Identity-Aware Secret Scanning: From “Found” to “Fixed”

Identity-aware secret scanning connects exposed credentials to the non-human identities behind them, so security teams can understand risk, map blast radius, and remediate safely without breaking production.

Secret scanning is having a weird moment: we’re finding more leaked credentials than ever, but we’re not fixing them any faster. Tools can flag an API key in Slack or a token in a repo in seconds — but they can’t tell you whether it’s still live, what it can reach, or how to disable it without breaking something.

This gap between detection and remediation is where most organizations stay stuck. It’s also the gap Oasis is built to close.

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Why is traditional secret scanning no longer enough?

Secret scanning changed the game at first. Suddenly you could see API keys, tokens, and passwords bleeding out of code and collaboration tools that were previously opaque. It felt like progress… until the volume spiked and the fixes didn’t keep up.

Fast-forward a few years and the picture looks very different. Secret sprawl has exploded. AI agents and automation frameworks are creating and passing around credentials at machine speed. Research teams keep publishing numbers in the millions for secrets leaked on public GitHub alone, and most of those credentials stay valid long after they’re exposed.

So yes, scanning is necessary. But for most organizations, it’s no longer the hard part.

The hard part is everything that comes after the alert fires.

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Why does secret remediation break down after detection?

If you’ve ever been on the receiving end of a “high-priority” secret scanning alert, you know the pattern. A token shows up in Slack, or in a pull request, or buried in a CI log. The scanner tells you it looks like an AWS access key or a database password. It might even give you a confidence score.

From there, the questions start: Is it real? Is it still valid? What does it touch? Who owns it? What breaks if we turn it off?

None of those questions are answered by the string itself. They’re questions about identity.

Most scanners stop short of that. They’re good at pattern matching — and increasingly at using AI to decide whether something “looks secret-ish” — but they don’t know how your systems are wired together. They don’t know which services rely on that credential, which data it can reach, or which team actually owns it.

So the burden falls on humans. Every non-trivial finding turns into a small investigation: checking cloud consoles, chasing down service owners, grepping configs, reverse-engineering how a token is used. Security teams pay this investigation tax over and over again.

Even when you finally understand the picture, you’re stuck in an uncomfortable tradeoff. You know you should revoke or rotate the secret, but if you don’t really know where it’s wired in, you risk “fixing” the issue by taking production down.

Move too fast and you break things. Move too slowly and you leave the window open.

That’s the real problem with traditional secret scanning — not that it misses secrets, but that it leaves you alone at the most dangerous part of the journey: remediation.

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What is identity-aware secret scanning?

Identity-aware secret scanning links exposed credentials to the non-human identities that use them, validating whether they are live, what they can access, and how they should be remediated.

Instead of treating secrets as random strings, it treats them as broken identities.

At Oasis, we start with non-human identities — service accounts, service principals, managed identities, API keys, bots, and agents — and build a live inventory of how they authenticate, what they access, how they behave, and who owns them.

When a scanner finds a leaked secret, the first step is easy: detect and redact it. But from there, a different question matters more — which identity does this belong to?

Once you can answer that, everything else gets easier. You can tell if the secret is still valid, understand its privileges and blast radius, see how it’s used, and route the issue to the team that actually owns it.

That’s the shift from “I found a string in a repo” to “I understand the risk of this identity and how to fix it safely.”

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How does identity context change secret remediation?

Identity context reveals what a secret can do, where it’s used, and who owns it — making safe rotation or revocation possible.

This turns remediation from an investigation problem into an execution problem.

Instead of broadcasting alerts into the void, incidents are tied to specific non-human identities with known owners. Instead of guessing what might break, teams can see dependencies and usage patterns before taking action.

That’s what we mean by identity-aware secret scanning: moving from string-level signal to identity-level understanding.

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A better story for “secret found in Teams”

Consider a very ordinary incident.

An engineer copies a key into a Teams thread to debug an issue. The message gets deleted a few minutes later, but not before your scanner sees it and opens an alert.

In most setups, the next week is spent validating the finding, hunting through consoles, checking logs, filing tickets, chasing down owners, and eventually asking someone to rotate the key — hoping it doesn’t break something important.

With Oasis in the loop, the same alert kicks off a very different flow.

The secret is matched against your inventory of non-human identities. The platform knows it’s not just “some key,” but the credential for a specific service accounts backing a customer-facing application. It can see that this identity has write access to sensitive data, runs in production, and has been used in the last 24 hours.

Because ownership is part of the model from day one, the incident is routed to the team that can actually fix it. From there, remediation options are clear: revoke immediately if risk is high, or orchestrate a safe rotation that updates consumers, verifies health, and only then retires the old secret.

The difference isn’t just better alerts. It’s that the path from detection to remediation is built in, not bolted on.

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From scanning to full remediation

At scale, secret scanning will always surface more findings than anyone wants to triage. The goal isn’t just to drive that number to zero — it’s to make sure the ones that matter are understood and fixed quickly.

For every exposed secret, teams need answers to four questions:
Is it live? What can it do? Has it been abused? Who owns it?

Traditional scanners stop at the first step. Identity-aware secret scanning completes the picture by correlating secrets to non-human identities, assessing usage and exposure, assigning ownership, and driving safe remediation — whether that’s revocation or rotation.

Once this loop is in place, remediation becomes repeatable instead of reactive. You’re no longer hoping separate tools and teams will connect the dots under pressure.

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Where secret scanning fits in a healthier future

The long-term direction is clear: fewer long-lived secrets, more ephemeral and policy-driven access. Managed identities instead of hard-coded keys. Short-lived credentials instead of permanent ones.

But most organizations aren’t starting from a clean slate. They’re starting from years of accumulated secrets scattered across code, vaults, SaaS tools, logs, tickets, and now AI agents.

Identity-aware secret scanning is how you live in both realities at once.

You still need scanners. You still need vaults. You still need good developer hygiene. What’s been missing is the layer in between — a platform that understands who a secret belongs to, what it can do, and how to change it without breaking what your business runs on.

That’s what Oasis is built for: making “found” and “fixed” part of the same motion, not two different stories that sometimes connect — and too often don’t.