Generative AI has become part of daily legal practice, and the trend is well documented in Brazil, one of the fastest-moving legal markets on this front. Research from FGV Direito SP, a leading Brazilian law school and innovation research center, found that 58% of legal professionals use AI tools daily, and 80% rely on the technology frequently.
That shift mirrors what's happening inside the courts themselves. Brazil's National Council of Justice (CNJ) reported that 45.8% of courts and judicial bodies already apply generative AI to text-based tasks, from case summarization to draft preparation.
Adoption has outpaced control. The same FGV research found that only 20% of organizations have formal AI governance policies in place, or are in the process of building them. The result is that public AI tools built for general consumer use are routinely handling complex drafts and sensitive client data.
Using AI tools without security safeguards can expose a company to data breaches, contractual penalties, and privacy violations. Gaining speed without losing control has become one of the central challenges for legal departments trying to structure their operations around measurable outcomes.
Public generative AI tools, the kind accessed through a browser by millions of users, were built to serve broad, shared environments. When a lawyer pastes a clause from an M&A agreement or a spreadsheet of labor contingencies into one of these tools, that information gets processed according to the provider's own infrastructure and policies.
Depending on the provider's terms, that input can be stored or used to further train the underlying model. Once that happens, confidential documents are no longer under the company's direct control.
This has a direct impact on privacy obligations. Brazil's General Data Protection Law (LGPD) requires a clear legal basis and adequate security for handling personal data. Submitting employee or client information to platforms without solid security and transparency guarantees makes the job harder for the company's Data Protection Officer, and it opens the door to regulatory penalties.
There's also a technical oversight problem. FGV's research found that 75% of professionals use AI both in areas where they have expertise and in areas outside it. Without verification checkpoints or clear usage rules, relying on open AI tools raises the odds that surface-level reviews and operational errors slip through.
Moving to the right tool means reviewing practical security requirements before rolling out access to the team. IT and Legal Ops leaders should weigh the following criteria for any solution under consideration:
Retention and training guarantees. The provider should disclose how long data is retained and confirm it will not be used to train external models.
Encryption and isolation. Beyond protecting data in transit and at rest, the solution should guarantee that each company's environment is isolated, with no data sharing across clients.
Access management. The system should let teams define exactly who can view or process each category of document.
Usage logs. The platform needs to generate an auditable record of who ran each query and which document was involved.
These requirements are part of the broader process of selecting and implementing AI-enabled legal software built for corporate use.
Public AI tools are built to serve millions of users at once. Corporate platforms are built to protect confidential information, control access, and maintain audit trails.
The table below summarizes the core differences between the two models:
Saving a few minutes on isolated tasks is not the same as increasing the legal department's overall capacity. Using tools in an ad hoc way can produce small individual wins, but it doesn't resolve team bottlenecks or reorganize how demand flows through the department.
Without data on response time, request volume, and team capacity, legal keeps struggling to demonstrate its real value to the CEO and CFO.
Integrating secure solutions into process management makes it possible to organize deadlines, reduce operational errors, and get a clearer read on the department's capacity. That kind of integration also moves the operation closer to the competencies outlined by CLOC, particularly around technology, data governance, and process optimization.
ENSPACE brings demand management and AI capabilities into a single environment. The platform tracks deadlines, priorities, workload, and delivery indicators across the department.
Within that environment, AI features operate under access, security, and traceability rules defined for the operation.
To let teams move fast without exposing sensitive information, the platform runs on a protected corporate architecture:
Controlled data use. Information stays under the company's control and is never used to train AI models.
Access control and encryption. Data is protected by encryption, and each user only accesses what their role permits.
Specialized agents. Teams can build assistants configured for specific tasks, such as draft review or notice triage, using models like Claude, GPT, Gemini, or DeepSeek depending on what each task requires.
Pre-deployment validation. Agents go through a testing phase with response tracing before they're released into the team's daily workflow.
When AI runs in a protected environment, legal teams can automate tasks, speed up analysis, and increase output without compromising confidentiality. The operation gains efficiency without expanding the company's exposure to regulatory, contractual, or operational risk.
The core difference comes down to how data is handled. In public AI tools, data processing follows the provider's own policies. In protected corporate environments, the company controls access, data isolation, and specific rules for storage, retention, and use, delivering stronger security and compliance.
Using free AI tools can create compliance violations when personal data is entered without a legal basis, transparency, adequate safeguards, or clear guarantees around retention and sharing. Companies should review a provider's policies before authorizing any tool for use.
Custom AI Agents are virtual assistants configured to handle specific legal tasks under predefined rules, instructions, and permissions. They strengthen security because they operate strictly within the company's protected environment, run on encrypted infrastructure, and go through a validation phase before being built into any workflow.
To ensure this, companies should adopt corporate solutions that include a contractual zero-retention clause. That guarantee confirms information is processed temporarily on a protected server and discarded according to the terms of the agreement.
Logical data segregation keeps each company's document repository and knowledge base separate from every other environment on the platform. Combined with proper access controls, this structure reduces the risk of unauthorized users viewing confidential information, case reports, or contract drafts.
