In today’s data-driven world, protecting Personally Identifiable Information (PII) and Protected Health Information (PHI) is more than just a compliance requirement — it is a fundamental business responsibility. As organizations handle larger volumes of increasingly complex data, relying on manual processes to manage sensitive information simply is not sustainable.
Data breaches continue to rise, and the risk of exposing PII or PHI has become a top concern for legal, compliance, and privacy stakeholders. If sensitive data is mishandled or improperly disclosed, the consequences can include regulatory penalties, litigation, operational disruption, and long-term loss of public trust. As a result, many organizations are seeking to modernize their data protection practices using more intelligent, scalable tools.
To address this challenge, artificial intelligence (AI) solutions are being adopted to improve how sensitive information is identified, classified, and reviewed across large datasets. These technologies allow organizations to move from reactive, manual processes to proactive, automated workflows that support speed, accuracy, and compliance.
Proactive Risk Management Through AI Solutions
Traditional data governance methods often depend on manual review, which becomes inefficient and error-prone as data grows. AI-powered platforms offer a scalable alternative by continuously scanning structured and unstructured data sources — such as emails, file shares, and cloud repositories — for sensitive content.
AI solutions leverage advanced techniques like Natural Language Processing (NLP), machine learning, and entity recognition to detect sensitive information with greater precision. These tools help organizations identify high-risk content early, reduce human oversight burdens, and respond to regulatory or legal requests with improved confidence and speed. In legal and compliance settings, AI can also assist with redaction and privilege review, ensuring confidential information is properly protected during litigation, audits, or investigations.
Smarter Data Classification
One of the core strengths of modern AI solutions is their ability to classify data based on context, language, and metadata. Unlike keyword-based systems, AI models learn from real-world patterns, improving over time through continuous feedback.
In practice, organizations have reported over 90% precision in PII/PHI detection, along with 10 times more improvements in identifying relevant materials compared to traditional methods. These gains reduce time spent reviewing irrelevant documents and help teams focus on higher-value decision-making.
The Future of PII Reviews: Investing in AI Solutions
As privacy laws such as the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and the California Privacy Rights Act (CPRA) evolve, AI provides a scalable way to meet compliance expectations while reducing operational strain. Tasks like data redaction, classification, access auditing, and retention tracking can be automated to support both regulatory adherence and internal efficiency.
AI also plays a growing role in supporting Data Subject Access Requests (DSARs), where individuals request access to their personal data. By automating the search and extraction of relevant records, AI helps organizations meet legal deadlines and reduce the burden on legal and privacy teams.
In one high-volume use case, a healthcare provider used AI to reduce 3.8 million potentially sensitive documents to 20,000 for targeted review. Another enterprise saw document review time drop by 85%, enabling faster breach response and improved resource allocation.
What AI Solutions Can Identify in PII Review
Modern AI solutions are capable of identifying a wide range of sensitive data types with a high level of accuracy. These typically include, but are not limited to:
- Social Security Numbers (SSNs)
- Medical records and insurance IDs
- Passport and driver’s license numbers
- Email addresses and phone numbers
- Financial account and payment card details
- Health records and diagnostic data
- Government-issued identifiers
AI solutions go beyond basic pattern matching by interpreting the context in which data appears. This helps reduce false positives, avoid overlooked records, and support more reliable data protection across the enterprise.
Manual vs. AI-Driven Review Comparison
Feature | Manual Review | AI-Driven Review |
|---|---|---|
| Accuracy | Moderate (human error risk) | High (up to over 90% precision) |
| Speed | Time-intensive | Real-time or near real-time |
| Scalability | Limited by human capacity | Scales to millions of records |
| Cost Efficiency | High manual effort | Reduced effort through automation |
| Consistency | Varies by reviewer | Standardized and repeatable output |
Technical Integration with Existing Workflows
A key advantage of AI solutions is their ability to integrate into existing enterprise tools and legal workflows. Whether supporting litigation, compliance audits, or breach response, AI can be embedded into document review, e-discovery, and DLP systems. Many AI platforms offer direct integrations with tools, such as Relativity — enabling faster document processing, targeted redaction, and privilege identification. Integration with identity management systems also helps enforce access controls and maintain audit trails.
Ethical AI Governance and Adoption
Implementing AI for PII/PHI review requires thoughtful governance. Organizations should ensure transparency in how models operate, monitor for potential bias, and implement ongoing performance audits. AI should complement — not replace — human expertise, and its use must align with ethical and regulatory frameworks. Clear documentation, controls, and cross-functional collaboration (e.g., between information technology (IT), legal, and compliance teams) are essential to maintaining trust and accountability in AI-supported processes.
Getting Started: Practical Implementation Guidance
To adopt AI for PII/PHI review effectively, organizations should start with clear, manageable objectives. A suggested approach includes:
- Begin with a pilot in a low-risk, high-volume workflow (e.g., internal data reviews or policy documents).
- Define success metrics such as reduced review time, improved classification accuracy, or fewer false positives.
- Select an experienced AI solution provider that offers robust support, security alignment, and integration options.
- Evaluate results and refine the model before scaling to higher-risk use cases like litigation support or breach response.
This phased approach allows teams to build internal confidence, validate performance, and align AI adoption with strategic privacy and compliance goals.
Conclusion: Turning Strategy into Practice
AI solutions are no longer experimental — they are proven tools for managing the complexity of modern data protection. When thoughtfully deployed, AI can dramatically improve how PII and PHI are identified, reviewed, and secured. But technology alone is not enough. True success requires a comprehensive strategy that includes governance, policy alignment, and collaboration across legal, privacy, and IT teams. For organizations committed to safeguarding sensitive data, now is the time to invest in AI solutions that drive both efficiency and compliance.
To move from planning to execution, organizations should consider the following next steps:
- Conduct data-mapping exercises to understand where sensitive information resides across systems and workflows.
- Adopt privacy-by-design frameworks to embed data protection into business processes and technology decisions.
- Train legal, compliance, and technical teams on the capabilities and limitations of AI to ensure responsible and effective use.
By taking these actions, organizations can strengthen their data governance posture, reduce regulatory risk, and build long-term trust with stakeholders.
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© Copyright 2025. The views expressed herein are those of the author(s) and not necessarily the views of Ankura Consulting Group, LLC., its management, its subsidiaries, its affiliates, or its other professionals. Ankura is not a law firm and cannot provide legal advice.
