The insurance industry is at a crossroads. Artificial intelligence (AI) is revolutionizing underwriting accuracy, accelerating claims processing, and enhancing customer service delivery. Yet for insurance technology leaders, the promise of AI comes with a critical obligation: ensuring every deployment is built around robust governance to protect business operations and customer trust.
Before an AI solution enters production, organizations must establish comprehensive governance frameworks that address risk management, regulatory compliance, and ethical considerations. These elements enable confident AI deployment at scale. Without effective governance foundations, even the most sophisticated models can expose organizations to significant operational, legal, and reputational risks.
The Case for AI Governance Committees for Organizational Alignment
Many insurance organizations establish AI governance committees as the cornerstone of their AI strategy.
While these committees are typically established by senior business leadership, CIOs play a crucial leadership role as key stakeholders who bring essential technical expertise and operational insight to the governance process. They anticipate and respond to challenges that can make or break AI deployments – ensuring that AI initiatives align with business objectives, regulatory requirements, ethical standards, technical capabilities, infrastructure requirements, security considerations, and integration challenges.
An effective committee brings diverse perspectives from underwriting, claims management, legal, compliance, IT operations, and executive leadership. This composition ensures that AI deployments consider technical feasibility and business impact across all critical functions. CIOs are crucial for bridging the gap between insurance AI capabilities and practical implementation requirements.
The committee's core responsibilities extend far beyond simple oversight. They lead in developing comprehensive risk management frameworks, establishing data governance standards, evaluating AI projects for compliance, and creating policies that align with industry regulations and company values. Most critically, they serve as the authoritative body for use case evaluation, determining whether AI can be deployed to a use case and where it should be deployed to maximize ROI responsibly.
Navigating the Regulatory Landscape
Insurance CIOs are often the point person – or manage that person – for compliance with a complex insurance regulatory environment that spans multiple jurisdictions.
These cover federal requirements, like HIPAA, which applies to any AI system handling health information, to the New York Department of Financial Services Cybersecurity Regulation (23 NYCRR Part 500), which mandates specific cybersecurity requirements for financial services companies, including insurers.
They also cover data privacy regulations, which add additional layers of complexity. The California Consumer Privacy Act (CCPA) governs the collection and use of personal information, while the EU's General Data Protection Regulation (GDPR) affects any insurer handling EU citizens' data.
Perhaps most significantly, the EU Artificial Intelligence Act (EU AI Act) – which took effect in early 2024 established the world's first comprehensive legal framework for AI systems, focusing on risk management, AI model transparency, and ethical AI practices.
Technology leaders play a critical role in overseeing the phased implementation of the Act:
- August 1, 2024: The Act officially entered into force.
- February 2, 2025: Prohibitions on unacceptable AI practices became effective.
- August 2, 2025: Rules for general-purpose AI (GPAI) models take effect for new models.
- August 2, 2026: Rules for high-risk AI systems take effect.
- August 2, 2027: Full application to all risk categories.
Technology/information leaders are responsible for enabling internal governance frameworks to ensure compliance with present regulations and emerging standards without disrupting production systems. Strict regulatory compliance builds trust, reduces the potential for financial penalties, and positions your business as a leader by establishing the highest ethical standards for AI implementation and operation.
What CIOs Should Expect from Vendors
Building AI systems in-house is beyond the reach of all but a few companies. Insurance CIOs need a systematic approach to evaluating AI vendors that goes beyond technical capabilities. The governance committee should establish a three-part evaluation process that examines vendor claims, demonstrates real-world performance, and validates security protocols.
Direct vendor engagement
Confirm that all submitted information accurately reflects the technology's capabilities and compliance with regulatory standards. This includes detailed discussions about how the AI makes decisions, what data it requires, and how it integrates with existing systems.
Schedule live demonstrations
Showcase the technology in production-like environments, accompanied by performance data that validates the AI's capability to operate reliably under real-world conditions. This step reveals potential integration challenges and helps assess whether the solution delivers its promises.
Perform comprehensive security due diligence
Evaluate vendor protocols for data storage, encryption, and model training data sources. Vendors should complete detailed information security questionnaires and demonstrate compliance with relevant industry standards.
Enterprise-Grade Security and Compliance Standards
Leading AI vendors recognize that governance isn't an afterthought – at Roots, we view this as a fundamental design principle. CIOs should expect nothing less than comprehensive security and compliance frameworks that address the specific needs of insurance operations.
At minimum, AI vendors should meet the following criteria:
- Security Certifications: Maintain current SOC 2 Type II and ISO 27001 certifications, verified through annual independent audits to ensure ongoing compliance with critical data protection controls.
- Regulatory Compliance: Demonstrate adherence to all relevant regulations, including those governing personal financial information, protected health information, and personally identifiable information (PII), to ensure comprehensive data protection. This is increasingly important as nearly 30 US states have adopted the NAIC Model Bulletin or similar frameworks governing insurers’ use of AI, emphasizing accountability, transparency, and governance.
- Modern Technical Architecture: Deliver enterprise-grade AI solutions built on leading cloud platforms such as Microsoft Azure or Amazon AWS – eliminating the need for on-premises infrastructure while maximizing uptime through geographically distributed data centers.
- Data Encryption: Employ end-to-end, Department of Defense (DOD)-level encryption for data in transit and at rest, with additional HTTPS encryption for all platform communications.
- Ethical AI Framework: Operate under clear, enforceable principles that ensure explainability, safeguard data privacy, and drive continuous model improvement. This framework directly addresses the transparency requirements that insurance regulators increasingly demand, while supporting the bias-free decision-making essential for fair claims adjudication and underwriting.
Insurance domain-specific vendors like Roots exemplify these standards, demonstrating how comprehensive governance can be built into AI solutions from the ground up – and why that governance is no longer optional as regulatory scrutiny accelerates nationwide.
Best Practices for Production Deployment
Successful AI governance requires ongoing vigilance beyond initial deployment. Insurance CIOs should establish continuous monitoring frameworks to:
- Track AI performance
- Identify potential bias in decision-making
- Ensure ongoing compliance with evolving regulations
Documentation is critical for a tightly regulated industry like insurance. This documentation serves multiple purposes – supporting regulatory examinations, enabling model audits, and providing the transparency needed for explainable AI requirements.
Every AI system should maintain detailed training data records, model development processes, validation procedures, and performance metrics to meet these needs.
Regular governance committee meetings should review deployed AI systems, assess new use cases, and evaluate changes in regulatory requirements. The committee should also maintain a knowledge base of privacy regulations, emerging AI trends, and industry best practices to ensure the organization stays ahead of compliance requirements.
The Strategic Imperative
AI governance isn't just about risk mitigation or checking off some boxes – it's about enabling innovation in a safe and secure environment. Insurance companies that establish robust governance frameworks can deploy AI solutions more confidently, scale implementations more rapidly, and realize greater business value from their technology investments.
The insurance industry's reputation depends on customer trust. By prioritizing transparency, accountability, and ethical AI practices, CIOs can ensure that AI deployment strengthens rather than compromises this fundamental relationship.
As AI continues transforming insurance operations, the organizations that succeed will be those that view governance not as a constraint but as a competitive advantage. The framework you establish today will determine not just compliance but also the speed and scale at which your organization can embrace AI's transformative potential.
The time for preparation is now. Whether you build an AI system or work with a trusted insurance AI partner, ensure your governance framework is ready to support your business objectives and customers' trust in your organization.
Curious how insurers are putting Roots to work? Check out our case studies to see insurance-specific AI in action.