The Challenge
Like many personal injury law firms across the United States, this firm manages thousands of pages of medical records every month. These documents contain Protected Health Information (PHI), including patient names, addresses, insurance details, medical record numbers, physician notes, diagnostic reports, and treatment histories.
As generative AI became increasingly capable of summarizing medical records and extracting case insights, attorneys saw an opportunity to improve the way they reviewed complex documentation.
However, the firm’s leadership viewed AI adoption through a different lens.
Before allowing attorneys to use AI at scale, they needed confidence that sensitive client information would remain protected and that AI usage could be governed consistently across the organization.
The firm wanted to ensure its AI adoption strategy supported its broader obligations around protecting PHI under the HIPAA Privacy Rule and HIPAA Security Rule, while maintaining the oversight expected during client security reviews and internal compliance assessments.
The challenges quickly became apparent:
- Attorneys could unintentionally upload PHI into public AI tools.
- There was no centralized visibility into AI usage across the organization.
- Security teams couldn’t determine what sensitive information was being shared with AI models.
- AI interactions were not logged, making audits and investigations difficult.
- Existing DLP solutions relied on static pattern matching, generating excessive false positives while failing to understand document context.
- Blocking AI entirely would negatively impact innovation while encouraging unsanctioned use of consumer AI tools.
The firm needed a solution that would allow attorneys to leverage AI confidently without compromising governance, security, or client trust.
The Solution
The firm deployed Wald AI as a secure AI gateway between employees and leading AI models.
Instead of preventing attorneys from using AI, Wald enabled secure adoption by automatically enforcing security controls before information ever left the organization.
Every uploaded medical record and every user prompt passed through Wald’s Context Intelligence engine.
Rather than relying on keywords or predefined rules, Wald analyzed the context of documents and prompts to identify sensitive information such as patient identifiers, medical record numbers, insurance details, addresses, dates of birth, and other forms of PHI.
Sensitive information was automatically sanitized before requests reached the AI model, while preserving the surrounding context needed to generate accurate legal and medical summaries.
At the same time, Wald introduced enterprise-grade governance across every AI interaction.
Security and compliance teams gained centralized visibility into:
- Which users accessed AI
- Which AI models were used
- What sensitive information was detected
- What information was sanitized
- Which governance policies were applied
- Complete audit logs for every AI interaction
Instead of relying solely on employee awareness and manual processes, the firm established automated controls that supported responsible AI adoption while improving oversight of sensitive data.
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