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?

[Request Early Access →]

About the Author: Jack Miller is the CTO and Co-founder of HelmGuard.