The Three Lines Model of Risk Management

Organizations worldwide, and in particular the finance sector, have long relied on the Three Lines of Defense—now modernized as the Three Lines Model—as a blueprint for sound risk management.   Here's a quick primer on what it is, some of the challenges, and how AI can help make its implementation easier.

The Three Lines Model: A Quick Primer

Codified by the Institute of Internal Auditors (IIA), the updated Three Lines Model articulates the roles and relationships involved in effective governance and risk oversight:

• First Line: Operational management, responsible for delivering services and managing risks directly.
• Second Line: Risk and compliance functions, offering guidance, monitoring, and challenge.
• Third Line:  Independent assurance to the governing body, typically delivered by internal audit or other non-management providers.

Each line contributes to both the protection and creation of value, but doing so requires enormous effort in coordination, documentation, reporting, and review.

Where the Pain Lies: Manual Friction and Fragmented Reporting

Despite its conceptual strength, the model struggles in practice. Risk and compliance teams often spend disproportionate amounts of time on low-value manual tasks, such as:

• Gathering data across siloed systems.
• Formatting periodic risk reports.
• Manually reconciling control testing results.
• Tracking regulatory changes and mapping them to internal policies.
• Re-validating risk assessments for minor contextual shifts.

These functions are not only time-consuming but prone to human error. Meanwhile, internal auditors face mounting expectations to deliver faster, deeper insights—often with legacy tools and incomplete data.

Agentic AI: A New Operating Layer for the Three Lines

LLM-based AI, and AI agents, have the potential to radically reduce these frictions. By combining autonomous planning, tool integration, and context retention, these agents can operate persistently across the first, second, and third lines, automating workflows while adapting to changing organizational needs.

Here’s some examples:

1. Automated Control Monitoring (First & Second Lines)

Agentic AI doesn’t just trigger alerts—it can reason through next steps, update logs, and escalate anomalies to first-line operational owners for immediate action. Where threshold breaches or systemic patterns are detected, the agent notifies second-line risk or compliance teams for formal oversight and remediation planning. These interactions are logged end-to-end, preserving full audit trails.

2. Regulatory Mapping and Change Management (Second Line)

Financial institutions must constantly adapt to evolving regulatory frameworks. An agentic AI system integrated with external regulatory feeds and internal policy libraries can: Parse new regulations; Compare them with existing internal controls; Highlight gaps or misalignments; and, Propose new control language or risk narratives.

This allows second-line functions—such as risk, compliance, and legal—to shift from manual review toward higher-order tasks like validation, prioritization, and risk acceptance decisions. AI agents serve as an analytical front-end, accelerating compliance processes while preserving the judgment role of human experts.

3. Continuous Risk Reporting (All Lines)

Traditional risk reporting is static, retrospective, and slow. Agentic AI flips this model by: Aggregating real-time metrics from disparate systems; Generating draft risk dashboards or heatmaps; Synthesizing commentary based on risk trends, control failures, and remediation timelines.

These capabilities extend to the third line, where internal audit agents can autonomously generate audit working papers, extract testing evidence, and even conduct preliminary control assessments—significantly accelerating audit cycles and increasing coverage.

4. Real-Time Collaboration and Workflow Orchestration

Via integrations with systems like Slack, Jira, or ServiceNow, agentic AI can act as an integration layer across the three lines —ensuring that first-line teams, risk managers, and auditors are aligned. For example: An LLM agent might observe a pattern of control failures and draft a remediation plan; The same agent notifies the relevant owner, tracks approvals, and updates the control register; and then, If delays occur, escalate issues according to a predefined logic tree, with full traceability.

Building Resilience, Not Just Efficiency

Critically, the role of agentic AI is not just automation—it’s about resilience. By reducing time spent on rote tasks, organizations can reallocate human attention to high-order issues: emerging risks, strategic decisions, and culture. And because agents operate 24/7 with memory and reflection capabilities, they can detect patterns that humans might miss and enforce consistency across lines.

Moreover, these systems inherently support governance-by-design: all actions are logged, structured, and reproducible, making it easier to demonstrate compliance and readiness during regulatory reviews.

Final Thoughts: Augment, Don’t Replace

The Three Lines Model will remain foundational for financial institutions. But as the complexity of risk multiplies, LLM-based agents represent a necessary evolution. They don’t replace risk professionals—they augment their capacity, elevate their role, and streamline the model into a living, responsive system.

By thoughtfully deploying agentic AI, banks and financial institutions can honor the spirit of the Three Lines Model—clear roles, effective governance, and independent assurance—while finally escaping the inefficiencies that hold it back.