It’s easy to assume SaaS breaches come from complex, targeted attacks. But in reality, some of the most damaging incidents start with something simple like a forgotten test user granted admin access to an AI app that quietly slips through the cracks. As AI adoption accelerates and SaaS sprawl continues, these hidden risks can slip through the cracks of even mature security programs.

So how do you connect the dots before a small oversight turns into a major security incident? It starts with visibility, not just into what apps are in use, but also who has access, and what they’re actually doing across your SaaS environment. SaaS identity threat detection is essential.

The scenario: A test user, an AI app, and an unexpected login

Let’s walk through a real-world scenario that’s becoming more common as organizations experiment with AI tools:

• Your team is piloting an AI-powered SaaS app. To streamline testing, an admin creates a test account (“Test User 1”) and grants it admin rights.
• Months later, that same test account is still active. It’s not tied to a real person, and its permissions have never been reviewed.
• Suddenly, a security tool flags an unusual login to Salesforce from a location your company never does business in. At first glance, it seems unrelated. But with the right visibility, you notice it’s the same identity: “Test User 1.”

On their own, each detail might seem harmless. But together, they reveal a bigger issue: lateral movement. A privileged account, intended only for testing, is now accessing another critical app from a suspicious location. This is the kind of risk that can lead to data exposure, compliance violations, or worse.

How do test users and shadow AI create hidden security gaps?

1. Test users often fly under the radar. They’re not part of your normal onboarding, may not be in your HR or IAM systems, and rarely get the same scrutiny as employee accounts. If they have admin access, the risk is multiplied.

2. AI apps introduce new types of identities and permissions. AI tools can act both as applications and as users—accessing, creating, or modifying data. If you don’t know which AI tools are present or who has access, you’re missing a major piece of the puzzle.

3. Traditional tools don’t reveal the full picture. Most security products are great at showing activity in one app or sending alerts about logins. But they rarely correlate discovery (what’s being used), identity (who’s accessing), and behavior (what’s happening) across your entire SaaS ecosystem. That leaves a gap that attackers—and even well-intentioned insiders—can exploit.

These gaps aren’t always obvious until something goes wrong, but they can be detected early with the right approach. And when multiple signals align, they point to threats that would otherwise go undetected.

Connecting the dots with SaaS identity threat detection

When organizations bring together app discovery, identity access, and behavioral analytics, the results speak for themselves. Most tools alert on isolated events (unless you have a solution like AppOmni that connects them). That’s the difference between reacting late and seeing the threat pattern early:

• Faster investigations. Context-rich signals help analysts spot toxic combinations of risk—like a test user with admin access behaving abnormally—without wading through endless alerts.
• Reduced exposure. By continuously mapping access and privileges, you can catch over-provisioned accounts and shut down unnecessary permissions.
• Less alert fatigue. By focusing on meaningful, correlated events, teams can prioritize real threats, not noise.
• Greater confidence in SaaS and AI adoption. With complete visibility, security teams can safely enable innovation while minimizing hidden risk.

How to connect Discovery, Identity, and Behavior

Step 1: Find everything, even the unapproved (App Discovery)

First, you need to see all SaaS and AI apps being used, not just the ones IT set up. Shadow IT and unsanctioned integrations are common sources of risk. Continuous discovery gives you a living inventory, surfacing apps and integrations you might not otherwise know exist.

Step 2: Map every account, especially the non-human ones (Identity Access)

Next, correlate identities across your SaaS landscape, including test users, service accounts, and AI bots. Know which accounts have privileged access, where those privileges extend, and whether they’re still needed. This is crucial for enforcing least privilege and Zero Trust.

Step 3: Spot suspicious patterns across apps (Behavior Analysis)

Finally, monitor behavior across applications—not in silos, but with context. Did a test account log into a tool as an admin and then access another critical tool from a new location? Are there privilege changes in one app followed by data downloads in another? Correlating this activity is how you catch subtle signs of lateral movement, privilege escalation, or data exfiltration.

Why SaaS identity threat detection matters, especially as AI usage grows

AI adoption means more integrations, more machine identities, and more opportunities for gaps to form between apps, users, and behavior. Attackers know that overlooked test accounts or stale admin privileges are the perfect foothold for moving laterally and escalating their access.

And it’s not just about external threats. Well-intentioned staff may set up accounts or integrations that inadvertently create risk, especially when testing new SaaS or AI tools. Without a connected view, it’s almost impossible to manage SaaS risk proactively.

See this in action: Investigate risks like a pro

Ready to see how a connected approach works in practice? In this episode of AppOmni SaaS Security Slice, we’ll walk through how to identify and investigate emerging SaaS risks, including how to:

• Discover shadow SaaS and AI usage
• Detect risky identities and behaviors
• Investigate threats with correlated insights

Watch the episode and see how to uncover the hidden connections that matter—before a small oversight becomes a major incident.