Today's healthcare and insurance organizations manage vast amounts of highly sensitive data across an increasingly complex ecosystem. Clinical providers, insurers, and partners rely on shared data from diverse sources—including electronic medical records, imaging systems, and billing platforms.
However, many lack a clear understanding of where this data resides, who owns it, and how it's accessed. Without this foundation, they cannot confidently classify or protect sensitive information—leaving them vulnerable to compliance violations, regulatory fines, and legal risks. This lack of visibility also affects critical operations. In insurance workflows such as underwriting, decisions may be based on incomplete or inaccurate data, increasing risk—especially in high-stakes scenarios like mergers and acquisitions, where evaluating the data security posture is crucial.
These challenges are intensified by outdated, fragmented environments that are difficult to integrate and modernize. Sensitive data is scattered across disconnected systems and formats, leading to duplication, inconsistency, and reduced visibility. Meanwhile, excessive permissions remain a constant risk, increasing the likelihood of misuse, insider threats, and accidental exposure.
As organizations accelerate AI adoption, including generative AI, to enhance clinical and operational efficiency, they introduce a powerful new capability alongside a significant increase in risk. When sensitive data is used without proper governance and controls, exposure can grow quickly and unpredictably.
Operationalizing data security has long been a challenge. Despite significant investments, many organizations still lack complete visibility. Traditional tools that rely on regex, trainable classifiers, and other pattern-based methods identify only a small portion of sensitive data and often overwhelm teams with false positives.
The good news is that modern data security governance platforms have moved beyond these limitations. Healthcare and insurance organizations should seek solutions that leverage context-aware AI for discovery, risk monitoring, and remediation—delivering outcomes such as:
Gain better visibility into data: To effectively protect sensitive information, providers first need to understand exactly what data they possess, where it is stored, who accesses it, and how it is shared.
Context-aware AI scans each data record thoroughly and can identify not only personally identifiable information (PII), protected health information (PHI), and payment card information (PCI), but also detect other important business records that other tools might overlook. It also recognizes duplicate or near-identical data and determines the category and subcategory for each record. For instance, it differentiates between a HIPAA authorization and a workers' compensation document. This detailed level of information helps security teams make smarter decisions when assigning classification labels, choosing where data should be stored, or setting access and retention rules.
Prevent sensitive data leaks: Security teams must ensure that employees and third-party contractors do not access data they shouldn't and verify that authorized users do not share it. They need a solution that enables them to contextually discover, monitor, and protect their sensitive data—not only at rest but also in transit—to prevent it from being shared with unauthorized users, personal email addresses, file-sharing applications, social media, or GenAI tools.
Enable GenAI without expanding the attack surface: Generative artificial intelligence (GenAI) is reshaping our world in real time. Tools like Microsoft Copilot, ChatGPT, Perplexity, and Claude are changing the way we make decisions, solve problems, create content, and interact both at work and at home. While they offer greater operational efficiency, better decision-making, and lower costs, they also introduce significant data security risks for insurers.
Providers need a solution that helps them detect when employees use unsanctioned or "shadow AI," so they can maintain control and protect their data. They also need to ensure that, no matter where data is stored, it is accessed by the correct identities, at the appropriate times, and for the intended purposes. A comprehensive data security and governance solution will allow them to set guardrails on which data should be blocked or redacted by groups and for each GenAI application, and help them curate data when training their own proprietary GenAI models.
Excel in regulatory compliance audits: Regulatory frameworks help healthcare and insurance companies reduce risks, implement processes, and maintain customer trust. However, mapping security controls to these frameworks can quickly become overwhelming. An additional challenge is that different regions may have vastly different data handling and classification requirements.
Organizations need a clear overview of their compliance status, tools to resolve issues, and peace of mind that they aren't one audit away from disaster. They should seek a solution that offers a dashboard displaying their current compliance status across all relevant regulations and security controls, as well as support for custom frameworks. Additionally, they require granular visibility into all data records that violate compliance, with the ability to remediate them directly within the platform.
Improve the effectiveness of current security tools: Tools like zero trust network access (ZTNA) and cloud access security broker (CASB) don't analyze data to determine whether to allow or block access. Instead, they enforce policies based on labels, so if those labels are wrong or missing, they could either expose sensitive information to unauthorized users or prevent access necessary for productivity. Context-aware AI and autonomous classification help ensure that sensitive data is labeled correctly and remains accessible only to authorized individuals.
Experience faster ROI, smarter policies, and less stress: Context-aware AI significantly accelerates the data discovery process and saves countless hours that administrators used to spend on tuning algorithms and chasing false positives. However, since new data is constantly generated and continually changing, capturing only a snapshot at a single point in time is insufficient.
Security teams can save time and enhance data protection by implementing a solution that continuously monitors data, flags risks, and automates remediation steps. Picking a provider that offers managed services can also reduce the workload on overstretched security teams by providing data security experts to assist with tasks such as deploying the platform and training their teams on it, building a data governance roadmap, mapping classification labels, and reporting on and tracking progress toward their goals.
