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Process Details

  • Inputs: List of SOX controls and their associated evidence requirements,Specific parameters for data retrieval (e.g., date ranges, report names),Templates for expected evidence format (where applicable)
  • Outputs: Collected and organized SOX control evidence,Report of evidence completeness and initial validation results,List of exceptions or issues encountered during data gathering
  • Systems: GRC Platforms (ServiceNow GRC),ERP Systems (SAP, Oracle NetSuite, Microsoft Dynamics 365),CRM Systems (Salesforce),HRIS Systems (Workday),Document Management Systems (SharePoint, DropBox),Custom internal applications (via APIs or UI automation),Log Management tools (Splunk)

SOX Evidence Collection and Review

Agnostic

Use Case Overview

An AI agent designed to automatically access various business applications and repositories to gather predefined evidence required for Sarbanes-Oxley (SOX) control testing, perform initial checks for completeness or obvious errors, and organize the evidence for auditor review.

Challenges

  • High potential for costly errors from manual data handling.
  • Significant time and resources are spent on repetitive, low-value work.
  • The manual process is difficult to scale without increasing headcount.
  • Process bottlenecks lead to delays and missed deadlines.

Solution

This use case solution follows these general steps at a high level:

  1. Control Evidence Definition InputReceives a list of SOX controls to be tested and the specific evidence required for each (e.g., system-generated reports, screenshots of configurations, approval logs)
  2. System Access & Data RetrievalAccess various Business Applications (e.g., ERP systems like SAP/Oracle, CRM like Salesforce, HRIS like Workday), run reports, or query databases to extract the required evidence, and retrieves documents from Document Management Systems (e.g., SharePoint, OpenText) or shared drives
  3. Evidence ValidationPerforms basic checks on the retrieved evidence, such as verifying report date ranges, checking for signatures on approval forms, ensuring file completeness, or matching key parameters against control attributes
  4. Evidence Organization & StorageOrganizes the collected evidence in a structured manner (e.g., by control ID, testing period) within a designated secure repository.
  5. Flagging & NotificationFlags any missing evidence, access issues, or evidence that fails initial validation checks, and notifies the relevant control owner or auditor

Primary Benefits

  • Increase EfficiencyDramatically reduce the time and manual effort required to complete the process.
  • Enhance AccuracyEliminate human error to ensure data integrity and reduce financial risk.
  • Empower EmployeesFree your team from monotonous tasks, allowing them to focus on strategic work that requires their expertise.
  • Improve ScalabilityHandle growing volumes of work without a proportional increase in operational costs.
  • Ensure TransparencyMaintain a complete, auditable trail of every action the AI agent takes, described in plain English.

Related Use Cases

FAQ

Our applications range from modern SaaS to legacy on-premise systems. How does the agent extract data from such a diverse landscape? +

It can interact with systems in multiple ways:
APIs: For modern applications with available APIs.
Scripts: For legacy systems or databases.
File Processing: It can parse user lists from various formats, including CSV, Excel, and even structured text within PDFs.

What is the process for configuring the agent to collect evidence for our specific set of controls? +

This is done by translating your existing risk and control matrix into a “collection plan” for the agent.
For each control, you define:
The source application.
The specific report or document needed.
The parameters for the extraction (e.g., date ranges, company codes).
The validation checks to perform. This configuration is typically done once and then simply executed each testing period.

Controls and systems evolve. How do we update the agent's collection tasks? +

Because the collection plan is maintained separately from the core automation logic, updating a task is straightforward. If a control changes to require a new report, your team can simply update the plan on the Kognitos platform to point to the new source. This modular design means you can adapt to changes in your control environment without needing a major redevelopment project.

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