What Does Automated Evidence Collection Look Like for SOC 2

Automated evidence collection for SOC 2 is the process of using AI-driven workflow recorders to capture real-time application interactions, screenshots, and system metadata. Unlike infrastructure monitoring, it focuses on "last mile" controls—such as role-based access and change management approvals—to generate audit-ready PDF evidence packs that integrate directly with GRC platforms like Drata or Vanta.

Why Automated Evidence Collection Matters in 2025

The primary challenge in modern SOC 2 audits is the "20% manual gap." While legacy compliance automation platforms successfully monitor infrastructure (AWS, GitHub, Okta) via API, they cannot "see" what happens inside your proprietary application.

Historically, this required security teams to spend 40–80 hours per quarter manually taking screenshots, blurring PII, and writing narratives. Automated evidence collection eliminates this manual labor by using AI agents to record tests and generate documentation that is more accurate and verifiable than human-made files.

The Hidden Costs of Manual Evidence

Context Switching: Engineering teams lose focus when interrupted for "audit screenshots."
Human Error: Missing timestamps or blurry images lead to auditor follow-up requests.
Audit Friction: Auditors spend more time verifying the authenticity of manual files, extending the audit window.
Point-in-Time Risk: Manual evidence only proves compliance at the moment the screenshot was taken, creating gaps in continuous monitoring.

What Does the "Automated" Process Look Like?

Automated evidence collection transforms a multi-step manual chore into a streamlined, verifiable workflow. Here is the functional breakdown of how the technology operates.

1. Workflow Recording (The "Capture" Phase)

Instead of taking static screenshots with a snipping tool, you use an AI-powered browser extension or agent (like Screenata). You perform the control test—for example, showing that a non-admin user cannot access billing settings—while the agent records the DOM (Document Object Model), network requests, and visual frames.

2. AI-Driven Metadata Extraction

The system uses Optical Character Recognition (OCR) and computer vision to understand what is happening on the screen. It automatically identifies:

User Identity: Who is performing the test?
System State: Which URL and environment (Production vs. Staging) are being tested?
Timestamps: Cryptographically verified NTP-synced time.
Control Context: Which SOC 2 Trust Services Criteria (TSC) does this action satisfy?

3. Automated PII Redaction

Privacy is a major concern during evidence collection. Automated tools use AI to detect and blur Personally Identifiable Information (PII) like email addresses, credit card numbers, or names before the evidence is saved. This ensures that your SOC 2 audit does not inadvertently trigger a GDPR or CCPA violation.

4. Generation of "Evidence Packs"

The final output is not just a folder of images. It is a structured Evidence Pack (typically a ZIP or PDF) containing:

The Narrative: An AI-generated description of the test steps.
Visual Proof: High-resolution, timestamped screenshots.
The Manifest: A JSON file containing machine-readable metadata for the auditor’s tools.

How to Automate SOC 2 Evidence: A Step-by-Step Guide

Step 1: Map Your Manual Controls

Identify the controls that Drata or Vanta cannot automate via API. Common examples include:

CC6.1: Logical access to application settings.
CC7.2: Visual proof of production deployment approvals.
CC8.1: Screenshots of vulnerability scan results from internal dashboards.

Step 2: Initialize the AI Agent

Open your evidence automation tool (e.g., Screenata) and select the specific control you are testing. The AI loads the "Success Criteria" for that control so it knows what to look for during the recording.

Step 3: Execute the Test Flow

Perform the action in your browser. The agent captures every click and page load. If you are testing CC6.1, you would log in as a "Viewer," navigate to the "Admin" panel, and show the "Access Denied" screen.

Step 4: Review and Redact

The AI presents the captured sequence. It will suggest redactions for sensitive data. You confirm the narrative description written by the AI to ensure it matches your internal terminology.

Step 5: Export to your GRC

With one click, the Evidence Pack is sent to your GRC platform (Drata, Vanta, Secureframe). The task is marked as "Complete" in your compliance dashboard, and the auditor has everything they need for review.

Comparison: Manual vs. Automated Evidence Collection

Feature	Manual Collection (Legacy)	Automated Collection (Screenata)
Effort per Control	60–90 Minutes	5 Minutes
Evidence Format	Unstructured PNGs/Word Docs	Structured, Searchable PDF Packs
Metadata	None (or manual typing)	Cryptographic timestamps & DOM data
PII Handling	Manual blurring in Photoshop	Automated AI-based redaction
Auditor Trust	Low (Easy to manipulate)	High (Verifiable metadata chain)
Preparation Time	4–6 Weeks	1–2 Days

Example Use Case: CC6.1 – Logical Access Controls

Objective: Prove that the application restricts access to sensitive administrative functions based on user roles.

The Automated Workflow:

Trigger: The compliance manager assigns a task to verify role-based access for the internal CRM.
Action: The engineer opens Screenata and logs in as a "Support Agent."
Test: They attempt to click the "Export All Customer Data" button. A modal appears saying "Permissions Required."
Capture: Screenata records the click, the modal, the URL, and the user's session ID.
Output: A PDF report is generated titled CC6.1_Access_Restriction_CRM.pdf. It contains three screenshots with captions: "User Login," "Attempted Export," and "Access Denied Message."
Result: The report is automatically attached to the CC6.1 control in Vanta.

The Anatomy of an Audit-Ready Evidence Pack

What does the actual file look like when it reaches an auditor? A modern, automated evidence pack consists of four critical layers:

1. The Executive Summary

A cover page detailing the Control ID, the date of the test, the individual who performed it, and the final result (PASS/FAIL).

2. The Step-by-Step Narrative

AI-generated text that describes the logic of the test. Example: "The tester navigated to /settings/billing. The system identified the user as 'Role: Viewer' and correctly suppressed the 'Update Credit Card' button, satisfying the requirement for least-privilege access."

3. The Visual Evidence Chain

A sequence of screenshots with "Compliance Overlays." These overlays highlight the specific elements on the page that prove the control is active (e.g., a green box around a "Two-Factor Authentication Enabled" badge).

4. Technical Metadata (The Manifest)

A manifest.json file that includes:

Hash Values: To prove the screenshots haven't been edited.
Browser Logs: To show the system's technical response (e.g., a 403 Forbidden error).
Environment Specs: Proving the test occurred in the production environment.

Integration: Connecting Screenata with Drata and Vanta

Automated evidence collection is not a replacement for GRC (Governance, Risk, and Compliance) platforms; it is an essential extension.

Drata & Vanta: Act as the "System of Record." They track your policies, monitor your AWS/GCP settings, and manage your employee onboarding.
Screenata: Acts as the "Evidence Sensor." It captures the application-level data that the GRCs cannot reach via API.

The Integrated Workflow:

Vanta flags a manual control (e.g., "Quarterly Access Review") as "Action Required."
The user launches Screenata directly from the browser.
The evidence is captured and formatted.
Screenata uses the Vanta API to upload the PDF evidence pack directly to the correct control, moving the status from "Gap" to "Compliant" instantly.

Best Practices for Automated Evidence Collection

To ensure your automated evidence is accepted by Big 4 and mid-market auditors, follow these best practices:

Enable Continuous Capture: Don't wait for the "audit window." Record your evidence monthly to prove that controls were functioning throughout the entire Type II period.
Use Production Data (Carefully): Auditors prefer evidence from your live production environment. Ensure your automation tool has robust AI redaction to keep PII out of the audit logs.
Standardize Your Narratives: Use the same terminology in your AI-generated narratives that you use in your written SOC 2 policies.
Include "Negative" Tests: Don't just show things working; show them failing for unauthorized users. Proving that an unauthorized person cannot do something is often stronger evidence for CC6.1 than showing an admin can do it.
Review the Manifest: Occasionally check the JSON metadata to ensure your system is capturing the correct environment data (URLs, IP addresses).

Frequently Asked Questions

What is the difference between a screen recorder and automated evidence collection?

A screen recorder (like Loom) creates a video file that an auditor must watch in its entirety. Automated evidence collection (like Screenata) understands the UI, extracts metadata, blurs PII, and generates a structured, searchable PDF report that maps directly to SOC 2 controls.

Do auditors accept AI-generated evidence?

Yes. Auditors value consistency and traceability. AI-generated evidence packs that include cryptographic timestamps, DOM data, and verifiable metadata are often considered more reliable than manual screenshots, which are easily manipulated.

Can I use this for ISO 27001 or HIPAA too?

Absolutely. While the Control IDs might change (e.g., Annex A controls for ISO 27001), the requirement for visual proof of application-level security is consistent across all major security frameworks.

How much time does it actually save?

On average, companies using Screenata reduce their manual documentation time by 92%. What usually takes a full work week (40 hours) can be completed in less than 4 hours of total effort.

Does it replace Drata or Vanta?

No. It complements them. Drata and Vanta automate your infrastructure; Screenata automates your application. You need both to achieve 100% automation.

Key Takeaways

✅ Automated evidence collection closes the 20% gap of manual application testing that traditional GRC tools miss.
✅ AI agents capture visual proof plus technical metadata (DOM, timestamps) for higher auditor trust.
✅ PII is redacted automatically at the source, ensuring privacy compliance during the audit.
✅ Audit-ready PDF packs replace messy folders of screenshots, reducing audit review time by weeks.
✅ Integration with Drata and Vanta creates a seamless "continuous compliance" loop for SOC 2 Type II.

Learn More About SOC 2 Automation

For a complete guide to automating SOC 2 evidence collection, including what automated evidence collection looks like in practice, see our comprehensive SOC 2 automation guide.