Law firms and real-estate organizations are adopting generative AI at a rapid pace. Attorneys use it to summarize documents, draft correspondence, and support research. Brokers and property managers use it for listing descriptions, contract review support, tenant communication templates, and operational workflows.
The challenge is that AI adoption has outpaced data governance. Many firms now rely on AI tools without clear visibility into where data is stored, how it is processed, or whether the tools meet regulatory or contractual requirements.
This gap has created a new category of operational risk—one that disproportionately affects industries with sensitive client data and strict confidentiality expectations.
Both sectors manage information that is highly valuable to attackers and highly sensitive to clients:
Legal firms hold case files, private communications, financial records, identity information, and materials protected under attorney–client privilege.
Real-estate and property-management firms handle tenant applications, payment data, access-control credentials, lease records, and building-system integrations.
Generative AI tools—especially public, cloud-hosted platforms—introduce privacy challenges that traditional software did not.
Key concerns include:
Data persistence:
Many AI systems retain user inputs to train models or improve performance, creating long-term exposure.
Cross-tenant mingling:
Shared-model architectures may analyze data from multiple organizations to refine model behavior.
Opaque data-handling practices:
Organizations often lack documentation showing how data is processed, stored, or deleted.
Model drift and output reliability:
AI that produces incorrect summaries or misinterprets legal or real-estate documents can create operational and reputational risk.
Regulatory bodies such as the American Bar Association (ABA), FTC, and state real-estate commissions now emphasize accountability, transparency, and data-handling controls when AI is used in operational workflows.
Generative AI expands the exposure surface in several ways.
When staff upload contracts, tenant data, case notes, screening documents, or internal emails into public AI tools, the information may leave the controlled environment defined by the firm’s policies, compliance obligations, or cyber-insurance requirements.
Attorney–client privilege and tenant privacy requirements impose strict limits on how data can be stored and processed. “Convenience use” of AI tools can unintentionally violate privilege protections or breach privacy obligations.
When AI is used to summarize leases, cases, agreements, or due-diligence files, firms must preserve a record of the source data, the AI’s output, and the human review steps that validated accuracy.
Without this traceability, the firm cannot demonstrate compliance or defend decisions later.
Modern practice-management and property-management platforms are embedding AI features rapidly.
Firms may be using generative AI without realizing it, because the capability is built into existing software.
Across the industry, this is one of the most common points of data leakage.
Legal and real-estate leaders don’t need to ban AI—they need to govern it.
The following elements form the baseline for safe AI adoption:
Clear rules defining what can and cannot be entered into AI tools.
Privileged information, tenant data, financials, and identifiable records should be restricted to vetted platforms with contractual safeguards.
Organizations must confirm how vendor AI systems handle:
data retention
training usage
geographic storage
access controls
encryption
deletion policies
These details must align with regulatory and contractual obligations.
All AI outputs—summaries, drafts, recommendations—must undergo human verification before becoming part of client files, official communication, or legal interpretation.
Firms must maintain records of:
what information was submitted to AI
what the AI produced
who approved the output
how it was used in decision-making
This level of traceability is essential for compliance, dispute resolution, and risk management.
AI introduces new data flows across cloud services. Continuous monitoring, anomaly detection, and access-pattern analysis help identify misuse or unexpected data movement early.
Law firms must align AI use with privilege, ethical duties, and confidentiality requirements.
The ABA’s 2023 guidance emphasizes practitioner responsibility for understanding AI tools, maintaining client confidentiality, and validating accuracy.
AI cannot become a “blind input”—it must operate within documented safeguards.
Real-estate firms increasingly rely on AI-enhanced screening tools, smart-building systems, and CRM platforms.
These systems often connect to tenant records, payment information, and building-access management.
Data governance must extend beyond documents to encompass operational technology, vendor systems, and interconnected property systems.
Most SMBs using AI today do so without formal governance, which increases the likelihood of privacy violations, data exposure, and compliance gaps.
The path forward begins with visibility: understanding which tools are in use, what data they access, and how they interact with existing systems.
A structured readiness evaluation allows firms to identify the technical, procedural, and compliance controls needed to adopt AI safely.
Securafy supports this modernization through its AI Readiness Assessment, which examines data flows, risk points, and governance requirements specific to legal and real-estate environments.
Generative AI offers efficiency gains for legal and real-estate organizations, but it also introduces new privacy obligations.
The firms that benefit most will be those that combine AI adoption with disciplined governance, continuous monitoring, and clear operational boundaries.
By aligning AI use with data-handling requirements, privilege obligations, and tenant privacy standards, SMBs can leverage AI confidently—without exposing sensitive information or compromising client trust.