Agentic Secret Finder (ASF) is an AI-powered capability in Microsoft Security Copilot that detects leaked credentials in unstructured content, such as emails, chat logs, documents, and screenshots, where traditional pattern-matching tools struggle. Agentic Secret Finder (ASF) is “agentic” because it relies on a multi‑step, multi‑agent reasoning workflow rather than a single pass detector. Detection, verification, and contextual analysis are handled by distinct reasoning stages, allowing ASF to find real credentials without flooding users with false positives. Unlike regex-based scanners, ASF uses reasoning to identify not just credentials, but the systems they unlock, helping security teams understand exposure and respond faster. In benchmark testing on synthetic datasets, ASF achieved 98.33% true credential detection with zero false alarms on realistic emails, chats, notes, and documents—while traditional regex scanners detected only about 40% of the same credentials. ASF is now generally available in Security Copilot, supporting 20+ credential types with high precision and actionable context.

The Problem: Credentials Hide Where Traditional Tools Can’t See

When security incidents happen, leaked credentials don’t always appear in clean, predictable formats. They show up buried in email threads, pasted into Teams messages, embedded in Word documents, or captured in screenshots of logs and terminals. These are exactly the places where security teams spend the most time and where traditional credential scanning tools fail.

Most existing tools rely on regular expressions or simple pattern matching. This works reasonably well for structured environments like source code repositories, where credentials follow predictable formats. But in real-world incidents, credentials look different. A storage key might be split across multiple messages in an email thread. A credential could be reformatted, partially redacted, or embedded alongside explanatory text.

In these situations, pattern matching produces two painful outcomes: it misses real credentials because the format doesn’t match a known rule, or it floods analysts with false positives that waste time. Security teams are left manually reviewing content, guessing which findings are real, and piecing together what systems might actually be at risk. In practice, this failure mode has a real human cost that security analysts end up reviewing thousands of alerts, manually inspecting email threads and chat logs, and trying to determine whether a suspicious string actually unlocks a storage account, API, or production service. Teams can spend days reconstructing context across messages and documents just to understand what a credential grants access to, slowing containment and increasing risk during active incidents.

This is the gap Agentic Secret Finder was built to close.

The Solution: ASF Brings Reasoning to Credential Detection

Agentic Secret Finder approaches credential detection as a reasoning problem, not a string-matching exercise. Instead of asking “does this text match a pattern?” ASF asks human-like questions: Is this text describing a credential or access mechanism? Does the value look real and usable? What system or resource could this access?

This shift is subtle but powerful. ASF doesn’t just detect credentials, it connects them to doors: the specific targets those credentials unlock, such as API endpoints, storage accounts, applications, or services. This is critical for triage. Instead of stopping at “this looks like a credential,” ASF tells analysts what that credential actually opens. Without context, a credential triggers manual follow‑up. When it’s linked to a specific target, analysts can immediately assess impact and act.

By understanding messy, real-world content the way a human investigator would, ASF delivers findings that security teams can trust and act on immediately. It’s designed specifically for the unstructured, noisy environments where incidents actually unfold.

Why ASF Outperforms Traditional Pattern Matching

Traditional credential scanners are built for clean data. ASF is built for reality.

Traditional tools struggle when:

• Credentials appear in natural language descriptions rather than code
• Context determines whether a string is sensitive or benign
• Credentials are incomplete, malformed, or partially redacted

ASF excels because it:

• Reasons through context, understanding surrounding text to identify what’s truly sensitive
• Detects credentials and their associated resources together, providing the “what” and the “where” in a single pass
• Handles noisy, unstructured inputs like emails, chat logs, documents
• Assigns confidence scores to help teams prioritize findings and reduce alert fatigue

What ASF Can Do Today

ASF is now generally available in Microsoft Security Copilot, with capabilities shaped directly by real security workflows across incident response, red teaming, and SOC operations.

ASF detects over 20 major credential categories, spanning cloud provider credentials like Azure Storage Keys and AWS Access Keys, authentication credentials including Microsoft Entra passwords and OAuth tokens, database connection strings, SSH private keys, API keys, and generic credentials that don’t fit predefined patterns. This broad coverage means analysts can scan investigation artifacts without worrying whether the credential type is supported.

What makes ASF particularly effective is where it works. Email threads where credentials are discussed across multiple messages. Teams chats where credentials are pasted quickly during troubleshooting. Word documents and internal wikis where credentials are documented for operational handoffs. Incident reports and post-mortem notes written under pressure. These are the environments where traditional pattern-matching tools fail, and where ASF delivers the most value.

In benchmark evaluations, ASF achieved 100% recall with 0% false positives on synthetic datasets containing embedded Azure Storage credentials, compared to 40% recall from traditional regex‑based tools such as CredScan. In more complex scenarios involving multiple credential types and noisy email content, ASF maintained 98.33% recall with 0% false positives. These results were observed on synthetically generated evaluation datasets spanning emails, chats, notes, and documents, designed to reflect how engineers communicate and how credentials may be inadvertently shared in real‑world workflows.

ASF is currently integrated into Security Copilot, actively supporting incident response workflows, and working toward deeper integrations with developer platforms such as GitHub to bring contextual credential detection to source code analysis at scale.

Using ASF in Security Copilot

ASF is available as a skill in Microsoft Security Copilot, making credential detection a seamless part of analyst workflows.

How to use ASF:

1. Enable the ASF skill in Security Copilot via “Manage Sources” → “Manage Plugins” (Figure 1)
2. Select “FindSecretInText” from Promptbook (Figure 2)
3. Submit unstructured content directly in the Copilot prompt: paste the text blob that might contain credentials (Figure 3)
4. ASF analyzes the content using its multi-agent workflow, detecting credentials and associated doors (Figure 4)
5. Review actionable findings with contextual details

Figure 1. Enabling the Agentic Secret Finder (ASF) skill in Microsoft Security Copilot

Figure 2. Selecting the FindSecretInText prompt, which invokes ASF’s multi‑step credential detection and verification workflow

Figure 3. Submitting a text blob containing embedded credentials for analysis (example is synthetic)

Figure 4. ASF output with detected credentials and associated doors (example credentials and associated doors are synthetic)

What’s Next for ASF

ASF is a living capability. Over the next six months, we are working towards coverage and deepening integrations:

• Exploring integrations with GitHub to reduce false positives in credential scanning for code repositories
• Optimizing for large-scale analysis to handle enterprise-wide scans efficiently with reduced latency
• Exploring graph-based risk modeling to map relationships between credentials, services, and attack paths

Our long-term vision goes beyond detection: we want to help security teams understand how credentials are used, what risks exist if they’re exposed, and what the impact of rotation or revocation would be. By moving from “what’s leaked” to “what does it mean,” ASF will enable smarter prioritization, faster response, and more confident decision-making.