Identity Governance for AI Agents and Machine Identities

What are AI Agents?

Alright, let’s break this down. AI agents—what the heck are they? Honestly, if you’ve got chatbots on your website, you’re already familiar with them. But we’re not just talking about bots answering questions. I mean, AI agents are out here running entire workflows, pulling data, making decisions, interacting with each other. Autonomously.

You probably have AI agents doing their thing right now—whether it’s handling customer service or automating tasks that would’ve taken someone hours to do. Sounds pretty cool, right? Well, yeah. But here’s the catch—they’re autonomous. And that’s where the mess begins.

See, AI agents don’t exactly follow the same rules we set for humans. They don’t ask for access—they just go in and do whatever it is they were trained to do. Which, hey, that’s great—until they go rogue. Or worse, you don’t even know they’ve got access to sensitive data, because they don’t need to check with you before they grab it. That’s a big problem.

Machine Identities in Enterprise Systems

Now, what about machine identities? This is a biggie, and honestly, most people don’t realize how many machine identities they have. APIs, IoT devices, virtual machines, bots… the list goes on. And guess what? They need identity governance too.

Think about this: IoT devices are pretty much everywhere now, right? Cameras, sensors, everything. And what happens if one of those gets compromised? Now it’s not just your IT system that’s vulnerable—it’s the whole infrastructure. Machine identities are as important as human ones when it comes to managing access. The problem is, nobody thinks about it until something goes wrong.

Key Differences Between Human, Machine, and AI Agent Identities

Alright, so how is all this different from what we’re used to with human identities? Here’s where it gets a little tricky:

• Permanence vs. Ephemerality: Humans have a permanent identity at work. They’ve got an email address, an access card, all that jazz. But machine identities and AI agents? They don’t stick around. One moment they’re there, the next, they’re gone. They exist based on tasks, not roles. So, their access needs to be just as dynamic.
• Manual oversight vs. autonomy: We have IT teams tracking human access—permissions granted, revoked, changed. But AI agents? They just act. You don’t have someone constantly watching over them. You need systems that can track what’s happening while it’s happening, instead of getting a report months later.
• Context-dependent vs. static permissions: Human roles are easy. You’re in HR, you get access to HR stuff. But AI agents? They need dynamic permissions based on what they’re doing at the time. Today they need access to this, tomorrow it’s something different. That’s where governance needs to be smarter than just traditional RBAC.

The Problem with Static Models

Look, traditional identity governance systems are awesome for managing human identities. They’ve been around for a while and, let’s be honest, they do a pretty decent job of keeping things in check. But when it comes to AI agents and machine identities? Not so much.

Why? Because these systems were built for static roles. Humans have roles—they’re defined, they’re predictable, and you can assign them permissions based on a list of predefined rules. Easy. But AI agents? Not so much. They’re dynamic, ever-changing, and way too fast for traditional role-based models (RBAC or ABAC) to keep up with.

I mean, how do you even define a role for something that doesn’t have a “role”? You can’t just tell an AI agent, “You’re an HR bot now” or “You’re a finance assistant.” It doesn’t work like that. AI agents work across multiple systems, depending on what they need to do—and their permissions need to shift based on context, not just fixed roles.

No “Intent” or “Contextual Awareness”

Here’s where it gets even stickier. Traditional systems don’t care about intent or context. They simply grant access based on what you’re told. But AI agents? They need to be understood within their context. Like, if an AI agent is supposed to pull financial data, it should have access to it—but only if it’s actually processing that data. Not a second longer.

But how can you trust a system that’s used to granting static permissions when everything is moving so fast and is so dynamic? That’s the issue with traditional IGA tools—they weren’t designed to understand context, intent, or, frankly, the autonomy of AI-driven systems.

Imagine this: You’ve got an AI agent chaining multiple API calls across your systems to perform a single task, but it’s accessing stuff it doesn’t need in the process. In traditional identity governance, you’d have no idea it was even happening. You wouldn’t know the agent was accessing that information until months later—if you caught it at all.

Privilege Escalation and Shadow Access

Here’s the deal: AI agents can escalate privileges fast. They start with just enough access, then before you know it, they’ve picked up more rights. No one noticed, and now they’re accessing more than they should. Shadow access is sneaky like that—it just builds up.

Data Leakage Through Agent-to-Agent Communication

AI agents don’t work in isolation—they talk to each other. And when they do, sensitive data can leak across systems without anyone realizing it. If one agent gets compromised, all that data moving between them is vulnerable.

Accountability and Audit Gaps

AI agents act on their own. They don’t log actions like humans do. So, when something goes wrong, it’s tough to pinpoint who—or what—caused it. Audit gaps become a serious problem when we don’t have full visibility into these agents’ actions.

Compliance Risks — GDPR, SOX, ISO 27001 Challenges

Compliance is already tough, but when AI agents are involved? It gets even trickier. GDPR, SOX, and others expect clear tracking of data access and decision-making. But AI agents move fast and without the same oversight, leaving compliance up in the air.

Adaptive Identity Governance

Alright, here’s the deal: AI agents and machine identities? Yeah, they don’t exactly play by the rules. They don’t fit into the neat little boxes that we’ve built for human access. So, what do we do? We adapt.

The old-school identity governance models aren’t gonna work. I mean, come on—AI doesn’t ask for permission, right? It just goes in, does the job, and moves on. That’s why we need adaptive identity governance—it adjusts based on what’s actually happening. It’s not static, like human roles, it’s more like—well, like a fluid system that changes with the flow. A bit like your favorite streaming platform remembering what you watched last night and tailoring the recommendations for today. Simple, but powerful.

IGA + CIEM + PAM Convergence

Now, I get it—acronyms everywhere. IGA, CIEM, PAM—sounds like we’re speaking in code, right? But seriously, they work. They have to work together. Why? Because without them working hand-in-hand, AI agents are still floating around ungoverned.

Here’s the thing: Think of it like a car—you’re not driving with just the steering wheel, right? You need the whole dashboard. That’s what CIEM + IGA + PAM do. They give you a complete picture. Together, they’ll make sure that when AI agents need to do their thing, they’re also doing it within the boundaries you’ve set. And if they try to go off track? You’ll know about it, like, instantly. That’s the power of these tools working together. So yeah, it sounds like a lot, but when it clicks? Total game-changer.

1. Dynamic Discovery of AI Agents

Here’s the thing—AI agents are sneaky. They show up, disappear, and sometimes you won’t even notice them until they’ve already done something. So, what do we do? We discover them dynamically, that’s how.

Seriously, if you can’t see it, you can’t govern it. Imagine walking into a room full of people—and someone sneaks in, does their thing, and leaves without anyone noticing. That’s what it’s like when AI agents are running amok. You need something that’s gonna spot them instantly—before they start getting all cozy in your systems.

If you’re not tracking them, it’s like playing hide and seek—but you’ve got your eyes closed the whole time. Automated discovery tools are your eyes in this case. They’ll spot the AI agents as soon as they appear, and you’ll be able to manage them.

2. Context-Aware Access Certification

So now we’re in the thick of it. AI agents need context, not just a list of permissions. What do I mean? Well, access can’t just be granted based on “Hey, they’re an AI agent.” It’s more than that. AI agents are like freelancers—they don’t work on the same tasks all the time. One day they’re pulling data from marketing, the next, they’re doing a system update. So, what do we do? We give them access based on what they need right now, not just what’s written in their job description.

Access reviews should be like checking in on the freelancer’s task list every few hours—not just once a quarter. You need to check if they’re doing what they’re supposed to, and if not, change their access, stat.

3. Just-in-Time Provisioning and Deprovisioning

Okay, this one’s a no-brainer: Just-in-Time (JIT) provisioning. You don’t just leave the keys to your office lying around, right? Well, AI agents shouldn’t get permanent access either. You give them temporary access, right when they need it—and then take it back when they’re done.

JIT provisioning means you don’t have to worry about AI agents hanging around with unnecessary permissions. You give them what they need, when they need it. Simple. Effective. Secure.

4. Behavioral Monitoring and Anomaly Detection

Here’s a fun one: behavioral monitoring. You know how when someone suddenly starts acting weird at work, you notice? AI agents should be the same. If they start accessing things they shouldn’t, or doing tasks out of line, you should know instantly.

AI agents are smart—too smart. They can learn on their own, but that means they can also drift off course. Anomaly detection will flag anything that’s out of place before it turns into a disaster. It’s like watching for red flags in your favorite show—if something doesn’t feel right, you want to catch it before it becomes a big deal.

5. Credential Lifecycle Automation

Finally, let’s talk credential lifecycle automation. You don’t let someone walk around with a key after they’ve finished the job, right? Same goes for AI agents. They need short-lived credentials, and you want those credentials to disappear once the task is over.

If you don’t have this, you’ve got credentials sitting around, collecting dust. Ephemeral tokens, short-lived keys—these are your friends when it comes to AI agent governance. No leftovers, no unused access.

Assigning Identity and Ownership at Creation

Alright, first things first—when we talk about AI agents and machine identities, governance starts the moment they’re created. This isn’t just about giving them a name or throwing them into the system. You need to assign them ownership from the start. It’s like creating a new employee profile: you define what they’re supposed to do, what access they need, and who’s responsible for them.

It’s all about metadata—you’ve got to document the agent’s purpose, scope, and owner. Without this, you’ve got a rogue AI agent running around doing God knows what, and no one to hold accountable if things go wrong.

Intent-Based Access Controls

Now, let’s talk about access controls. Traditional models like RBAC (Role-Based Access Control) don’t cut it here. AI agents don’t have a specific “role” in the way we think of human roles. Instead, you need to move towards intent-based access controls.

What’s that mean? Well, you don’t want to assign access just because an agent’s “job” says so. You want to make sure they only have access when they need it, based on the task they’re trying to accomplish. Think of it like a temporary badge that gets issued only when an agent needs to access a certain system or data. As soon as the task is over? Boom. Badge revoked.

Real-Time Risk Scoring

Now, what if your AI agent starts acting a little too “creative” with its permissions? It could be harmless—or it could be a red flag. That’s why you need real-time risk scoring. This means continuously tracking what the agent is doing and scoring its actions based on risk factors. Is it acting outside its normal scope? Is it accessing data it doesn’t need? If so, trigger an alert and maybe revoke access.

It’s kind of like giving a credit score to AI agent activity. If it starts making risky moves, you’ll be the first to know.

Policy Enforcement Points (PEP) for AI Agents

Okay, here’s a cool one: Policy Enforcement Points (PEP). This is your way of saying, “Hey, AI agent, you must follow these rules.” The PEP is where you enforce the rules in real time, as the agent tries to access something. It’s like a bouncer at the club—only allowing people (or agents) in if they meet the criteria.

These PEPs work with Policy Decision Points (PDP) to make sure your AI agents aren’t doing anything outside the rules. One PDP makes the decision (Is this agent allowed access?), and the PEP enforces it (Nope, denied). Simple but effective.

Integrating AI Agents into IGA and CIEM Workflows

Alright, so, here’s the deal. You need to integrate AI agents into your IGA and CIEM workflows. Simple, right? But… it’s not that simple. You can’t just slap AI into your existing systems and expect it to work. That’s not how this goes.

You need a unified dashboard. Yeah, I said it—dashboards. But seriously, you want to be able to see human and AI identities in one place. Otherwise, how do you know what’s happening? You’ve got AI bots pulling data, making decisions, who knows what else—they need to be tracked, right?

And look, this whole manual review thing? Forget about it. Automate your access reviews, please. If you’re doing it by hand, you’re already behind. AI agents move fast. Keeping up manually? Good luck with that.

Here’s an example—imagine you’ve got a cloud AI bot that’s accessing sensitive data. Does it have the right permissions? Who’s tracking that? With CIEM + IGA, you’ll know. Done.

Establish Ownership and Accountability for Each Agent

Okay, let’s start with the basics. If you’re running AI agents in your systems, you have to assign ownership. I’m not talking about just giving them access and forgetting about it. You need to know who’s in charge of each AI.

Seriously—if an AI agent causes a mess (and they will, at some point), someone has to be accountable. Without ownership, it’s like saying, “It’s not my problem” and hoping it all works out. Spoiler: it won’t.

Enforce Least-Privilege and JIT Access

Look, least privilege isn’t just for humans. AI agents need limited access too. Giving them full access to everything because they’re “AI” is a terrible idea. Trust me, it’ll bite you in the end. You should only give AI agents access to what they need, and only when they need it. That’s where Just-in-Time (JIT) access comes in.

With JIT, you’re not leaving doors open. AI agents get access to a system when they need to do their job, and then you cut them off once they’re done. It’s like renting a car for a day—you get the keys when you need them, and they’re handed back once you’re done driving. Easy.

Continuously Audit and Certify Access

You’re probably tired of hearing about auditing, right? But here’s the thing—AI agents don’t take breaks. They’re always working, always moving. And without continuous auditing, you’ll miss things.

You can’t afford to wait until your quarterly access review to catch rogue AI agents. It’s gotta be real-time. Automated reviews, certifications—you need them all. And keep in mind, this should be automatic. No manual audits anymore, please.

Integrate AI Observability Tools

Look, if you can’t see what your AI agents are doing, how can you trust them? AI observability tools are essential. You need to track how agents are behaving, what data they’re accessing, and whether they’re staying within the boundaries. Without this, it’s like letting a teenager borrow your car with no GPS or tracking. Not a good idea.

Automate with IGA Workflows to Reduce Reviewer Fatigue

Finally, here’s something you should know: automating your IGA workflows isn’t just about efficiency—it’s about sanity. You don’t want your team bogged down in manual work. Automated access reviews, certifications, provisioning—they all lighten the load. It also means fewer mistakes. Trust me, your team will thank you when they don’t have to manually check access for every agent every time.

Alright, so here’s the deal: AI agents and machine identities aren’t going anywhere. In fact, they’re only going to become a bigger part of how we do business. But here’s the thing—governing these agents is totally different from governing human identities. They’re faster, they’re smarter, and they don’t play by the same rules.

We’ve got to catch up. Traditional identity governance systems? They weren’t made for AI agents and machine identities. You can’t just slap RBAC on them and call it a day. Adaptive governance, real-time monitoring, and CIEM are the only ways to stay on top of things.

I’m not kidding here—it’s time to rethink the whole governance model. You don’t want to be the company scrambling when things go wrong. AI agents need their own system, and if we don’t adapt, we’ll fall behind.

So, yeah, get your AI governance figured out. Start today. Trust me, it’s worth it. You’ll be ahead of the curve, and your security will thank you later.

How do AI agents differ from machine identities?

Alright, so AI agents are like your new digital coworkers—they’re doing tasks autonomously, making decisions, and interacting with other agents or systems. Machine identities, on the other hand, are more like background workers—things like APIs, IoT devices, or virtual machines that need access but don’t have autonomy. In short, AI agents think for themselves, and machine identities just follow instructions.

Can traditional IGA tools govern AI agents?

Well, technically, no. Traditional IGA tools are built around human identities, so they don’t have the flexibility needed to handle AI agents. These agents are dynamic, and traditional tools can’t track their behavior in real time or adjust access based on the agent’s changing tasks. You need a more adaptive identity governance system to keep up with AI-driven systems.

What role does CIEM play in securing AI-driven systems?

CIEM is a game-changer when it comes to managing AI agents. It helps you track and manage access across your cloud infrastructure—so you can be sure that AI agents have the right permissions and are not going rogue. It’s basically the watchdog for cloud-based AI identities, making sure they stay on track.

What’s the best way to automate access certification for AI agents?

Easy. You need automated workflows that run continuous access reviews for AI agents. No more manual reviews—automate everything so it’s constantly checking if an agent still needs access. This will help avoid mistakes, save time, and ensure compliance without the headache.