To intelligently automate and enhance Risk & Control Assessment using Agentic AI services, we can design a multi-agent system where each AI agent performs a specialized function aligned with the traditional responsibilities of human actors across the three lines of defense.
Below is a breakdown of diverse agentic AI services required and organized by function, responsibility, and defense line:
1st Line of Defense (Business / Operations / Risk Owners)
1. Control Data Agent
Operational purpose: Continuously collect and maintain data on existing controls, updates from business units, and control test results. These controls are derived from multiple policies and standard operating procedures documentation.
- Tasks:
- Interface with systems which have control repository along with control owners.
- Controls are associated with risks and process data to identify any non-functioning controls during assessment.
- Update the Control Library, in case of any new control additions.
- Detect control design gaps or changes via data analytics, vis-vis regulatory policies, or internal procedural upgrades.
- Control assessment focuses on any weakness in control design or control operating effectiveness.
- AI Capabilities: Natural Language Processing (NLP), Robotic Process Automation (RPA), Knowledge Graphs.
AI Solutions:
- Natural Language Processing (NLP): For extracting controls from unstructured sources like policy documents, SOPs, audit reports.
- Robotic Process Automation (RPA): To automate data collection from multiple systems and update control libraries.
- Knowledge Graphs: To map relationships between controls, risks, and processes for better traceability and reasoning.
2. Operational Process Mapping Agent
- Business Purpose: Map and monitor business processes for any gaps prompting inherent risks.
- Tasks:
- Extract workflows & tasks to be attended from BPM tools.
- Identify critical control points, which are mapped to risks and other dimensional reference points.
- Simulate process scenarios to highlight potential weaknesses.
- AI Capabilities: Process Mining, Simulation Modeling, NLP.
AI Solutions:
- Process Mining: Automatically discovers, monitors, and improves real processes by extracting knowledge from event logs in BPM systems.
- Simulation Modeling: Uses statistical models and simulations (e.g., Monte Carlo) to test different scenarios and identify potential risk bottlenecks.
- NLP: To understand and convert semi-structured workflow documents into formal process models.
2nd Line of Defense (Compliance / Risk Function)
3. Risk Identification & Scoring Agent
- Purpose: Continuously identify inherent & emerging risks and score them based on internal and external data.
- Tasks:
- Leverage external news, regulatory changes, and internal incidents.
- Calculate inherent and residual risk scores, which will have computation viz, frequency & severity of the risk
- Continuously update the risk profile of the processes
- Recommend priority areas for assessment.
- AI Capabilities: Machine Learning (ML), Anomaly Detection, Sentiment Analysis.
AI Solutions:
- Machine Learning (ML): For pattern recognition in historical incidents and emerging data trends to detect new risks.
- Anomaly Detection: Identifies deviations in behavior or data that could indicate emerging risks.
- Sentiment Analysis: Processes external data (news, social media, regulatory updates) to identify negative sentiment or early risk indicators.
4. Assessment & Response Agent
- Purpose: Automate the preparation and distribution of risk assessments.
- Tasks:
- Populate risk templates based on historical data.
- Suggest responses and control improvements.
- Trigger workflows for review and sign-off.
- AI Capabilities: LLMs for structured documentation, AutoML for recommendation systems.
AI Solutions:
- Large Language Models (LLMs): To auto-generate structured assessment documentation and response recommendations.
- AutoML (Automated Machine Learning): Optimizes models that recommend risk responses and control adjustments with minimal human tuning.
5. Control Effectiveness Evaluation Agent
- Purpose: Evaluate whether controls are effectively mitigating the risk.
- Tasks:
- Analyze historical performance of controls.
- Use audit and incident data to assess control effectiveness.
- Provide visualizations for decision-making.
- AI Capabilities: Predictive Analytics, Reinforcement Learning.
AI Solutions:
- Predictive Analytics: Uses past control performance data to predict future effectiveness.
- Reinforcement Learning: Continuously improves control strategies based on trial-and-error feedback (e.g., which controls most effectively mitigate specific risks).
3rd Line of Defense – Audit Management
6. Audit Readiness & Compliance Agent
- Purpose: Ensure all assessments and documentation are audit ready.
- Tasks:
- Maintain audit trails.
- Validate data lineage and source.
- Cross-verify assessment logic for independence.
- AI Capabilities: Explainable AI (XAI), Rule-based Systems, Traceability Frameworks.
AI Solutions:
- Explainable AI (XAI): Provides transparency in model decisions (important for audits).
- Rule-Based Systems: Codify regulatory compliance rules to verify if processes meet audit criteria.
- Traceability Frameworks: Ensure full visibility from data origin to decision-making (e.g., data lineage tracking).
Cross-functional Agents
7. Knowledge Management Agent
- Purpose: Create, maintain, and update the institutional knowledge base on risks and controls.
- Tasks:
- Train in past risk assessments, audits, incidents.
- Answer queries from business users.
- AI Capabilities: LLM fine-tuning, Semantic Search, Ontology Management.
AI Solutions:
- LLM Fine-tuning: Tailors a general-purpose LLM to the organization’s specific risk/control domain for answering queries.
- Semantic Search: Allows intelligent search across documents using context and meaning rather than keywords.
- Ontology Management: Structures and updates the risk-control knowledge domain (taxonomy, definitions, relationships).
8. Collaboration & Orchestration Agent
- Purpose: Coordinate tasks among different agents and human stakeholders.
- Tasks:
- Route tasks to appropriate agents or humans for validations.
- Escalate risks that need mitigation or remain unresolved.
- Schedule periodic assessments automatically.
- AI Capabilities: Multi-Agent Orchestration, Workflow Engines.
AI Solutions:
- Multi-Agent Orchestration: Enables coordination between various AI agents and human roles.
- Workflow Engines: Manages task routing, approval hierarchies, and escalations across defense lines.
9. Change Detection & Alerting Agent
- Purpose: Detect deviations from expected control behavior change or emerging external risks.
- Tasks:
- Monitor key risk indicators (KRIs) or key control indicators (KCI’s)
- Alert on anomalies or threshold breaches.
- AI Capabilities: Streaming Data Analysis, Event Processing, Alert Prioritization.
AI Solutions:
- Streaming Data Analysis: Continuously monitors data streams for KRI/KCI metrics.
- Event Processing: Identifies and responds to significant events (e.g., threshold breaches).
- Alert Prioritization Models: Uses ML to score the severity and urgency of alerts.
10. Reporting & Dashboard Build Agents
- Purpose: To update & build a risk register & control register to submit to the board
- Tasks:
- Collate all the data from the risk profile & control profile agents
- Create the risk & control register by giving a accurate picture of the status of the control mitigation aspects
- Collect the output data from risk appetite matrix to measure if the residual risk if more than the inherent risk
AI Capabilities: Streaming Data Analysis, Event Processing, Alert Prioritization.
AI Solutions:
- Data Aggregation Engines: Merge multiple sources (risk, control, audit) into coherent summaries.
- Data Visualization Tools: AI-enhanced tools like Power BI with AI visuals, Tableau with ML predictions.
- Risk Appetite Matrix Calculators: Use rule-based logic and ML to evaluate whether residual risk is acceptable based on organization-defined thresholds.
