Artificial Intelligence is no longer an experiment in the legal industry—it’s here, and it’s changing how we serve clients every day. At the same time, it challenges us to safeguard a cornerstone of our profession: confidentiality.
As an innovation leader working alongside law firms and corporate legal departments, I’ve seen firsthand how enthusiasm for AI often meets resistance when questions of client data protection arise. Below, I share lessons learned and offer a roadmap for responsible AI adoption.
The legal sector has always balanced innovation with responsibility. Cloud storage, predictive coding, and collaborative platforms each raised concerns before becoming accepted practice. AI is the latest—and perhaps the most disruptive—chapter in this ongoing story.
AI Adoption: Beyond the Pilot Stage
Across the industry, law firms and corporate legal departments are embedding generative and machine learning ai into daily workflows.
- Contract Analysis: Corporate legal teams now rely on AI to extract key terms (dates, obligations, indemnity provisions, etc) and compare proposed contracts against corporate standards. Handling such analysis for thousands of agreements in a fraction of the time paralegals once needed.
- Litigation Support: Law firms are using generative AI to identify relevant documents, surface hot documents, identify privileged and sensitive content, and draft issue summaries during review.
- Legal Research and Drafting: AI Tools allow lawyers to generate memos or check case law in conversational style, reducing research time dramatically.
Law schools and continuing legal education providers are responding by integrating AI literacy into curricula. The Financial Times recently reported that top law schools are requiring students to learn how to use—and critically evaluate—AI tools before graduation. Bar associations from California to New York are publishing guidance on competence and confidentiality when using generative AI.
Adoption is spreading quickly. So are questions about whether we can use these systems without compromising client trust.
Confidentiality: The Central Barrier
In nearly every conversation we have introducing these technologies, confidentiality ranks as the leading concern. We see this born out in survey after survey. The American Bar Association (ABA) underscored this in its 2024 guidance, warning that lawyers must understand the technology’s risks and take “reasonable steps” to prevent disclosure of client information.
Two risks dominate conversations:
- Data Retention in Public Models – Consumer-facing AI platforms may log, or reuse prompts to train their models. Even anonymized text can be sensitive if it reveals strategy or unique client details.
- Accuracy and “Hallucinations” – Generative AI can invent citations, misstate facts, or include phantom authorities. The first sanctions for relying on such hallucinations came in 2023. Unbelievably, the professional laziness (and the corresponding sanctions) continues in 2025.
Both risks tie back to confidentiality. A system that mishandles data, or outputs fabricated content, places both client trust and attorney credibility at risk.
A Checklist for Responsible AI Use
To manage these risks, a set of best practices has emerged. These recommendations draw from bar opinions, case law, and best practices from our clients approaching the issue.
1. Segregate Workflows and Data
Confidential workflows and data should be isolated from public AI platforms. If client data must be used, it should run through secure, private, or in-house systems where retention policies are transparent.
Some firms are deploying “sandboxed” generative AI tools inside their own firewalls. This keeps prompts and outputs entirely within the firm’s environment, eliminating concerns about cross-user data exposure.
Even “anonymized” data may contain enough information to, due to AI’s sophisticated bulk analysis capabilities, unwittingly break confidentiality.
Bottom line: Do NOT put client data into any AI tool without a clear understanding of where that data goes, how long it resides there, and whether or not it becomes training content for future models.
2. Document Policies
Written policies reduce risk and create consistency. A sophisticated approach includes creation and maintenance of AI “playbooks” that cover:
- What categories of data may never be entered
- Required levels of review and sign-off (internal and client)
Firms and providers lacking such documentation face greater exposure to malpractice or disciplinary action. Clear policies make it easier for case teams and staff to innovate without stepping outside ethical boundaries.
3. Train Teams on AI Risk Awareness
AI literacy is quickly becoming a core professional skill. Training should cover, as applicable to each situation:
- The possibility of hallucinations or fabricated citations
- Bias in training datasets
- Legal or professional duty to verify and supervise output
The ABA’s 2024 opinion highlighted competence as a moving target: lawyers must understand enough about AI to use it responsibly. In practical terms, this means firms and providers need regular training sessions—similar to cybersecurity refreshers—to keep staff aware of both capabilities and risks.
4. Monitor Case Law and Regulation
The legal landscape around AI is evolving almost monthly. Consider these developments:
- Case Law: Globally, courts have already sanctioned or criticized lawyers for improper AI use.
- Ethics Opinions: State bars in California, Florida, and New York have issued or are drafting opinions that address competence, confidentiality, and supervision.
- Regulation: The European Union’s AI Act, for example, passed in 2024, begins phased enforcement in 2025. It requires transparency, classification of AI systems by risk, and specific obligations for “high-risk” uses.
Firms that assign responsibility for monitoring developments—often through a compliance or knowledge management lead—are better positioned to adapt quickly.
5. Balance Innovation with Ethics
Efficiency gains from AI are real. Deloitte reported in 2024 that general counsel anticipate up to 40% cost savings from adopting generative AI in certain workflows.
At Purpose, our clients are already seeing this savings.
But the true measure of success is not speed alone. Responsible adoption means achieving those gains while safeguarding privilege, ensuring accuracy, and maintaining client trust.
The duty of confidentiality remains absolute. The technology must serve lawyers, not replace their judgment.
Looking Ahead
AI will continue to reshape legal practice. The question is no longer whether the technology works—it does. The question is whether firms and corporate legal departments can integrate AI responsibly, without eroding the foundation of attorney-client trust.
The roadmap is clear:
- Segregate confidential workflows and data
- Balance innovation with professional responsibility
By following these principles, legal professionals can embrace AI’s potential while preserving what clients value most: discretion, accuracy, and trust.
Final Thoughts
AI adoption is accelerating across the profession. The firms and departments that thrive will not simply adopt these tools—they will adopt them with care.
Confidentiality is not an obstacle. It is the compass guiding responsible innovation.
