AI Agents in Enterprise Risk Management: From Structured Workflows to Dynamic Problem-Solving
Discover how AI transforms enterprise risk management through structured workflows and dynamic agents, enabling both automated processes and flexible problem-solving.
Jan 8, 2025
In the rapidly evolving landscape of enterprise risk management, AI isn't just enhancing existing processes—it's fundamentally reimagining how organizations understand and respond to risk. At HelmGuard, we're deploying AI capabilities across two distinct but complementary approaches: structured workflows and dynamic agentic systems.
The Dual Nature of AI in Risk Management
As we work with enterprises to transform their risk management capabilities, we've found that different challenges demand different solutions. Some tasks benefit from carefully orchestrated, predictable processes, while others require more flexible, autonomous problem-solving. Understanding this distinction is crucial for organizations looking to maximize the value of AI in their risk operations.
Structured Workflows: Precision Through Process
Our structured workflows represent carefully designed, AI-enabled pipelines that tackle specific, well-defined challenges in risk management. These workflows excel when:
The process has clear inputs and outputs
Steps can be precisely defined
Quality standards must be consistently met
Integration with existing systems is crucial
For example, one of our core workflows automates the mapping of security frameworks to an organization's environment. This process requires precise understanding of both the framework requirements and the client's security landscape—a perfect case for a structured, AI-enabled pipeline that ensures consistency and accuracy.
Dynamic Agentic Systems: Flexibility Through Intelligence
In contrast, our dynamic agentic systems—embodied in our AI assistant Helm—handle more open-ended challenges that require adaptability and contextual understanding. These systems shine when:
Problems are less structured or predictable
Multiple approaches might be valid
Real-time adaptation is necessary
Integration with human workflows is key
Helm can receive natural language instructions and work with a suite of security primitives—from reading security APIs to creating Jira tickets or sending Slack messages. This flexibility allows it to tackle complex, multi-step problems while maintaining clear communication with human team members.
Building Effective AI Systems for Risk Management
Drawing from industry best practices and our own experience, we've developed key principles for building these systems:
Design for Integration
Build systems that work seamlessly with existing security tools
Ensure clear handoffs between AI and human team members
Create robust feedback loops for continuous improvement
Prioritize Control and Oversight
Implement appropriate guardrails for autonomous operations
Maintain clear audit trails of AI actions
Enable human oversight at critical decision points
Making It Work in Practice
Here's how these approaches complement each other in real-world scenarios:
Structured Workflow Example: When a new compliance framework needs to be implemented, our structured workflow systematically:
Analyzes framework requirements
Maps them to existing controls
Identifies gaps
Generates actionable recommendations
Dynamic Agent Example: When managing employee access lifecycles, Helm operates as an intelligent orchestrator:
Monitors HR systems for employee changes and automatically triggers appropriate workflows
Processes access requests through natural language interaction, gathering context and justification
Applies access policies with both rigor and flexibility—automatically handling standard cases while intelligently routing exceptions
Manages the complete lifecycle from onboarding through role changes to offboarding
Maintains audit trails and generates compliance documentation
Under the hood, we're exploring an innovative dual-phase approach: using test-time compute for sophisticated planning, followed by more traditional agent architectures for reliable execution. This allows us to combine the benefits of extensive reasoning during the planning phase with efficient, reliable execution of the developed plans.
Looking Forward
The future of enterprise risk management lies in effectively combining both structured and dynamic approaches. As we continue to develop these capabilities, we're focused on:
Expanding our library of specialized workflows
Enhancing Helm's ability to handle complex, multi-step tasks
Improving integration with enterprise systems
Developing more sophisticated feedback mechanisms
By thoughtfully applying both structured workflows and dynamic agents, we're helping organizations move from reactive to proactive risk management, ensuring they stay ahead in an increasingly complex threat landscape.
Interested in seeing how HelmGuard can transform your risk management?
About the Author: Jack Miller is the CTO and Co-founder of HelmGuard.