and workload-native identity mechanisms instead of shared secrets.

Using a cloud native zero trust solution gives teams a single way to govern access across infrastructure and applications without forcing engineers through slow, manual ticket queues.

Principle 7: Automate Access Requests, Approvals, and Revocation

Manual identity management rarely scales well in fast-moving cloud-native environments, which inevitably leads to privilege creep and leaving unnecessary access active indefinitely. Moving to software-automated access solves this by ensuring permissions are revoked the instant they are no longer required. Automation ensures that permissions are granted for legitimate needs and removed the instant they aren’t required, closing security gaps.

Actionable Implementation Tips

Set up ChatOps-based workflows (e.g., via Slack or Teams). A developer can type a command to request access; their manager receives a notification and clicks “Approve,” which triggers an automated script to provision temporary access that self-destructs after the task is complete.

With a cloud native access management platform like Apono, those requests can happen directly in Slack, Teams, or CLI, with approvals, provisioning, and revocation handled automatically.

Make Zero Trust Access Practical for Cloud-Native Teams

Zero trust identity and access management only works when access is continuously evaluated, least privilege is enforced by default, and standing permissions are removed wherever possible. For cloud-native teams, that means controlling access not just at login, but across Kubernetes, cloud infrastructure, databases, CI/CD pipelines, internal apps, and machine identities. 

That is the gap many teams still need to close. Zero trust only works when access controls are practical enough for engineers to use and strong enough for security teams to trust.

See how Apono helps teams replace standing permissions with just-in-time, self-serve access across cloud, Kubernetes, databases, and CI/CD.

Book a personalized demo

There’s never a good time to disclose a breach, but days before your IPO has to rank near the bottom of the list. That was the backdrop to the Vercel breach.

On Sunday the 19th, the company confirmed that attackers had walked into parts of its internal environment and walked back out with customer API keys.

Early reporting focused on the flashy parts: an attacker claiming ties to ShinyHunters, a $2 million BreachForums demand, crypto teams rotating credentials with the IPO roadshow in full swing. But trace the incident back to its origin and it doesn’t start with a zero-day. It starts with a checkbox.

A Vercel employee signed up for Context.ai, a third-party “AI Office Suite,” using their enterprise Google Workspace account. The OAuth consent screen asked for access, and the employee clicked Allow All.

Months later, Context.ai itself got hit by infostealer malware. The attackers walked into its internal systems, found every OAuth grant the company held on behalf of its customers, and used the Vercel employee’s grant to take over their Workspace account and pivot into Vercel’s internal environment.

That single click is really the whole story, because everything that followed traces back to one identity holding more standing access than it should have, and one OAuth token quietly inheriting all of it.

The Vulnerability Wasn’t the AI Tool

It’s tempting to frame this as “Context.ai got breached, therefore third-party AI is dangerous,” and while that’s true, it isn’t the useful lesson here.

The real story is that Vercel’s own access posture decided how bad Context.ai’s breach could get. A vendor’s incident became Vercel’s incident because the trust between them was static, broad, and permanent.

“Allow All” meant an OAuth token, held indefinitely by a third party, could act as the employee inside Workspace. It survived weekends, vacations, and stretches where nobody even used the product.

When it eventually got stolen, it was still valid, still broadly scoped, and still invisible to security.

Vercel’s guidance afterward was to flag more environment variables as sensitive. That’s useful tactical advice, but it treats a symptom, because any privilege left standing is a privilege available to whoever steals the identity that holds it.

The harder fix, and the real one, is to stop leaving privileges standing at all.

How Zero Standing Privileges Could Have Changed the Vercel Breach

That’s the gap Zero Standing Privileges (ZSP) is built to close. The premise is simple: no identity, whether human, service, or agent, holds permanent access to privileged resources. Access is requested when needed, granted Just-in-Time at Just-Enough scope, and revoked automatically when the task is done or the timer expires.

Apply that model to each hop of the Vercel chain and the blast radius collapses at three clear points:

• At the OAuth hop: “Allow All” becomes a scoped, time-bound request that runs through policy.
  
  • The AI tool gets only what it actually needs for the hour, not the whole Workspace forever
  • By the time Context.ai’s token is stolen, it’s already expired or scoped down to nearly nothing
• At the Workspace hop: The employee’s account doesn’t carry standing access to Vercel’s internal infrastructure.
  
  • Compromising the Workspace account gets you into that account, and nothing more
  • Production is a separate, time-bound request with its own approval path, so the lateral movement never happens
• At the environment-variable hop: Nobody holds a persistent right to enumerate those variables in the first place.
  
  • The “sensitive” versus “non-sensitive” label stops being load-bearing
  • Exposed API keys stay behind a control the attacker has to actively request, which is both harder and more visible

None of these are speculative controls. This is how modern privileged access management is supposed to work, and the tooling to do it already exists.

What’s missing in most organizations is the operational commitment to stop treating OAuth consent, SaaS account access, and cloud IAM roles as set-and-forget.

Governing OAuth Itself

Most identity programs stop at the front door. They know who logs in, from where, and with what factor.

What they don’t govern is what third-party apps those users authorize once inside. That’s the exact gap this breach exploited.

OAuth grants deserve to be treated as privileged access in their own right: discoverable, reviewable, and time-bound wherever possible.

Your people should be able to try a new AI tool without handing it a permanent backstage pass to Workspace. Your platform team should know which vendors hold tokens and which have gone idle long enough to revoke.

Agents, copilots, and SaaS-to-SaaS integrations all consume OAuth now. The shadow identity sprawl inside Workspace and Microsoft 365 is where the next breach is already forming.

The Agentic Era Raises the Stakes

OAuth tokens aren’t mostly issued to humans anymore. They’re increasingly issued to AI agents, copilots, and SaaS integrations that act on behalf of humans.

Every one of those is an identity that can be compromised somewhere up the supply chain. Everyone benefits enormously from the lazy convenience of “Allow All.”

Keep giving agents the same blank-check access we used to give humans, and the identity surface grows faster than anyone can audit. ZSP is how it stays manageable, and how a vendor’s bad day stops being your bad day.

The lesson worth taking away

The Vercel breach isn’t really a story about Context.ai, Lumma Stealer, ShinyHunters, or even AI. It’s a story about what happens when standing privileges meet a determined attacker.

The mechanism changes every cycle, but the underlying exposure never does.

If your organization still lets employees grant enterprise-wide OAuth scopes with a single click, still issues production credentials that live forever, still treats access as something you configure once and forget, you are one stolen token away from the same headline.

The fix isn’t to panic about AI tools. It’s to make every privileged access grant temporary, scoped, and approved by default.

That’s what Zero Standing Privileges means in practice, and the bar every security program should now be building toward.

If this is the week your team starts rethinking standing access, our 2026 Buyer’s Guide to Privileged Access Management Platforms is a useful next step. It walks through what modern Cloud PAM actually looks like and gives you a framework for comparing it to what you have in place today.

Hims & Hers, one of the biggest telehealth platforms in the U.S., just disclosed that millions of customer records were exposed. Not because of some sophisticated exploit, but because a single compromised login had standing access to a connected platform. 

One identity was all it took.

This breach is worth paying attention to not because it’s unusual, but because it’s so ordinary. 

The access model that made it possible is the same one most companies are still running, and it raises an uncomfortable question: could your vendors prove their privilege controls hold up if someone came looking?

How the Hims Data Breach Unfolded

In early February 2026, the ShinyHunters ransomware gang targeted Hims & Hers as part of a broader campaign against companies using Okta for single sign-on. 

They ran the same play they’ve been running for years: 

• Impersonate IT support
• Call employees
• Talk them into entering credentials and MFA codes on phishing pages

Once inside the compromised Okta SSO account, the attackers didn’t need to escalate privileges or move laterally. The account had standing access to the Hims & Hers Zendesk instance, so they walked straight in and helped themselves to millions of support tickets between February 4th and 7th.

The exposed data included customer names, contact information, and details from support interactions. 

This isn’t the first time ShinyHunters has used a third-party vendor as the way into an enterprise, compromising SSO credentials to slip through vendor platforms and steal customer data from the companies on the other side.

How Third-Party Vendor Access Puts Your Data at Risk

Third-party vendors make attractive targets because they hold broad, persistent access to their customers’ environments, often without the same security oversight those customers apply internally.

That creates risk on both sides. Vendors that suffer a breach become the reason their customers end up in disclosure letters and regulatory conversations. 

For the enterprise, every vendor with standing access to sensitive systems is an extension of your own attack surface that you have less visibility into and less control over.

Enterprise buyers have noticed, and they increasingly want proof that partners manage privileged access with real, auditable controls. 

In healthcare, if a vendor can’t demonstrate least privilege, why would a covered entity hand them ePHI? Proving it starts with how you think about standing access in the first place.

Why Zero Standing Privileges Matters for Healthcare

Zero Standing Privileges (ZSP) is the access model that would have prevented the Hims breach from playing out the way it did: no account should hold continuous privileged access by default. It works through two mechanisms:

• Just-in-Time (JIT) access grants privileges only when they’re needed and revokes them the second the task is done.
• Just-Enough Privilege (JEP) scopes those privileges to exactly what the task requires.

If that compromised SSO account had no standing access to Zendesk, the attacker would have taken over the account and found nothing to use.

This is also what the regulations describe. HIPAA’s Security Rule (45 CFR § 164.312) mandates access controls that limit ePHI to authorized individuals, audit mechanisms for every access event, and workforce security measures that revoke privileges when tasks are complete.

The Minimum Necessary Standard (45 CFR § 164.502(b)) requires access be limited to only what’s needed for the specific task. PCI-DSS, SOC 2, ISO 27001, and NIST all land in the same place.

ZSP handles the human side of this problem. But the vendor trust conversation is about to get harder, because human credentials aren’t the only ones with standing access anymore.

AI Agents and the Next Wave of Vendor Access Risk

Healthcare companies are starting to roll out AI tools: coding copilots for engineering teams, customer-facing agents that handle intake data and scheduling. These agents inherit their users’ credentials by default.

Now consider the Hims scenario again, but with agents involved. If a vendor’s customer-facing agent has standing access to ePHI and that vendor’s SSO gets compromised, an attacker isn’t just reading support tickets anymore. They’re potentially accessing whatever those agents can reach, at whatever speed those agents operate.

As more healthcare companies push their vendors to adopt AI tooling, the question of how those vendors govern agent access becomes part of the trust conversation too.

How Apono Enforces Zero Standing Privileges

Whether you’re a vendor trying to prove to healthcare customers that their data is safe with you, or an enterprise trying to hold your vendors to a higher standard, the practical challenge is the same: you need ZSP to actually work in your environment without slowing your teams down. 

That’s what Apono is built to do.

Apono creates ephemeral privileges at the moment of the request, scoped precisely to the task, and eliminates them the instant the work is done. No pre-built roles to activate, no static RBAC structures to layer on top of.

Most PAM tools require someone to pre-define a library of roles that need ongoing maintenance and right-sizing. Apono sidesteps that by generating roles and permissions dynamically, in the native policy language of the target environment, so privileges are right-sized by default.

For AI agents, Apono’s Intent-Based Access Controls evaluate what the agent is trying to do against the sensitivity of the privileges being requested. Low-risk actions proceed automatically, sensitive operations get routed to a human, and everything is logged end to end.

For healthcare organizations, here’s how that maps to the requirements auditors are actually checking:

Mapping Apono to HIPAA Requirements

Learn more about how Apono helps to secure your ePHI and simplify HIPAA compliance with our HIPAA and HITECH Compliance data sheet. 

Proving Privileged Access Controls Wins Business

The Hims breach is a reminder that protecting health data isn’t just about satisfying auditors. It’s about whether customers and partners are willing to do business with you at all.

If you’re rethinking how your organization manages privileged access after stories like this, our 2026 Buyer’s Guide to Privileged Access Management breaks down what modern ZSP-ready solutions should look like, what to ask vendors, and how to compare options on the capabilities that actually matter. It includes a full RFP checklist you can use in your evaluation today.

Today, we’re introducing Approval Escalation, a new capability in Apono that automatically moves access requests forward when the original approver doesn’t respond in time.

Because no one should be stuck waiting seven hours just to do their job.

The Approval Bottleneck is Costing You More Than You Think

Most access doesn’t need a human in the loop. Modern access management platforms like Apono let organizations automate the majority of access requests through self-serve workflows, policy-based auto-approval, and Just-in-Time provisioning. For routine access, users get what they need in seconds without anyone having to click “approve.”

But some resources are sensitive enough that you genuinely want a human making the call. Production databases, admin-level cloud permissions, compliance-restricted environments. For these, manual approval isn’t a broken process; it’s the right control.

The problem is what happens when that manual approval stalls.

Industry data paints a pretty stark picture. The average time to manually approve an access request across organizations is 430 minutes. That’s over seven hours of someone being completely blocked from doing critical work.

Now here’s the interesting part. Half of all manual approvals actually happen in about a minute. The problem isn’t the typical request. It’s the outliers, the requests that take hours or even days, dragging the average up and creating real pain for the people stuck waiting.

And that pain is real. Even global admins can feel powerless when an approver simply isn’t responding. There’s no override, no break-glass option. Just waiting.

Why This Keeps Happening

The reasons behind stalled approvals are frustratingly mundane:

• The approver is out of office
• They’re stuck in back-to-back meetings
• They’re in a different time zone and your urgent request landed in their inbox at 2am
• The notification got lost in a flood of emails and Slack messages

None of these are edge cases. They’re everyday realities. And without a system to handle them, every one of these situations turns into a productivity black hole.

How Approval Escalation Works

The concept is straightforward: if the first approver doesn’t respond within a configurable time limit, the request automatically moves to a backup approver. No manual follow-ups, no chasing people down, no requests dying in a queue.

Here’s the flow:

1. A user submits an access request and a clock starts ticking
2. The time limit expires without a response from the primary approver
3. The system escalates the request to a designated backup approver
4. The backup approver acts on the request, and the user gets unblocked

The key design decision here is that escalation is fully automated, based on a preset time limit that admins configure. It’s predictable and consistent, which means teams can rely on it without worrying about gaming or abuse.

In this Access Flow, we see that if someone from the manager group doesn’t approve the request within 30 minutes, that it gets escalated to Gabriel. Then if Chris doesn’t respond in an hour’s time, it goes to Christine. 

This approach allows layering to ensure that the request gets the attention it deserves and our engineer can get his or her work done.

Context is Everything

One thing we were intentional about: the backup approver can’t make a good decision if they don’t understand why a request just landed in their lap.

So when a request escalates, the new approver sees all the context they need to act quickly:

• Why the request was escalated (the original approver didn’t respond)
• Who the original approver was
• How much time has already passed
• All the original details of the request itself

No guesswork, no Slack threads asking “wait, what is this?” Just enough information to make a fast, informed decision.

What this Solves

When you put it together, Approval Escalation directly attacks the core problems that stalled approvals create:

• User frustration drops because people aren’t left in limbo with no recourse
• Wait times shrink because requests always have a path forward, even when someone is unavailable
• Operational costs decrease because you’re no longer paying people to sit around blocked

This isn’t a nice-to-have. In a world where speed and agility define how well teams operate, you can’t afford to have your workflows grind to a halt for hours at a time because one person didn’t check their email.

Get Started with Approval Escalation

Access requests shouldn’t stall just because someone is in a meeting or on a different continent.

Approval Escalation ensures that every request has a path to resolution, keeping your team moving and your access workflows scoped, time-bound, and fully auditable.

If you’re looking to eliminate approval bottlenecks and keep Just-in-Time access flowing smoothly across your organization, we’d love to show you how it works.

Book a demo to see Approval Escalation in action.

Today, we’re introducing Justification Coach, a new AI-powered capability that helps users write better access request justifications in real time, so admins get the context they need for audits and investigations without having to chase people down after the fact.

The Justification Problem No One Talks About

Every access governance platform asks users to justify their requests. It’s a cornerstone of compliance and a key input for auditors. In theory, it works great.

In practice? We keep hearing from customers that the justifications their teams are writing just aren’t cutting it.

Some are obviously junk: 

• Random characters 
• Whitespace 
• Exclamation points
• Completely empty fields

Others are real words but barely more helpful, things like “access,” “working,” “dev,” or just a single abbreviation.

When we looked at the data, over 42% of justifications fell into one of these categories. And the rest weren’t necessarily much better, with many clocking in at just two or three words that still don’t communicate any real intent.

That matters because justifications serve two critical purposes.

• In a compliance audit, they’re how you demonstrate why someone had access to a sensitive resource. If an auditor asks “why was this person granted production access?” and the answer on record is “asdf,” that’s a problem you have to explain.
• During an incident investigation, justifications are supposed to help admins reconstruct what happened and why elevated access was granted. A one-word justification gives them nothing to work with.

Why Users Write Bad Justifications

It’s tempting to blame lazy users, but the reality is more nuanced.

Most access request forms give users a blank text box with no guidance on what a good justification looks like. There’s no feedback, no structure, and no indication of what “good” even means in that context.

So users do what people naturally do with unstructured fields under time pressure: they write the bare minimum and move on.

Some admins we spoke with told us they’ve resorted to manually rejecting requests with poor justifications, essentially playing a back-and-forth rejection game until the user provides something useful. That’s painful for everyone involved, and it doesn’t scale, especially when approver quality varies and not every approver is trained to enforce the same standard.

How Justification Coach Works

Instead of punishing bad justifications after the fact, we built a system that guides users toward better ones in the moment.

Think of it like the password strength meter you’ve seen on every signup form. As you type your justification, AI evaluates it in real time against a set of quality criteria and gives you visual feedback on how well your justification communicates your intent.

The validation looks at whether you’ve:

• Described your task so it’s clear what you’re actually trying to accomplish
• Justified the level of permission relative to what you’re requesting
• Included an action or verb that indicates specific intent
• Provided a reference like a ticket ID that ties the request to a trackable workflow

Because the AI also has context about the resource you’re requesting, the permission level you selected, and other metadata, it can make intelligent assessments. 

If you’re requesting admin-level access but your justification only describes a read operation, the validation will flag that mismatch and nudge you toward a more precise request.

Enablement First, Enforcement later

One design decision we were deliberate about: in this initial release, Justification Coach is advisory, not blocking. Users see the feedback and quality indicators, but they can still submit their request even if the justification doesn’t hit every mark.

Why? Because we talked to customers and found something interesting.

Admins universally wanted this feature, but they also told us they preferred starting with enablement over enforcement. The goal right now is to teach users what a good justification looks like and build the habit organically, not to create a hard gate that adds friction without context.

That said, enforcement is on the roadmap. Customers have already asked for the ability to configure which access flows require a minimum justification quality, so admins will eventually be able to set that threshold per access flow based on sensitivity.

What’s Next: From Validation to Intent

This feature is part of a bigger story around how Apono understands user intent.

Right now, we’re setting up the guardrails that improve justification quality across the board. Over time, as that quality improves, the data opens up new possibilities:

• Smarter autocomplete based on context
• Pre-filled justifications based on similar past requests
• Deeper integration with ticketing systems to automatically pull context from linked issues

The vision is straightforward. The better Apono understands what a user is trying to do and why, the better it can streamline the entire access experience, from request to approval to audit, while keeping access scoped, time-bound, and fully documented.

See it in Action

Bad justifications aren’t just an annoyance. They’re a compliance gap and an investigation blind spot.

Justification Coach helps close that gap by guiding users toward meaningful justifications at the point of request, reducing admin overhead and producing audit-ready access records without any additional configuration.

Book a demo to see how Justification Coach can strengthen your access governance.

For many enterprise security teams, audit season feels less like validation and more like reconstruction. Not because they lack logs, and not because their teams are careless, but because their privilege model was never designed to produce a clean, unified story. In Microsoft Entra ID environments, Privileged Identity Management (PIM), works well as long as your world is entirely Microsoft.

But no enterprise operates in a single-vendor bubble.

The moment your infrastructure spans Azure, AWS, Kubernetes, databases, SaaS platforms, and/or hybrid systems, auditing privileged access becomes fragmented. What was once a straightforward activation workflow turns into a cross-system investigation exercise.

Asset Type	Tooling Required
Azure	Entra ID PIM
AWS	AWS IAM + Identity Center
GCP	Google Cloud IAM + PAM
Kubernetes	RBAC + manual namespace policies
Databases	Native DB permissions + manual access controls
SaaS platforms	Per-app admin consoles (Salesforce, Okta, GitHub, etc.)
Hybrid / on-prem	Active Directory + Legacy PAM

That is not a configuration issue, it’s a control model issue.

Where the Friction Actually Shows Up

Inside Entra, PIM can require approval, time-bound role, and log activation. Within Azure, that can be enough to show that elevation occurred.

But auditors for SOC 2, ISO 27001, and other compliance frameworks do not ask whether elevation occurred. They ask broader questions. Who had privileged access to production? Under which approval? For how long? What actions did they take? Was this enforced consistently across environments?

The moment those questions extend beyond Azure, the resources needed to get answers start to scatter.

Security teams export PIM activation logs, correlate Azure activity logs, gather AWS CloudTrail, review Kubernetes audit logs, and cross-reference ITSM tickets. Then they manually stitch together timelines to approximate a complete picture.

Even then, these disparate systems paint an incomplete picture. Auditors need to know specific details like:

• Who had privileged access to production? 
• Under which approval? 
• For how long? 
• What actions did they take?
• Was this enforced consistently across environments?

Activation logs show that someone elevated, but they don’t tell the story of how privilege was governed and exercised across systems. 

That gap is where audit preparation becomes slow, manual, and unnecessarily stressful.

PIM’s Scope is too Narrow for Enterprise Architectures

This is not about dismissing PIM. It solves a specific problem (i.e. temporary elevation inside Microsoft’s ecosystem), but enterprise privileged access governance is much broader.

The limits of PIM’s functionality leaves several gaps that compliance teams must find other ways to account for:

• PIM does not eliminate standing privilege across all environments
• It does not provide unified lifecycle visibility across cloud platforms
• It does not standardize approvals outside Microsoft systems
• It does not connect privileged actions back to approvals in a structured, cross-environment way

So while elevation inside Azure may be controlled, the broader enterprise story remains fragmented.

And fragmentation is what makes audits painful.

An auditor is not looking for five different log sources. They want one coherent explanation of who did what, when, and why, along with evidence that the process is consistent everywhere.

If assembling that explanation requires correlating multiple systems that were never designed to operate as one, the burden lands on security and identity teams – which only increases as environments scale.

Today, humans elevate across clouds and platforms. And as automation and AI-driven workflows proliferate, they will operate across those same systems at speed and volume that manual audit reconstruction cannot sustain.

If auditing is difficult now, it becomes unmanageable without rethinking the control model.

The Shift: Centralize the Control Model

Most teams try to solve this by improving reporting. They extend log retention, add dashboards, and invest in aggregation, hoping better visibility will ease audit friction.

That helps at the margins, but it does not fix the core issue. You are still correlating disconnected systems after the fact, stitching together logs that were never designed to tell one coherent story.

What actually changes the experience is centralizing privilege governance. When access is requested, approved, provisioned, and monitored through a single control layer, the audit narrative assembles itself. Evidence stops being something you scramble to collect during audit season and becomes something your control model produces continuously.

In practical terms, this means creating consistent guardrails across environments. Access is requested in a standardized way. Approval workflows are enforced consistently. Privileges are provisioned dynamically on the target system. Access expires automatically. Privileged activity is traceable from request through execution.

If these processes are not monitored and managed continuously then they will quickly become irrelevant as the cloud environments constantly and rapidly change.

When the control model is centralized and continuously monitoring, audit preparation becomes reporting instead of investigation.

How Apono Simplifies Audit Life

Apono was built as a centralized privilege control plane for multi-cloud and hybrid environments.

Identity providers like Entra ID and Okta continue to handle authentication, while Apono governs the privilege layer across cloud platforms, Kubernetes, databases, infrastructure, and SaaS systems.

Instead of relying on standing access and scattered controls, Apono enables organizations to move toward Zero Standing Privileges by converting persistent permissions into time-bound, requestable access flows. It continuously surfaces and reduces unnecessary privilege across environments.

The impact becomes most visible during audits.

Because Apono manages the full access lifecycle – from request, to approval and provisioning, and all the way through to expiration – it becomes the source of truth for privileged access across systems.

With structured session auditing, it extends beyond provisioning events to capture the actions performed during access. Instead of replaying sessions or combing through and manually correlating raw logs, security teams can directly answer who accessed what, under which approval, for how long, and what actions occurred.

By centralizing privilege guardrails and connecting approvals to actions, Apono dramatically reduces the operational load of proving control.

With Apono, not only do audit preparation time drop and investigation time shrink, but conversations with auditors become simpler and more defensible.

A Different World Than PIM Was Built For

PIM was built for a Microsoft-centric world and enterprises do not operate in one.

As long as privileged access is governed in silos, audits will require correlation, manual effort, and reconstruction. The only scalable way to reduce that friction and prepare for the wide-scale deployment of agents across systems is to centralize the control model across environments and eliminate standing access by design.

When the control model changes, the audit experience changes with it.

Make Privileged Access Audits Simpler

In multi-cloud environments, proving who had privileged access and why often requires stitching together logs from multiple systems.

When access governance is fragmented, audit preparation turns into manual investigation.

A centralized privilege control model changes that. Access requests, approvals, and activity become traceable across environments, making audit evidence easier to produce and defend.

Evaluate your privilege model in a personalized session with our team.

Prefer to start on your own? Download the Audit Readiness Checklist to assess your environment.

An intern gets admin access to production for a temporary task, but nobody remembers to revoke it. Imagine that intern works at machine speed, never sleeps, and can chain dozens of actions before you’ve read the Slack ping—and has no instinct for when they’re about to do something irreversible.

That’s an AI agent in production. For many organizations, it’s already inside the environment with access that’s broader than anyone would approve for a human. Agentic AI is the most significant identity security problem that most security teams aren’t treating as one: 100% of organizations agree attacks on agentic AI workflows would be more damaging than traditional cyberattacks. Without focusing on the agentic identity crisis, you’re unlikely to recognize what actually needs to change before compliance frameworks and threat actors force your hand.

Why Agents Break Traditional Identity Assumptions

For the past few years, the identity security conversation has finally started paying attention to non-human identities (NHIs). Service accounts, API keys, OAuth tokens, CI/CD credentials: the identities that keep the stack running. 

The ratio of machine identities to human identities in a typical enterprise is now as much as 150:1, and a large share of those machine identities sit outside identity providers. They’re scattered across cloud providers and SaaS tooling. 

Visibility is fragmented, and lifecycle management is mostly an afterthought. Permissions are overly broad because nobody wants to be the person who broke the deployment pipeline on a Friday afternoon. AI agents inherit all of those problems and then add several of their own.

The gap between NHIs and agents is mostly behavioral. A service account is usually built for a narrow purpose, and its behavior is relatively predictable. An API token typically enables a bounded set of calls. Most of these identities are meant to be bounded, even when the permissions aren’t. You can anticipate what they’ll do because they don’t make decisions—they execute instructions.

Agents are different. They plan and take sequences of actions toward a goal. They can call external APIs, read from databases, write to storage buckets, trigger downstream agents, and update records across SaaS platforms. They might do all of that in one task run. Once agents start chaining calls across internal and third-party systems, API security becomes part of your identity problem, not a separate track.

When they hit ambiguity, they improvise, and that improvisation becomes actions in production. Critically, they chain actions across systems in ways that haven’t been reviewed. Often, the methods they use were not anticipated when you granted the permissions.

Source

McKinsey’s agentic AI security playbook makes the point well. The risk shifts when you move from systems that enable interactions to systems that drive transactions. A compromised agent can execute business logic at scale without a human as part of the process to notice something looks wrong.

Static, long-lived permissions weren’t designed for this. Role-based access control (RBAC) helps organize access, but it still doesn’t solve intent- and task-scoping. Behavioral analytics can help you spot anomalies. But neither fixes the core issue. Both still assume the identity behaves within a recognizable pattern.

Agent behavior is goal-directed, not role-directed, which means required access is a function of what they’re trying to accomplish, not what category they’ve been assigned to. Over-scoped permissions, unintended data exposure, and cross-system chaining are the most consistent patterns appearing in AI cyber risk reports. 

How Agent Deployments Quietly Accumulate Risk

A team wants to build an agent that can handle customer data requests. It needs read access to the CRM, write access to a Jira board, and the ability to query a database. 

The engineer shipping it doesn’t want to spend two weeks debugging permission errors, so they grant broader access than strictly necessary and tell themselves they’ll scope it down after the proof of concept (POC). That POC then becomes a production agent, and the permissions never get revisited.

This is a predictable outcome of an access model that makes least privilege painful. When role definitions are coarse or when there’s no tooling to create just-enough permissions on demand, overprivileging is the path of least resistance. The problem is that with agents, the blast radius grows because execution is faster and actions span more systems.

Add multi-agent architectures into the mix, where agents are orchestrating other agents and passing data between them, and a single compromised or misconfigured agent could move laterally across your stack. This is also where agentic pen testing starts to earn its place, showing you what an agent can do end-to-end when something goes wrong.

80% of organizations have already encountered risky behaviors from AI agents, including unauthorized system access and improper data exposure. Plus, only 21% of organizations say their organization feels prepared to manage attacks involving agentic AI or autonomous workflows. That number is not going to go down as agent deployments scale.

Cross-agent task escalation is an emerging risk class of its own. One compromised agent can abuse trust assumptions to get another agent to do something it shouldn’t. Chained agents mean that a vulnerability at step one of a workflow propagates through every subsequent step. By the time anyone notices something went wrong, the damage will have spread. 

Autonomous Systems Require Automated Governance

The access model for agentic AI needs to be built around a few foundational principles. These shouldn’t be advanced controls: they’re table stakes. 

Make Access Ephemeral by Default

Access must be ephemeral and intent-aware. An agent that needs read access to a database for a specific task should receive exactly that access, scoped to that task, for the duration of that task, and nothing more. Just-in-time and just-enough privileges access are the minimum viable security model for autonomous systems. This is what zero trust looks like in practice for agents: verify explicitly, grant the minimum, and remove access by default.

Log Intent, Not Just Activity

Audit trails have to capture task context and declared intent, not just API calls. If your current logging tells you that an agent made 400 API calls to your data warehouse between 2 and 3 AM, that’s not useful. You need to know what the agent was trying to do, what resources it requested, what it actually touched, and what it changed.

Define and Gate High-Risk Actions

High-risk actions need a human in the loop. An agent querying a read-only endpoint is different from an agent deleting records or triggering a payment. Defining which actions cross a risk threshold and requiring human authorization for those specifically is how you avoid the kind of incident that ends careers and regulatory standing.

The key is to define those actions in advance and require explicit, time-bound approval for them. And that approval needs to happen where teams already work, not in a multi-day ticket queue. Self-serve workflows in Slack, Teams, or CLI make it possible to introduce human oversight without slowing down engineering. 

Manage Agent Access as a Lifecycle

Treat agent access as a lifecycle: onboarding, scoped permissioning, continuous review, and fast revocation when scope changes. The same principles that apply to human identity lifecycle management, provisioning, review, and revocation apply to agents. And they have to run at machine speed, with automation enforcing policy by default, not reminders in a ticket queue.

Treat your AI agents as highly capable, deeply untrustworthy interns. They’re smart enough to be dangerous, and they have absolutely no instinct for when they’re about to do the wrong thing. This is the same operational discipline security teams are already trying to build with machine identity management, except agents force it to happen at machine speed. 

Least Privilege Still Applies to Agents

The regulatory and compliance environment hasn’t caught up to agentic AI, yet. But it’s moving fast, and theframeworks that govern identity and access today are broad enough to apply to agent access right now. Agents also change what ‘third-party access’ means in practice, which is worth reflecting in your vendor risk assessment, especially when agents can pull data from or push actions into external platforms.

For example, SOC 2’s CC6 controls around logical access don’t specify that access must be granted to humans. If an agent has standing access to sensitive resources, that’s a least privilege violation regardless of whether the auditor asks about it explicitly. 

The more interesting question is what happens when frameworks start asking about agents explicitly. The signs are there that auditors are probably already considering these things, even if the frameworks haven’t officially been updated yet. The Forbes Tech Council noted that organizations are accumulating what they’re calling an“identity tax,” which is the compounding cost of unmanaged identities. Agents accelerate that tax because they multiply and act quickly. 

When an auditor asks you to demonstrate least privilege enforcement for your agent workforce, “we gave it a role” is not going to be enough. Evidence needs to show that access was bounded by the task. Log entries need to include what the agent requested, why, and verification that permissions were not left standing. Auditors will also want proof that team members are checking and approving high-risk actions. That’s what compliance-ready agent access governance looks like.

Organizations that wait for explicit regulatory pressure to address this will be doing access remediation under a deadline, which is where mistakes happen. The companies building agentic access governance now are not doing it because of current regulatory requirements. They’re doing it because the alternative is scrambling to retrofit controls onto a sprawling agent estate under deadline pressure, which is exactly when mistakes slip through.

Operationalizing Agent Access at Scale

The identity security conversation has been shaped for decades by the assumption that the identities you’re governing are human, or at least human-like in their behavior. Agents break that assumption. They’re systems that operate other systems, autonomously, at scale, and often across trust boundaries.

The organizations that will navigate this well are the ones that treat agent access as a new (and very dangerous) risk surface requiring continuous, contextual, automated governance. The only sustainable answer is continuous, contextual, automated access governance, with JIT and JEP  as the default. If you’re already deploying agents into production workflows, it’s not a 2027 problem. It’s a 2026 control gap. 

Apono helps security and platform teams automate just-in-time, just-enough access across their entire stack, so agents get exactly what they need, for exactly as long as they need it, with full auditability and human gates where the risk demands it. If you’re deploying agents into production workflows, now is the time to modernize how you govern their access. 

See how Apono’s Agent Privilege Guard gives security teams runtime privilege controls for the agentic era, with ephemeral access and intent-aware enforcement. Or, book a demo to see how Apono enforces task-scoped access for humans and AI agents, without slowing your teams down.

Microsoft Entra ID Privileged Identity Management is designed to bring structure to privileged access inside Microsoft environments. It allows organizations to make roles eligible, require activation, and enforce approval workflows.

Within Azure, it performs that role predictably.

The challenge begins when engineering workflows extend beyond Azure.

Modern infrastructure rarely lives in a single ecosystem. Teams operate across additional clouds like AWS and Google Cloud, Kubernetes, SaaS platforms, databases, and hybrid systems. In that environment, the question shifts from whether PIM can manage activation to whether it can deliver a frictionless access experience across the full stack.

Because if access feels fragmented or slow, engineers adapt. And when engineers adapt, least privilege begins to erode as security best practices get discarded by demand for employee access and productivity.

Where DevX Friction Starts

Engineers usually know what they need to accomplish. Friction appears when they must translate that goal into the correct privileged role.

In Entra PIM environments, that translation depends on predefined Azure roles that are made eligible. Those roles must be created, scoped, and maintained ahead of time.

For engineers, friction often surfaces in familiar ways:

• Not knowing which eligible role maps to the task
• Activating a role only to discover it does not provide needed access to required assets.
• Waiting for role activation during time-sensitive work (5-45 min delays)
• Switching between cloud consoles to complete a single task

These issues are manageable in isolation. Repeated often enough, they influence behavior.

If the right role is unclear, engineers may request broader eligibility to avoid getting blocked. If activation delays interrupt debugging or incident response, certainty begins to outweigh precision.

That drift does not start with policy failure. It starts with workflow friction.

Where PIM’s Model Creates Structural Friction

PIM’s core model is built around eligible roles that exist in advance. Even though access is time-bound after activation, the roles themselves are static constructs that must be continuously maintained. This rarely occurs proactively. 

Access is more often extended after work stoppage.

Static Roles in Dynamic Environments

Azure roles are powerful, but they must anticipate future needs.

In fast-moving cloud environments, infrastructure evolves constantly. New resources are deployed. Services span multiple clouds. Responsibilities shift between teams.

As a result, those static roles break, which creates a moving target for security and compliance teams as they try to keep role definitions aligned with evolving engineering needs. When roles are too narrow, engineers activate one role, discover it is insufficient, and request another. When roles are broadened to reduce friction, they drift away from least privilege.

Over time, patterns emerge:

• Role definitions expand to reduce repeated activation friction
• Engineers request broader eligibility to avoid delays
• Precision gives way to convenience

PIM can enforce time-bound access inside Azure, but it does not dynamically shape access in the context of the work, and where the work is being done whether that work is in Azure alone or, more typically, beyond Azure.

Fragmentation Across Clouds

PIM provides structured control inside Microsoft environments. The challenge is that most enterprises do not operate inside a single cloud.

Authentication may be centralized, but privilege enforcement often becomes fragmented. When access works one way in Azure and another way in AWS or Kubernetes, the experience becomes inconsistent.

Engineers quickly learn which systems are slower or more restrictive and adjust their requests accordingly. Over time, least privilege becomes situational instead of systemic.

This is not a weakness in PIM itself. It is a limitation of using a cloud-native role system as a cross-cloud privilege model.

Workflow Expectations Have Changed

Engineering workflows have shifted. Slack, CLI, internal developer portals, and AI-driven tools are increasingly vital tools where developers spend a lot of their time.

PIM, however, is primarily console-driven. While it integrates well with Microsoft services, it does not provide native Slack-based request flows, MCP integrations, or AI-assisted guidance to help engineers determine what to request.

When engineers are unsure which role maps to their intent, the system does not guide them. They must interpret Azure role structures themselves.

When the request experience lives outside the workflow, context switching increases. During production incidents, even small delays influence behavior, and over time, engineers optimize for reliability and speed rather than tightly scoped privilege.

How Apono Aligns Access With Modern Engineering Work

Apono approaches privileged access as a unified heterogeneous control model across environments rather than a feature embedded inside a single cloud provider.

Engineers can request access through Slack, Teams, CLI, Backstage, a dedicated portal, and AI-driven interfaces including MCP integrations. The request happens inside the workflow instead of outside it, and the method does not change depending on which cloud they are operating in.

When engineers are unsure what to request, the system helps translate intent into the appropriate resource and scope. That reduces guesswork and discourages over-requesting.

Access is provisioned dynamically at runtime under defined guardrails rather than relying solely on static, pre-created roles. Privileges are available automatically in seconds, align with the task, and expire automatically when the work is done.

If work extends longer than expected, duration can be extended within policy without restarting the entire process. That reduces the incentive to request broader access up front.

Instead of expanding static roles to keep pace with change, Apono provisions access dynamically and applies consistent guardrails across Azure, AWS, Kubernetes, and SaaS environments. The request experience remains uniform regardless of where engineers are working, allowing least privilege to function as an operational reality rather than an aspirational policy.

Make the Secure Path the Fastest Path

At their core, engineers are problem solvers. If getting the right access they need to achieve their goals becomes a problem, then they will find ways around it.

It is up to security teams to put in place the tools and processes that will enable engineers to move faster without compromising on their security priorities. When requests are intuitive, guided, and time-bound by design, least privilege becomes practical instead of aspirational.

View the Apono vs. Microsoft Entra ID PIM comparison brief, or book a personalized walkthrough to see how Apono can help you build a cross-cloud access model engineers will actually want to use.

Anthropic’s new Claude Code Auto Mode Auto Mode is generating well-deserved attention. It introduces a classifier that sits between the developer and every tool call, reviewing each action for potentially destructive behavior before it executes.

It’s a real improvement over the only previous alternative to manual approval: the –dangerously-skip-permissions flag.

But the announcement is also useful for a broader reason. It puts a clear spotlight on the question that every organization deploying AI agents needs to answer: how do you give agents enough access to be genuinely productive without creating the kind of privilege risk that compounds over time?

Co-pilots Are Already Over-Privileged

Most conversations about agent security focus on a future where fully autonomous agents are making high-stakes decisions. That future is coming, but the privilege gap is already open today.

GitHub Copilot, Cursor, Claude Code, and other co-pilots run with the same credentials as the developer using them. If that developer has broad access to production infrastructure, databases, or cloud resources, so does the agent.

There’s typically no scoping to the task at hand, no independent audit trail, and no automatic revocation when the work is finished.

Our 2026 State of Agentic AI Risk Report found that 98% of 250 senior cybersecurity leaders said security concerns have already slowed deployments, added scrutiny, or reduced the scope of agentic AI initiatives.

That’s not fear of AI itself. That’s a rational response to the fact that existing access models weren’t designed for tools that operate autonomously, at machine speed, with inherited human credentials.

The Kiro incident at AWS last December illustrated this vividly. An AI coding agent inherited an engineer’s elevated permissions, decided autonomously to delete and rebuild a production environment, and caused a 13-hour outage.

Amazon framed it as a misconfigured access control issue, which is precisely the point. Privilege management is the issue, regardless of whether the actor is human or machine.

Why Runtime Evaluation Matters

What makes auto mode architecturally interesting is that it moves permission decisions from configuration time to runtime.

Instead of trying to predict which permissions an agent will need before a task begins, the classifier evaluates each action in the moment with whatever context is available. This is the right direction.

Static permission models force organizations into an impossible tradeoff: grant broad access and accept the risk, or lock things down and accept the lost productivity. Runtime evaluation is what breaks that tradeoff, and Anthropic deserves credit for embedding it directly into Claude Code.

From Actions to Intent

Where the approach has room to grow is in what it evaluates.

Auto mode looks at whether an action is potentially destructive. That catches the obvious cases like mass file deletion or data exfiltration. But risk isn’t always visible at the action level.

Consider an agent that creates a new IAM user, opens a network path, or modifies a security group. None of those look destructive in isolation. All of them can be devastating in the wrong context.

Understanding whether an action is risky requires understanding why the agent is taking it, what environment it’s operating in, and whether the action aligns with the task at hand.

This is the case for evaluating intent, not just actions. When privilege decisions are informed by the agent’s stated purpose, the sensitivity of the target resource, and real-time behavioral context, you can make much more graduated decisions:

• Routine work flows without friction.
• Sensitive operations get human oversight.
• Genuinely dangerous actions get blocked.

That graduated approach is what unlocks real productivity from agents, because it lets them handle more of the work safely instead of being walled off from anything that carries risk.

How We Approach This at Apono

We built Agent Privilege Guard around the principle that intent should drive every privilege decision an agent makes.

When an agent requests access, our Intent Analyzer evaluates the request against multiple layers of context: the agent’s stated purpose, the sensitivity of the target environment, user and asset context, and behavioral patterns. Based on that assessment, the system makes a graduated decision:

• Low-risk actions proceed automatically, keeping agents productive on routine work without developer interruption.
• Sensitive operations escalate to a human via Slack or Teams, so a real person makes the call on high-stakes actions before they execute.
• Credentials are ephemeral, created at the moment of the request, scoped to exactly what the task requires, and destroyed on completion.

Every action is logged end to end with stated intent, the approval decision, credential lifetime, and access granted or denied. When auditors ask questions, the evidence is already there.

This works across Claude Code, GitHub Copilot, Cursor, and other agent platforms from day one, with no rework of existing policies. That cross-platform coverage matters because the privilege challenge doesn’t stop at any single vendor’s boundary, and security teams need a consistent posture regardless of which tools their engineers are using.

Where the Industry Is Headed

Anthropic’s auto mode reflects a shift that the entire industry needs to make: from static, pre-configured permissions to dynamic, context-aware access decisions made at runtime.

We believe intent-based guardrails are the natural next step in that evolution. They let organizations deploy agents with more freedom, not less, while maintaining the control and auditability that security and compliance teams require.

The companies that move fastest on AI adoption will be the ones whose privilege models are smart enough to keep up.

Start Applying Intent-Based Access to Your AI Agents

See how Apono secures AI agent privileges across Copilot, Claude, and more, without slowing engineers down. Explore Apono’s Agent Privilege Guard today.

In the current AI boom, we race to use copilots, orchestration scripts, CI workflows, retrieval pipelines, and background jobs. Sometimes, we take for granted that every one of these things needs an identity. Service accounts. OAuth apps. API keys. Short-lived tokens. 

As AI velocity increases, so does the number of these non-human identities (NHIs). Instead of obsessing over model quality, latency, hallucinations, and GPU costs, we also need to consider how these identities impact security. Every agent you spin up carries credentials, and every credential carries permissions. 

Multiply that across environments, pipelines, and integrations, and AI poses a big identity sprawl problem. Then, you get the hidden cost of AI velocity: identity sprawl that security teams can’t realistically govern with tickets and quarterly reviews. 

What NHI Sprawl Actually Looks Like in 2026

To understand NHI sprawl today, it’s worth pausing for a moment to think about what this sprawl looked like just before the AI era. 

Five years ago, non-human identities were already quite abundant. But the patterns were predictable:

• Build pipelines had dedicated CI/CD service accounts.
• Kubernetes controllers spun up service accounts namespace by namespace.
• Cloud automation tooling used long-lived IAM roles mapped to deployment pipelines.
• SaaS APIs were integrated via app-specific API keys stored in secret managers.
• Backup jobs and ETL processes had static credentials managed by platform teams.

That was sprawl, but it was manageable sprawl. There were usually clear owners (platform, DevOps), predictable lifetimes, and manual reviews tied to release cycles or audits.

AI changed the rate and diversity of identity creation. The pattern many companies now face is:

• Every AI agent/platform needs API access to internal services, CRM, observability, messaging, etc.
• Every automation pipeline spun up to support those agents needs tokens, often embedded in CI/CD workflows.
• Every orchestration layer generates its own principals to schedule, coordinate, and execute jobs.
• Every multi-stage job expands credential usage across environments.
• Every “agent chain” creates implicit privilege inheritance across systems.

What used to be a handful of long-lived service accounts becomes hundreds or thousands of specialized machine identities scattered across your stack. And the scale is no longer hypothetical: machine identities outnumber human identities by 80:1 in enterprise environments. 

When AI Velocity Meets Identity Governance 

Before the AI wave, identity growth in most environments was relatively linear. New services were introduced deliberately. IAM roles were defined around stable workloads. Service accounts were usually tied to long-lived systems. Even if sprawl existed, it evolved at a pace humans could periodically review.

Governance could at least attempt to map identities to systems and owners.But AI velocity collides with typical governance efforts built around inventories, tickets, and periodic certifications.

That’s why this collision creates real cybersecurity risk:

• Privilege drift accelerates. Permissions expand faster than they’re reviewed.
• Blast radius increases. Machine identities often span systems humans never touch directly.
• Detection becomes harder. Machine-to-machine traffic looks normal by default.
• Abuse scales instantly. API-level access means no console login is required.

Why Security Can’t Keep Up

Security can’t keep up because identity governance is still designed around reviewable objects, while AI turns identity into a byproduct of automation. Identities aren’t “created and reviewed.” They’re generated continuously by pipelines and runtime systems.

And yet the control plane hasn’t evolved. Security teams often still:

• Review role assignments after deployment
• Approve access via tickets
• Run periodic certifications
• Maintain static RBAC models that assume stability

The other structural problem is visibility. Modern AI stacks rely heavily on short-lived tokens issued via OIDC or STS. Credentials churn rapidly, but privilege is still defined at the role, service account, or OAuth-scope layer, and that layer persists. 

Security tooling frequently shows what roles exist, but not whether they’re being exercised appropriately, by which workload, for which task, right now.

Compliance doesn’t fix this. You can document least privilege and prove access reviews happened. But if identity creation is embedded in CI/CD and runtime federation, then governance evidence will always lag behind the actual access picture. Many organizations formalize identity oversight within a broader cybersecurity risk management plan, but documentation alone doesn’t address runtime privilege drift in AI-heavy environments.

Zero Trust Paired With Non-Human Identity Controls

In practice, most organizations interpret zero trust as stronger authentication for humans, tighter network segmentation, and conditional access policies. That model assumes people are the primary actors inside systems. Those are important, but they still tend to be human-first. 

In AI-heavy environments, the point is that humans aren’t the dominant actors anymore. If identity is the new perimeter, then non-human identities are now the dominant traffic crossing it. Zero trust without explicit NHI governance leaves your highest-volume lane under-controlled. 

Just as product development lifecycles require structured governance from ideation through release, identity lifecycles demand the same rigor, from creation to privilege assignment to revocation. 

1. From Identity Management to Real-Time Identity Orchestration

Traditional identity management focuses on provisioning and deprovisioning. A user is assigned a role; a service account is granted permissions. The system trusts that assignment until someone revisits it.

That model assumes identities are relatively static. AI systems don’t operate in static environments. They federate across clouds and SaaS platforms. They redeploy frequently, and they rely on ephemeral workloads that request credentials dynamically.

So the control model must shift from managing identities as objects to governing access as runtime decisions. Real-time identity orchestration means:

• Evaluating machine access continuously, not just at creation.
• Governing both human and non-human identities under the same enforcement framework
• Treating identity decisions as runtime events rather than admin tasks.
• Instead of asking, “What does this role have?” the system asks, “Should this identity perform this action right now, in this environment, for this task?”

2. Credential Half-Life Is Collapsing

On paper, this sounds like it should lead to improved lower risk. After all, short-lived credentials reduce exposure windows. But while credentials expire quickly, privilege scopes often remain broad and persistent.

The role still exists, the service account still holds cross-system permissions, and the OAuth integration still has sweeping API scopes.

This creates a new threat profile where:

• Short-lived tokens can still be replayed within their validity window. 
• Over-permissioned roles are exercised continuously.
• Bot-driven API abuse happens at machine speed.
• Attackers don’t need long-lived keys if they can repeatedly obtain short-lived tokens via a compromised workload, OIDC misconfig, or stolen CI identity.

Credential half-life is shrinking, but authorization half-life often isn’t; that asymmetry demands runtime controls. 

3. Dynamic, API-Level Enforcement

Traditional security controls often sit at the network layer: proxies, gateways, firewalls. But most AI-driven interactions happen via APIs.

Dynamic, API-level enforcement means:

• Evaluating identity context at the moment of the call.
• Inspecting action types.
• Applying policy based on workload type, environment, and requested operation.

Rather than just verifying the token is valid, you verify the action is justified.

4. Auto-Expiring Access as the Default State

Standing privileges are handy, but they’re also dangerous. In AI-heavy systems, persistent permissions become invisible infrastructure. Service accounts retain write access long after the original need has faded. OAuth tokens sit embedded in integrations that no one revisits.

Auto-expiring access should be the default for NHIs. If an AI agent needs temporary cross-system access to perform a task, that access should be time-bound and self-revoking.

5. Contextual, Task-Scoped Permissions

An AI enrichment agent may need to read from one dataset, update specific records, and call a defined API endpoint. It rarely needs unrestricted write access across environments.

Contextual, task-scoped permissions limit identities to:

• Specific datasets.
• Specific API methods.
• Specific environments.
• Specific time windows.

Instead of granting “database write,” you grant “write access to this table during this job execution.” Granularity is the difference between “least privilege” on paper and least privilege in practice.

6. Agent-Specific Guardrails

Not all machine identities are equal.

A reporting agent shouldn’t share the same privilege boundary as a deployment pipeline. An inference endpoint shouldn’t hold infrastructure modification rights. You get the point. 

Agent-specific guardrails formalize those boundaries. They codify what that class of NHI is allowed to do, and what it is explicitly forbidden from doing. Also, these guardrails enable continuous adversarial exposure validation by ensuring that AI-driven workflows cannot exceed their intended blast radius. 

In AI ecosystems where agents chain together, this becomes critical. Without guardrails, privilege inheritance spreads invisibly across workflows. Guardrails enforce architectural intent.

7. Human Authorization for Disruptive Actions

Even in the AI-dominated world we find ourselves moving towards, certain operations should never be fully automated without oversight. Think:

• Deleting datasets.
• Modifying production infrastructure.
• Granting additional privileges.
• Rotating encryption keys.
• Accessing high-sensitivity data stores.

Human-in-the-loop controls for disruptive actions create deliberate checkpoints where machine speed must slow down.

Gaining Back NHI Control at Machine Speed

Static roles, quarterly reviews, and spreadsheet-based IAM were never designed for machine-dominated environments. When AI agents can create, modify, and move data in seconds, access governance has to operate at the same cadence.

As long as service accounts, CI tokens, and AI agents retain persistent write access by default, governance will always lag reality. You can’t manage NHIs at machine speed if access decisions were made months ago and never revisited. If you want to understand where your exposure really sits, start with standing access.
Autonomous agents are moving into production environments faster than security teams can assess the risk. Learn more about how to manage your agents’ access using our Agent Privilege Guard, and book a demo here.