Home » User Access Review Data Collection for SOX Compliance

Process Details

  • Inputs: List of in-scope applications,Reviewer assignment list
  • Outputs: Consolidated user access listings and permission reports from all in-scope systems,Standardized evidence package for reviewers,List of users with potential SoD conflicts or policy violations
  • Systems: ERP Systems (SAP, Oracle, Microsoft Dynamics 365),Treasury Management Systems,GRC Tools (ServiceNow GRC) & Others

User Access Review Data Collection for SOX Compliance

Agnostic

Use Case Overview

An AI agent that automates the collection of user access listings and permission reports from various critical financial applications, preparing the data for periodic user access reviews as required by SOX.

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 Inputlist of in-scope applications for SOX user access reviews (e.g., ERP Systems, Financial Reporting Tools, Treasury Systems)
  2. Data ExtractionExport user lists and their assigned roles and permissions from all in-scope applications. Various formats (CSV, Excel, PDF, text) are involved here.
  3. Data Standardization & ConsolidationConsolidates the data into a central repository or GRC Tool (ServiceNow GRC)
  4. Anomaly FlaggingFlags users with excessive permissions based on pre-defined Segregation of Duties (SoD) rules. Identifies dormant accounts or accounts with last login dates exceeding a threshold.
  5. Evidence Package PreparationOrganizes the collected reports and any initial flags into evidence packages for each application owner or reviewer.

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.

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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.

How are the Segregation of Duties (SoD) rules defined and applied by the agent? +

The SoD rules are defined in a simple, readable format (like a spreadsheet) managed by your compliance or business process experts. For example, a rule could state “A user cannot have both ‘Create Vendor’ AND ‘Approve Payment’ permissions.” The agent reads this rule matrix and compares it against the permissions data it collects for each user, flagging any violations it finds.

How does the agent handle cryptic permission names from systems like SAP to make reviews meaningful for business owners? +

The agent can incorporate a “translation layer” or a mapping table. Your team can define this table to map technical, system-specific permission codes (e.g., SAP’s F_BKPF_BUK or VA01) to plain-English, business-friendly descriptions (e.g., “Ability to Post GL Journal Entry” or “Create Sales Order”). The final evidence package will then display these understandable descriptions alongside the technical codes, enabling business owners to make informed decisions about whether the access is appropriate.

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