Legal Tech Is Booming and with the Right Vetting, AI Can Become Every Lawyer’s Most Trusted Assistant

As AI becomes inseparable from modern legal work, firms that master safe adoption practices will set the new standard of trust.

As law firms race to adopt AI-powered drafting and compliance tools, experts warn that not all platforms are created equal and choosing the wrong one could expose sensitive client information. According to a 2025 Legal Industry Report by the American Bar Association, roughly one in three legal professionals now use generative AI in their daily work. Yet, a report found that law firms still lag far behind in-house legal departments when it comes to AI use. This is largely due to fears around confidentiality and compliance.

Herold Lawverra, founder and CEO of Lawverra, says the hesitation is understandable but it doesn’t have to hold firms back.

“AI can dramatically improve accuracy and efficiency in legal work, but it must be handled with the same duty of care lawyers owe their clients,” says Lawverra. “Security isn’t just a feature because it’s already a prerequisite.”

Despite rapid advances in legal tech, many firms remain caught between innovation and compliance. Generative AI platforms promise faster contract drafting, automated due diligence, and risk detection, but when poorly vetted, they can store or process client data in insecure environments, sometimes even training on confidential material.

The issue is both technical and ethical. The ABA Model Rules require lawyers to maintain client confidentiality and understand the technologies they use. Yet, few law firms have formalized AI review frameworks.

“Too often, lawyers assume any AI that claims to be secure must be trustworthy,” says Lawverra. “But unless the tool has clear data isolation policies, encryption standards, and transparent terms of service, it could be sharing data with third parties or worse, using it to train public models.”

How to Vet an AI Legal Tool Securely

1. Check where your data lives. Make sure the tool discloses its data storage and jurisdiction.
2. Look for encryption for both in transit and at rest. AES-256 encryption should be standard.
3. Ask if your data trains the model. If it does, your client data could become someone else’s dataset.
4. Demand a confidentiality clause. A legally binding NDA or DPA should be part of any vendor agreement.
5. Test with dummy data first. Never upload real client material during your trial phase.
6. Review compliance certifications. ISO 27001 or SOC 2 compliance is a good baseline.
7. Train your staff. Even the best AI can’t protect data if users mishandle it.

“When a client shares sensitive information, they expect absolute discretion, regardless of whether a human or machine touches the file,” says Lawverra. “We’re seeing firms take shortcuts out of curiosity or cost-cutting like using open-source AI tools without realizing they may be feeding confidential material into public datasets. Once that data is out, you can’t claw it back. That’s why structured vetting must be standard practice, just like conflict checks.”

As AI becomes inseparable from modern legal work, firms that master safe adoption practices will set the new standard of trust. For Herold Lawverra, the question is no longer if AI belongs in law—it’s how responsibly it’s used.