How to build a Compliance Control AI Agent

TL;DR

• Automates compliance control monitoring and reminders across departments.

• Prioritizes pending or critical controls, maps them to owners, and generates tailored evidence requests.

• Cross-references regulations with internal policies to ensure alignment and audit readiness.

• Sends structured follow-up emails and produces a clear summary report of all actions.

• Saves thousands of hours by reducing manual reviews, fragmented follow-ups, and repeated control cycles.

Who it’s for

• Compliance Officers, CISOs, Internal Audit, Risk & Control Owners

• Regulated industries: Finance, Insurance, Energy, Telco

The core challenges this solves

• Fragmented ownership and stale spreadsheets lead to missed follow-ups.

• Manually interpreting regulations and mapping them to internal policies is slow and inconsistent.

• Evidence requests are unstructured and bounce around inboxes without accountability.

• Quarterly or monthly repeat controls multiply the workload and error surface.

What the agent delivers

• Prioritized control queue (pending/overdue, severity-weighted).

• Targeted follow-up emails per control owner with specific questions and evidence requests.

• Summary report of who was contacted, for which controls, with due dates and status.

• Audit trail of control, rationale, requested artifacts, and communications.

Outcomes & KPIs

• Time to generate owner follow-ups ↓ 90%+

• % critical controls with on-time evidence ↑

• Cycle time from request to evidence received ↓

• Audit exceptions and rework ↓

Overview

Financial institutions operate under strict regulations and invest heavily in risk mitigation and compliance controls. These controls must be designed based on both regulatory directives and the institution’s specific business practices. Implementing them requires coordination across multiple departments to define control types, gather evidence, and ensure proper risk mapping—reducing the chance of non-compliance and costly penalties.

The Challenge

With the release of ISO 31000, companies have a global standard for risk management. However, applying it demands significant time and resources. Developing effective controls requires interpreting complex regulations, aligning them with internal policies, and negotiating with stakeholders on how to collect the necessary evidence.

This is where an AI Agent can help: it assists compliance officers by interpreting regulations, coordinating with departments, and engaging owners to collect the documentation needed to demonstrate during audits or investigations that controls are properly implemented.

The Solution

The AI Agent automates follow-up and verification of compliance controls required by regulatory frameworks:

• It starts with an assessment of risk criticality, identifying which controls are pending.

• A control sheet is used to automatically prioritize which items require urgent attention based on business impact and regulatory exposure.

• The system then cross-references relevant legislation with internal procedures to ensure alignment with compliance standards.

Once critical controls are identified, the Agent generates an automated follow-up process:

• Sends personalized emails to control owners with a summary of the pending control.

• Includes targeted compliance questions and requests for supporting evidence.

• Creates structured, accountable communication across departments.

This automation supports continuous monitoring, ensures documentation is maintained for audits, and establishes recurring check-ins for controls that repeat monthly, quarterly, or yearly. Overall, it reduces non-compliance risk, saves significant time, and improves the consistency, transparency, and reliability of regulatory reporting and governance—protecting both resources and reputation.

The Results

• Faster control questions: Generating targeted compliance questions now takes seconds instead of 30 minutes per control—delivering greater speed, accuracy, and consistency.

• Reduced manual review: By leveraging knowledge bases to interpret legislation and internal policies, the Agent eliminates hours of manual reading and cross-checking.

• Streamlined follow-ups: Instead of fragmented, delayed, and manually tracked emails, the Agent automatically identifies owners, generates structured follow-ups, and ensures persistent communication until evidence is collected.

Since these controls are often repeated regularly (monthly, quarterly, yearly), the time savings compound over time, allowing regulated organizations to save thousands of hours across compliance and support functions.

Step-by-step build (StackAI nodes)

1. Scheduled Trigger (Trigger Node)

• Purpose: Starts the workflow at regular intervals (daily, weekly, or quarterly).

• Details: Fully configurable automation trigger to ensure continuous monitoring of pending controls.

2. Control Sheet (Action Node)

• Purpose: Reads the list of compliance controls from a tracking sheet.

• Details:

  
  

  • Source: Google Sheet (or Excel in SharePoint)

  • Sheet: Control List

  • Range: A:K

  • Connection: Google Workspace

  
  

• What it does: Imports all control items with their status, descriptions, and metadata to identify which controls are still pending implementation.

3. Owners and Emails (Action Node)

• Purpose: Retrieves owner details and their email addresses.

• Details:

  
  

  • Source: Google Sheet (or another data source)

  • Sheet: Owners

  • Connection: Google Workspace

  
  

• What it does: Maps each control to its responsible owner for personalized communication and accountability.

4. Anthropic (LLM Node: Claude 3.5 Sonnet)

• Purpose: Central brain of the workflow. It analyzes controls, matches owners, generates follow-up questions, and drafts emails.

• How it works:

  
  

  • Inputs: Data from Control Sheet and Owners and Emails.

  • Knowledge Base: Connected internally to regulations, directives, and company procedures (no extra node required).

  • Tasks:

    
    

    • Identify pending/critical controls.

    • Cross-check them against regulatory and internal policy context.

    • Draft personalized compliance emails for each owner with targeted questions and evidence requests.

    
    

  • Tools: Directly integrated with Send Email in draft mode, so the model can generate and prepare messages without needing an extra Gmail node.

  • System Prompt: Frames the model as a compliance officer ensuring controls are properly implemented.

5. Output Summary Report (Output Node)

• Purpose: Provides a clear summary of the workflow’s results.

• What it does: Compiles a report showing which emails were drafted, for which controls, and the follow-up status of each owner.

Workflow Considerations:

1. Adjustments can be made to the LLM Model to retrieve different type of controls or to follow-up on “In-Progress” Tasks

2. There is no need to loop over the email function as the LLM Model will handle the

   repetitive email generation automatically

3. New policies and procedures can be attached to the model to increase the reliability

   of control questions associated to each risk.