Deploying AI Agents in Regulated Industries: What to Know

AI agents are transforming how organizations operate, make decisions, and deliver value. For regulated industries like banking, healthcare, and insurance, these AI-powered assistants promise unprecedented efficiency and innovation—but they also present unique compliance and governance challenges that data leaders must address with urgency and strategic foresight.

What is an AI agent?

AI agents combine AI with automation to make decisions, work collaboratively, and complete tasks with minimal human intervention. As former Gartner analyst Sanjeev Mohan explains in conversation with Alation CEO Satyen Sangani:

"An agent is the next generation of robotic process automation to some extent... It's autonomous, which means it's operating on its own. It's taking action on my behalf... But the difference is that an agent is built on top of a foundation model, like a large language model... There are four parts to an agent. It has to sense the source or sense my environment, reason what to do with this, come up with a series of steps, and then act upon it. So, there's the sense, reason, plan, and act."

AI agents—autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals—are no longer futuristic concepts. They're becoming operational realities across industries—even those with strict regulatory frameworks.

According to McKinsey's "The state of AI in 2023: Generative AI's breakout year" report, high-performing organizations in AI adoption are 3.6 times more likely than other organizations to have leaders who communicate a clear data and AI vision, 2.3 times more likely to have a data governance committee, and 2.5 times more likely to embed data literacy programs across the organization. This data underscores a critical reality: successful AI agent deployment in regulated industries requires a robust data foundation and governance framework.

Navigating the regulatory maze: Industry-specific challenges

Regulated industries face a complex web of compliance requirements that directly impact how AI agents can be deployed and operated. Let's examine the specific challenges across three key sectors where AI agents are making significant inroads despite regulatory constraints.

Banking and financial services

The financial sector faces stringent regulations like GDPR, PSD2, and Basel frameworks that directly impact AI deployments. Financial institutions must ensure AI agents comply with:

• Anti-money laundering (AML) requirements

• Know Your Customer (KYC) protocols

• Fair lending practices

• Explainability standards for automated decisions

JPMorgan Chase has deployed AI agents through its COIN (Contract Intelligence) platform, which reviews commercial loan agreements. To ensure compliance, the bank implemented comprehensive governance protocols, including human oversight mechanisms and detailed documentation of AI decision pathways. This implementation saved 360,000 hours of manual review work annually while maintaining regulatory compliance.

Healthcare

Healthcare organizations deploying AI agents must navigate HIPAA, FDA regulations, and patient data protection laws. Key requirements include:

• Patient data privacy and security

• Clinical validation of AI-driven decisions

• Audit trails for all automated actions

• Clear documentation of algorithmic logic

The Mayo Clinic has implemented AI agents for clinical decision support that help identify patients at risk for specific conditions. Their implementation includes rigorous validation protocols, comprehensive data governance frameworks, and continuous monitoring systems to ensure HIPAA compliance and clinical accuracy (Mayo Clinic Digital Health).

Insurance

Lemonade Insurance has deployed an AI agent named "Jim" to handle claims processing. The system can review claims, validate policy coverage, and even process payments for straightforward cases, reducing settlement times from weeks to seconds in some instances.

Their compliance approach includes:

• Regular bias testing and fairness audits

• Explainable AI documentation for regulatory review

• Integration with their "Policy 2.0" plain-language insurance contracts

• Clear customer communication about when AI vs. human agents handle claims

The essential role of data catalogs in AI agent deployment

A robust data catalog serves as the cornerstone for successful AI agent implementation in regulated industries. Organizations with a solid foundation of data management capabilities—including the use of catalogs that ensure data is governed and support next-order comprehension by organizing metadata—are substantially more likely to achieve AI success.

Four ways data catalogs enable compliant AI agents

1. Comprehensive data visibility

Data catalogs provide a complete inventory of an organization's data assets, making it possible to identify which data sources are appropriate for AI agent training and operation. This transparency is crucial for meeting regulatory requirements that mandate clear data lineage and usage tracking.

2. Metadata management

Metadata is the foundation of AI-ready data. Rich metadata enables AI agents to understand data context, sensitivity levels, and applicable regulations, ensuring they operate within compliance boundaries.

3. Data quality assurance

Regulated industries must ensure AI agents make decisions based on high-quality, accurate data. Data catalogs with built-in quality metrics and validation capabilities help organizations maintain the data integrity necessary for compliant AI operations.

4. Access control and policy enforcement

Modern data catalogs implement granular access controls and policy enforcement mechanisms that allow AI agents to operate with appropriate permissions while preventing unauthorized data access or processing.

The evolution of data catalogs for AI agent support

Data catalogs have evolved significantly to support AI agent deployment in regulated environments. As the platforms have evolved, we've moved from manual documentation to intelligent, AI-enhanced systems that actively facilitate compliant AI operations.

What makes data catalogs particularly valuable for AI agent development is their ability to provide critical context through rich metadata. To develop AI models that accurately grasp the meaning and importance of the data they analyze, it's critical to have such contextual insights offered by a data catalog

This contextual understanding is crucial for regulated industries, where AI agents must be able to:

• Differentiate between personally identifiable information (PII) and non-sensitive data

• Understand data freshness and reliability to make appropriate decisions

• Recognize the regulatory implications of specific data elements

• Access the right data lineage information to explain decisions when required

By integrating data catalog capabilities directly into AI development workflows, organizations can build inherently compliant and context-aware agents. This integration helps data scientists and AI engineers build models that respect regulatory boundaries from the start rather than retrofitting compliance as an afterthought.

Data governance framework for AI agents in regulated industries

Successful AI agent deployments in regulated industries require a comprehensive data governance framework that addresses:

Regulatory mapping and monitoring

Create a detailed mapping of regulatory requirements to specific data governance controls, and implement continuous monitoring to identify compliance gaps.

Ethical AI principles

Establish clear ethical guidelines for AI agent development and deployment, addressing fairness, transparency, accountability, and human oversight.

Documentation and auditability

Implement comprehensive documentation practices for AI models and agent actions, including version control, decision logs, and audit trails that satisfy regulatory requirements.

Data protection by design

Incorporate privacy-enhancing technologies and data minimization principles into AI agent architectures from the beginning of the development process.

Best practices for deploying AI agents in regulated industries

Based on successful implementations across regulated sectors, we recommend the following best practices:

1. Begin with a data governance assessment

Conduct a thorough assessment of your current data governance capabilities and identify gaps that must be addressed before AI agent deployment.

2. Implement a comprehensive data catalog

A robust data catalog is essential for creating the trusted data foundation needed for compliant AI agents. Deploy a modern data catalog solution that provides the visibility, metadata management, and governance capabilities necessary for regulated environments.

3. Develop clear AI governance policies

Create detailed policies specifying AI agent development, testing, deployment, and monitoring requirements, with explicit provisions for regulatory compliance.

4. Establish cross-functional oversight

Form a governance committee with representatives from data management, legal, compliance, IT, and business units to oversee AI agent initiatives.

5. Implement continuous compliance monitoring

Deploy automated monitoring tools that continuously assess AI agent activities against regulatory requirements and internal policies.

By implementing these best practices, organizations in regulated industries can accelerate their AI agent deployments while maintaining the trust of customers, regulators, and other stakeholders. The key is striking the right balance between innovation and compliance—leveraging AI's transformative potential while respecting regulatory boundaries.

The future of AI agents in regulated industries

As AI technology continues to advance, we anticipate several emerging trends in regulated industry deployments:

Regulatory-aware AI agents that automatically adjust their behavior based on applicable regulations and data governance policies

Embedded compliance checks that validate AI agent actions against regulatory requirements in real-time

Enhanced explainability tools that make AI agent decision processes transparent to regulators and stakeholders

Industry-specific AI governance frameworks tailored to the unique regulatory landscapes of banking, healthcare, insurance, and other regulated sectors

The future of AI agents in regulated industries will likely be characterized by a convergence of innovation and compliance. As regulatory frameworks evolve to accommodate AI technologies, and as AI systems become more adept at understanding and adhering to compliance requirements, we'll see increasingly sophisticated applications that deliver value while respecting important boundaries.

Conclusion

For data leaders in regulated industries, successfully deploying AI agents requires balancing innovation with compliance. A robust data catalog and comprehensive governance framework are essential foundations for this balance, enabling organizations to harness AI's transformative potential while meeting regulatory requirements.

By implementing the best practices outlined in this article and leveraging modern data catalog capabilities, organizations can accelerate their AI agent initiatives while maintaining the trust of customers, regulators, and stakeholders.

As Sanjeev Mohan notes, "These times may be head spinning, but they have never been so exciting in our entire career. This is the golden age of data." For regulated industries embracing AI agents, this golden age brings both unprecedented opportunities and significant responsibilities—navigating them successfully requires the right data foundation and governance approach.

See for yourself how a data catalog can help you build powerful, trusted AI agents. Book a demo with us today.