Case Study Fast Facts – Compliance Automation and GRC Integration Overview

1. Problem Statement

• Resource Constraints: A 30+ person GRC team with no dedicated engineering support.
• Inadequate Assurance: Manual sampling that provided insufficient coverage and lacked data QA.
• Manual Process Overload: Evidence collection and ticket creation across multiple tools.
• Scalability Crisis: 15–20 annual audits; headcount could not keep pace.
• Technical Debt: Ad-hoc internal scripts not suitable for production.
• Compliance Burden: Quarterly security operations reviews required testing 18 different controls.

2. What options did the customer evaluate?

• Internal Development: Building an internal engineering team (minimum 3 dedicated engineers).
• Traditional GRC Tools: Considered vendors like Drata but found rigid/incomplete integrations.
• Replacement Solutions: Considered replacing AuditBoard; rejected due to existing investment.
• Augmentation Tools: Searched for middleware to enhance the existing AuditBoard investment.

3. Why did the customer select ComplianceCow?

• The Only True Middleware Solution: Coordinate activities, collect evidence, manipulate data, and upload to AuditBoard.
• Non-Disruptive: Augments rather than replaces existing GRC investments.
• Customizable: Build features based on specific needs; avoids rigid, incomplete integrations.
• Engineering Alternative: Eliminates need to build an internal engineering team.
• Partnership Approach: Custom solutions and dedicated support.

4. Solution Provided by ComplianceCow

• Automated Quarterly Reviews: Automated security operations review procedures for 16 controls in ~8 weeks.
• Key Integrations: GitHub, AuditBoard, Jira, Snowflake, Slack, and Cloud Infrastructure services.
• Evidence Automation: Automated evidence collection, manipulation, and upload to AuditBoard.
• Full Population Testing: Complete data analysis rather than sampling.
• Engineering Capital: Acted as an extended GRC engineering team; implemented fallback plans and training sessions.

How we automated 16 PCI controls

• Data plumbing: Connected Snowflake, GitHub, and change sources; normalized datasets for assurance.
• Rules & checks: Encoded quarterly review procedures as full-population control tests (no small samples).
• Tasking in Slack/Jira: Owner prompts, evidence requests, and tickets created automatically with due dates.
• Automated evidence: Collected, transformed, and uploaded artifacts to AuditBoard for review continuity.
• Fail-safes & enablement: Fallback plans kept cycles moving; training improved ramp time and consistency.

5. Key Benefits of the Solution

• Reduced cognitive load; no manual queries or specialized technical skills.
• Reduced bias; automation of assurance process and remediation flows.
• Reduced operator toil; Slack follow-ups meet owners where they are.
• Improved engagement of GRC teams with Security and Engineering.
• Scalability without proportional headcount increase.
• Continuous assurance; continuous evidence collection across audit cycles.

6. Results

• Operational Success: 16 PCI controls automated in the recent quarter.
• Strategic Vision Progress:: Moving toward automating annual audits end-to-end.
• Continuous Improvement: Each controls attestation process shows improvement over the previous.
• Human hours reduced by ~80% through workflow automation and consolidated execution.
• Future Roadmap: AI-powered automation; self-serve build for assessments/rules/workflows; measurement hygiene (hours saved, time-to-complete).
• Expansion Plans: Iterative rollout to additional divisions.
• Marketing Value: Logo usage and testimonials in progress.

Technology Stack

• GRC/Assurance: AuditBoard
• Workflow/Collab: Jira, Slack
• Data: Snowflake
• Code/Changes: GitHub
• Deployment: SaaS (current scope)

FAQ - Key Questions

• How many PCI controls were automated in the latest quarter?: 16 controls were automated in the latest quarter.
• Which systems were integrated for evidence and workflow?: AuditBoard, Jira, Slack, Snowflake, and GitHub were connected and in active use.
• What changed in the assurance model?: Automation enabled full-population checks (replacing small manual samples), increasing audit confidence.
• What operational impact did the team report?: The program reduced human hours by roughly 80% through workflow automation and consolidated execution.
• What deployment model is used currently?: SaaS for current scope; an on-prem path would be considered if future integrations require internal-only systems.
• What’s next for the rollout?: The team is expanding automation patterns to additional business units and advancing self-serve assessments, rules, and workflows.