A year ago, less than 5% of enterprise applications used agentic AI agents. This year, Gartner predicts that the number will rise to 40%.
AI has triggered an explosion of Non-Human Identities (NHIs) that move at machine speed. These agents are autonomous, ephemeral, and over-privileged. They have a lot of power, a lot of freedom, and currently, not a lot of oversight.
Teams need to get a handle on agentic AI agents. Using them in modern solutions is the only way to stay competitive. But to compete responsibly, teams need to know a few things: where these agents reside, what they can do, and how to limit their potential reach if they were to misbehave.
Exposure management (EM) secures this “silent workforce,” making it an essential strategic move for companies that want all the firepower of agentic AI—and none of the backfire.
The NHI & machine identity explosion
We have witnessed an explosion of NHIs and machine identities since AI was made public. Service account sprawl has pushed the demands of AI cybersecurity far beyond the reach of advanced tools using automation alone.
As Gartner stated, the number of agentic AI agents in enterprise tools is set to increase by 700% this year alone. In that light, service account sprawl becomes a real problem. These non-human accounts are spun up, used, forgotten, and never decommissioned.
They lie unnoticed until attackers search them out and find them, using them for high-impact attacks. Unauthorized access, lateral movement, data loss and more follow.
Service accounts are unmatched in their usefulness, doing things at a speed and scale humans can’t even approximate.
- They find fast and accurate customer service answers that require intensive database scans.
- They aggregate all available threat telemetry to find where attackers may be hiding, and where they’ll go next.
- They use last quarter’s data to build out executive-level forecasts for M&A investments in the coming two years.
And so much more.
But that power and freedom comes with a price. Risks inherent in non-human entities include:
- Over-privileging: Many NHIs are authorized to act as admins.
- Shadow NHIs: Organizations struggle to track, monitor, or delete machine identities when done, creating the perfect “backdoors” for attackers.
- Hard-coded credentials: Attackers know that passwords for these accounts are often hardcoded into configuration files or scripts, a key security vulnerability.
- Manageability: Keeping track of every human identity can be a challenge. Adding one new tool and potentially thousands more within the course of a single deployment can be more than most security teams can handle.
The only things that can keep pace with AI agents are more AI agents. These need to be sewn into cybersecurity solutions before organizations can even begin to compete.
Addressing entitlement gaps through identity attack paths
In agentic AI discussions we often refer to the “entitlement gap.” This is the disparity between what agentic AI agents are allowed to do, and how successfully we can actually govern, monitor, and secure those actions.
It’s very much like Frankenstein’s monster, created for a useful purpose but too overpowered for its maker to control. It doesn’t have to be this way, but currently, agentic agents are often engineered in a manner that puts them naturally ahead.
Their excessive permissions put them on equal footing with high-level executives, meaning that their identity blast radius – or what they have permission to influence – is extremely wide. The more power, the more responsibility.
But AI agents don’t have “responsibility” from within. Which is why solutions like exposure management need to enforce it from without. The best way to do this is to predict their next moves and run ahead to shut them down.
This is known as attack path hygiene; EM tools use AI and agentic capabilities of their own to “think” like those autonomous AI agents, running down the myriad ways they can be compromised, and presenting the top, most likely attack paths to defenders so they know where to strike.
AI‑driven risk acceleration ups the urgency
Agentic AI cybersecurity is the only thing that can make an impact (or at least start a trajectory) against autonomous agents. Especially when you consider the fact that attackers are using AI, too.
Threat actors are using tools like Claude and Gemini to create bots that can analyze organizations for vulnerabilities at an unprecedented scale. Agentic AI agents with exposed credentials are vulnerabilities. Agents with far too many privileges are vulnerabilities. Deeply embedded machines that everyone has forgotten about are vulnerabilities.
AI-powered identity attacks can affect these autonomous agents, swiping their credentials from scripts and operating with their over-powered permissions to execute machine-speed breaches.
In this climate, security for AI can only be achieved by using AI for security.
Exposure management: purpose-built to wrangle agentic AI
Using exposure management for AI security is essential to keeping pace with what only agentic AI can do.
EM platforms are taking over vulnerability management tools as the industry-standard way to monitor for all of the risks, in all of the places. Not just some of the risks, in some of the places. That means not just vulnerabilities, but:
- Misconfigurations
- Shadow AI (and shadow agentic AI)
- Shadow IT, shadow IoT, shadow APIs, shadow data
- Identity gaps
- Over-privileging
- Exposed database ports
- Forgotten subdomains
And most importantly, attack paths. Exposure management provides not only a visual on which identities are weak where (identity exposure mapping), but helps teams manage those identities across growing, global architectures (cloud identity governance).
It does more than find and patch; it hunts, identities, monitors, and remediates across the entire attack surface.
And it does it in a way that prioritizes which attack paths are worth pursuing, based on which ones lead to the “crown jewels:” the assets that, if hit, would impact business the hardest.
Protecting your over-powered identities
Exposure management finds every AI service account, API key, and autonomous agent across clouds, identity providers, IT, IoT, and OT. It bridges the entitlement gap to limit a machine identity’s reach.
And it draws a line from vulnerabilities to the real blast radius, showing teams clearly prioritized attack paths, so they know where to mobilize first.
Agentic AI agents are given enormous capabilities, and for good reason. But it takes tools with the same enormous capacities to keep ahead of them, and that’s where enterprises should be looking to invest as machine identities grow.
