Automated Control Testing and Evidence Collection Workflow

Manual control testing and evidence collection for audits is a costly, repetitive bottleneck, consuming hundreds of hours across finance, IT, and compliance teams. A custom automation workflow eliminates this toil by deploying scheduled agents that pull data directly from source systems like SAP, Oracle, or IAM platforms. These agents execute predefined test logic against live data, evaluate compliance, and flag deficiencies for immediate remediation, shifting compliance from a reactive, audit-driven scramble to a proactive operational function with measurable ROI in labor savings and risk reduction.

Implementation requires integrating with system APIs or using secure browser automation for legacy tools. The orchestrator, built with frameworks like LangGraph, manages state, schedules, and exception handling. Each evidence pack is formatted, watermarked, and logged with a defensible audit trail in the GRC platform. Critical to the architecture are approval gates for high-risk findings, configurable review cycles, and real-time dashboards that provide continuous visibility into control effectiveness and testing status across all jurisdictions.

The workflow is triggered by a scheduled regulatory cycle or an ad-hoc audit request. An orchestrator, typically built with LangGraph or a custom Python service, retrieves the relevant control definitions and test procedures from a GRC platform like ServiceNow or RSA Archer. It then dispatches specialized agents to execute each test. These agents connect directly to source systems—such as SAP for financial controls, SailPoint for IAM reviews, or Snowflake for data governance checks—to pull raw data, evaluate it against predefined rules, and capture screenshots, logs, or data extracts as evidence.

Each agent packages its findings—including pass/fail status, supporting evidence files, and a confidence score—into a structured JSON payload. The orchestrator aggregates these into a pre-formatted evidence pack, such as a PDF or a structured dataset. Any control deficiencies are automatically scored for severity, routed to the appropriate owner via Jira or ServiceNow for remediation, and logged. The complete, versioned evidence pack is then archived to a secure, immutable audit repository like Azure Blob Storage with a full chain-of-custody log, creating a defensible audit trail and eliminating last-minute evidence collection scrambles.

This workflow automates the execution of control tests by deploying specialized agents that connect directly to source systems like SAP, Oracle ERP, SailPoint IAM, and SIEM platforms. Agents pull transactional data, user logs, and configuration states, then evaluate them against pre-defined compliance rules. The primary operational bottleneck eliminated is the manual, quarterly scramble where teams waste weeks gathering screenshots and log excerpts, often under audit pressure. Savings come from reducing FTEs dedicated to evidence collection by 60-80%, cutting external audit fees through cleaner, pre-validated evidence packs, and materially lowering the risk of control failures going undetected between audits.

Implementation requires building a central orchestrator, likely with LangGraph or a custom microservice, to manage the stateful execution of agentic tasks, handle retries, and enforce idempotency. Each control is codified as a test script with specific API calls, SQL queries, or browser automation steps. Critical architectural decisions include designing a version-controlled evidence store with immutable logs, integrating approval gates for failed tests before remediation, and implementing granular observability to trace each test's data lineage. Rollout is sequenced by control criticality, starting with high-volume, rule-based ITGCs, while governance is maintained through a change management process for the test logic itself.

Manual control testing for SOX, SOC 2, or ISO audits is a costly, repetitive bottleneck. It involves teams logging into disparate systems—ERP, IAM, cloud consoles—to run queries, capture screenshots, and compile evidence packs. A custom automation workflow deploys scheduled agents that execute test scripts, pull logs and configurations via API, and evaluate results against defined rules. This eliminates weeks of manual evidence gathering per audit cycle, reduces human error, and frees compliance staff for higher-value analysis and remediation of actual deficiencies.

Implementation requires integrating with systems of record (SAP, Okta, AWS Config), defining test logic in a version-controlled rules engine, and establishing robust exception routing to human reviewers in tools like ServiceNow. The orchestration layer, built with frameworks like LangGraph or Prefect, manages state, schedules, and dependencies. Critical controls include immutable audit logs of all agent actions, cryptographic hashing of evidence, and approval gates for any remediation actions. This architecture delivers continuous compliance monitoring, slashing pre-audit preparation time by over 70% and providing real-time visibility into control health.