The insurance industry is facing a growing data crisis.
While carriers collect more information than ever before, they're simultaneously confronting escalating cyber risks, tightening regulatory scrutiny, and mounting compliance costs. If they can manage these issues, new technology like agentic AI is poised to transform how they do business, but where do they get started?
This is a fundamental risk management issue that strikes at the heart of what insurers do best. The companies that thrive in the next decade won't be those with the most data, but those with the most well-governed data. They'll be the carriers that understand one key truth: enterprise
Governance is the foundation
For insurers, an industry built on risk assessment and mitigation, the path forward is clear: govern first, then accelerate. Insurers sit on fragmented, legacy data; missing metadata and unclear ownership slow underwriting, complicate claims, and raise operational risk. The immediate priority has to be compliance and risk reduction to help guard against costly data breaches (
Uniform data governance lowers operational, regulatory, and cyber risk while creating the trusted data layer that AI requires to deliver meaningful business outcomes. Let's examine what "data governance" involves.
The five pillars of data governance for insurers
1. Data discovery: Modern insurers must maintain comprehensive visibility across their structured databases, unstructured documents, and legacy systems without costly migrations. The goal is to create a unified view across all systems.
2. Automated classification and policy enforcement: Manual data classification is unsustainable at enterprise scale; the task is too time-consuming, and the risk of human error too high. Automated PII detection and consistent tagging reduce human error while ensuring uniform policy application across diverse data sources.
3. Lifecycle management and data minimization: Once you know what you have, you can retain only the data that's needed and dispose of the rest. Removing redundant, obsolete, and trivial (ROT) data shrinks your attack surface, reduces storage costs, and simplifies compliance. This is about defensible, policy-driven retention that aligns with business and regulatory requirements.
4. Privacy, DSAR, and audit readiness: Modern privacy regulations mandate that organizations know who has what data, where it resides, and why it's being retained. Automated processes for data subject access requests (DSARs) and audit preparation make a once-chaotic process into a routine capability.
5. AI Governance: As AI models become central to business operations, tracking model and data lineage, implementing access controls, establishing usage policies, and maintaining ongoing monitoring becomes essential for both performance and compliance.
With the foundation set, AI can act as a growth engine
Once enterprise data governance is established, AI becomes a growth driver across core insurance functions. AI can unlock growth for insurers by freeing teams up to focus on higher value tasks, automating routine work and surfacing insights, with guardrails that keep data safe.
Generative AI's abilities in pattern-matching and unstructured data analysis can streamline and simplify processes like submissions triage and fraud detection, and advances in agentic AI allow for complex workflows that can augment aspects of customer outreach and self-service capabilities.
Well‑governed data is the scaffolding for growth in insurance: trusted inputs let people and AI move faster with trust, confidence, and control.
This is a whole-of-enterprise journey
Those executives and boards pushing AI as a cure-all need to understand that the transformative potential of the technology depends on the data underpinning it.
Without good data – without good data governance – you cannot have good AI. The insurance industry's digital future isn't about choosing between risk management and innovation — it's about recognizing that governed data enables both.
