Responsible AI in Legal Practice: Managing Accuracy, Confidentiality, and Ethical Risk

Legal teams face two problems with AI at the same time. The technology is moving fast, and the conversation around it is moving faster.

Most of what gets published swings between breathless enthusiasm and existential alarm. Neither framing helps legal professionals who need to make sound decisions about specific tools in specific workflows with real clients and real consequences, whether they work in a law firm, a corporate legal department, an alternative legal service provider, or a legal technology company.

The actual challenge is not whether to use AI. Legal teams are already using it, often embedded in contract platforms, research tools, and workflow systems they rely on daily. The challenge is how to use it responsibly given specific, well-documented risks.

Three concerns consistently surface across bar guidance, ethics opinions, and experience using these tools: accuracy and hallucinations, confidentiality and data security, and ethics, accountability, and bias. These are not theoretical. They have already produced court sanctions, ethics complaints, and client harm.

This article explains what each risk involves, where current professional guidance stands, and what responsible adoption looks like in practice.

How Generative AI Actually Works

Most concerns about AI in legal work trace back to a single mechanical reality. Generative AI systems produce content by predicting likely continuations based on patterns learned from vast amounts of data. They do not understand meaning, verify facts, or reason from first principles. They estimate what plausible output looks like, and they are remarkably good at it.

This distinction explains nearly every concern that follows. Accuracy problems arise because the system optimizes for plausibility, not truth. Confidentiality exposure arises because data must travel to the system for processing.

Accountability questions arise because the output looks authoritative even when it is wrong. Understanding these mechanics is not about becoming a technologist. It is about knowing enough to ask the right questions and recognize when something requires closer scrutiny.

Accuracy, Reliability, and Hallucinations

Generative models do not retrieve information the way a search engine queries a database.

They generate responses by predicting what is most likely to come next. When they encounter gaps in their training data or ambiguous questions, they do not pause or flag uncertainty. They generate plausible output anyway.

This is hallucination: confident, well-structured content that is factually wrong. In legal work, it manifests as fabricated case citations, misstated holdings, invented statutory provisions, incorrect regulatory references, and overstated factual claims.

The term "hallucination" can be misleading because it implies something unusual. In practice, it is an inherent characteristic of how these systems operate. Every output is a prediction. Some predictions are accurate. Others are not. The system itself cannot tell the difference.

The reliability problem compounds the accuracy problem. The same prompt can produce different outputs on different occasions. A contract clause that an AI drafts correctly on Monday may contain a material error on Tuesday. Past accuracy is not a reliable indicator of future performance, which makes spot-checking an inadequate substitute for systematic verification.

Several well-publicized court sanctions have resulted from lawyers filing briefs containing AI-generated citations to cases that do not exist. But the accuracy risk extends well beyond litigation. In-house teams relying on AI-drafted contract language may miss nonstandard terms. Compliance professionals using AI to interpret regulatory requirements may act on incorrect guidance. Legal operations teams automating intake or triage with AI may misroute matters based on flawed analysis.

What makes this particularly dangerous is the quality of the output. Hallucinated content often reads as entirely credible. The citation format is correct. The reasoning sounds coherent. Catching errors requires the same verification effort as checking accurate work, which means efficiency gains can evaporate if verification is not built into the workflow from the start.

Courts are responding with certification requirements for AI-assisted filings. Clients are asking how their legal service providers use AI and what quality controls are in place.

Where Professional Guidance Stands

Multiple bar associations now treat AI output the way legal organizations have always treated work from a junior professional: useful for drafts and initial research, but requiring independent verification before use.

ABA Formal Opinion 512, along with emerging state-level guidance, makes the obligation explicit: lawyers bear full responsibility for accuracy regardless of whether AI generated the content. The duty of competence now extends to understanding how these systems function and where they are likely to fail. For organizations providing legal services, this means building institutional competence, not just individual awareness.

What Responsible Practice Looks Like

Treat every AI output as a draft. This is not a hedge. It is the operational reality of working with systems that optimize for plausibility rather than correctness.

Build verification into the workflow at the point of creation, not as an afterthought. Require independent confirmation of citations, factual assertions, and legal conclusions before client delivery, whether that takes the form of a court filing, a contract, a compliance memo, or a matter recommendation.

Use AI for what it does well: organizing information, producing first passes, and accelerating early-stage work. Apply human judgment where it is indispensable: verifying accuracy, assessing risk, and deciding what to trust.

Confidentiality and Data Security

When legal professionals input client information into AI systems, that data travels to third-party infrastructure. Depending on the vendor's architecture, client facts, drafts, and documents may be processed on external servers, stored in system logs, used to train or improve models, or accessible to vendor employees and subprocessors.

This creates a confidentiality exposure that most legal professionals would never accept in other contexts. Pasting a sensitive fact pattern into a consumer AI tool is functionally equivalent to emailing it to an unknown third party with no confidentiality agreement in place. Yet this happens routinely because the interface feels private even when the infrastructure is not.

Some systems retain conversation history. Others use inputs to improve model performance, meaning client data could influence outputs generated for other users. For in-house teams, ALSPs, and legal technology providers handling data from multiple clients or business units, the cross-contamination risk is particularly acute.

The duty of confidentiality encompasses reasonable measures to prevent inadvertent exposure, not just intentional disclosure. Using AI tools without understanding their data handling practices can breach this duty even when no one intended to share anything.

The risk scales with sensitivity. Regulated data carries heightened obligations. Attorney-client privileged communications require particular care because privilege can be waived through disclosure to third parties. Deal-related information demands strict controls because premature disclosure can have material consequences. Organizations handling data subject to HIPAA, GDPR, or sector-specific regulations face additional compliance layers that general-purpose AI tools do not account for.

Where Professional Guidance Stands

Ethics guidance now frames vendor due diligence for AI tools as part of the duty of confidentiality and competence. Lawyers and legal organizations must understand, before using a tool, where data goes, who can access it, how long it is retained, and whether deletion rights exist.

The key questions:

1) Does the vendor use inputs to train models? Are there data localization requirements?

2) What subprocessors handle the data?

3) Can the organization audit data handling practices?

4) What happens to data after the engagement ends?

These are not optional inquiries. They are part of the professional obligation to protect client information.

What Responsible Practice Looks Like

Conduct vendor security assessments before deploying AI tools in legal workflows. If a vendor cannot answer basic questions about data handling, retention, and deletion, that tells you something important.

Establish clear policies governing what types of information can be entered into which tools. A tool approved for general research may not be appropriate for drafting involving specific client facts. A consumer-grade system should never touch privileged communications. Make these distinctions explicit and communicate them to everyone with access to your systems, including contractors and service providers.

For sensitive or regulated workflows, consider enterprise deployments where the organization controls the infrastructure. Train teams on data hygiene: strip identifying details when possible and understand the difference between consumer and enterprise AI environments.

Ethics, Accountability, and Bias

Two distinct concerns converge here.

The first is bias. AI systems trained on historical data can embed patterns reflecting existing inequities. In legal contexts, this matters for case outcome prediction, risk scoring, candidate screening, litigation strategy, and settlement valuation. A model that learned from biased data will produce biased outputs, and those outputs can drive decisions with real consequences for real people. For organizations serving diverse client populations or operating across jurisdictions, the stakes are compounded.

The second is accountability. When AI generates a legal analysis, the responsible party under current guidance is the lawyer or supervising professional. But AI systems can make it easy to defer judgment without realizing it. The output looks polished, arrives quickly, and the temptation to forward it without careful review is real, particularly in high-volume environments where speed is a competitive advantage.

These concerns are connected. Biased outputs that go unreviewed create both the harm and the accountability gap simultaneously.

Professional guidance draws a clear distinction between AI as a tool that assists legal professionals and AI as a system that effectively provides legal analysis without adequate oversight. When a professional reviews AI output, evaluates its reasoning, and exercises independent judgment, the system functions as a tool. When someone forwards AI output without meaningful review, the system is functioning as the advisor.

This line becomes harder to maintain with agentic AI systems that take actions across connected platforms. An agent that drafts a response, routes a matter, and updates a record creates multiple decision points where oversight can lapse if checkpoints are not designed in advance. The speed and autonomy that make agents valuable are the same characteristics that make governance essential.

There is also the question of unauthorized practice. When AI systems provide legal analysis directly to non-lawyers without meaningful attorney oversight, they may cross from permissible automation into territory that raises unauthorized practice concerns. Organizations deploying AI in client-facing or self-service contexts need to think carefully about where this boundary falls and design accordingly.

Where Professional Guidance Stands

ABA Formal Opinion 512, along with state-level guidance, emphasizes that lawyers remain fully responsible for AI-assisted work. The duty of supervision applies to AI systems the same way it applies to non-lawyer staff.

Emerging guidance addresses bias directly. Legal professionals and organizations that use AI tools producing biased or discriminatory outputs may face liability for downstream consequences, whether in litigation strategy or operational decisions like hiring and vendor selection.

The standard is not perfection. It is diligence: understanding the tools, mitigating bias, and maintaining substantive oversight.

What Responsible Practice Looks Like

Maintain meaningful human review at every material decision point. Meaningful review means engaging with substance, not skimming formatting.

Test AI tools for bias before deploying them in sensitive contexts. If a tool is scoring risk or screening candidates, validate its outputs against diverse scenarios. If you cannot evaluate a tool's outputs for bias, reconsider using it for that purpose.

Document AI use in your workflows. Clear records of human review and professional judgment protect both the client and the organization.

Documentation also creates accountability, which is the point. Define which tasks are appropriate for AI assistance and which require unaided professional judgment.

Putting It Together

The three concerns are interconnected in practice. Accuracy failures create accountability exposure. Confidentiality lapses erode client trust. Biased outputs generate liability. A responsible AI strategy addresses all three together.

• Evaluate before adopting any tool. Assess accuracy characteristics, data handling practices, and potential for biased outputs. Ask vendors specific questions and expect specific answers.

• Design workflows with verification checkpoints, data handling controls, and clear accountability at each step. Build oversight in from the start, not after deployment.

• Monitor outcomes, not just usage. Track error rates. Audit for bias periodically. Governance is ongoing, not a one-time implementation.

• Train everyone who uses AI tools on the risks, the organization's policies, and their professional responsibilities. Training is not a formality. It is how organizations close the gap between policy and practice.

Judgment Is the Point

AI changes what legal teams can accomplish. It accelerates drafting, organizes information, and compresses the time between question and first answer. These are real benefits. Responsible teams should capture them.

But the value of legal work has never been speed alone. It is judgment: knowing what matters, what to trust, what to question, what to advise. AI systems cannot do this. They can make the work surrounding judgment more efficient, freeing professionals for the decisions that require expertise.

The concerns about accuracy, confidentiality, and accountability are not reasons to avoid AI. They are reasons to adopt it deliberately, with clear processes, genuine oversight, and an honest understanding of what these systems can and cannot do.

AI should extend judgment, not replace it.