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LGPD for apps: how to adapt the preparation of applications to the law?

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Estimated reading time: 9 minutes

In recent years, the popularization of mobile devices, like smartphones and tablets, has brought a huge change to our society. As a result, the apps became part of people's daily lives.

These small programs installed on cell phones help simplify tasks, making routine easier and providing entertainment. However, faced with this new digital reality, a concern arises: the privacy and security of personal data

With the constant collection and sharing of information through applications, users began to wonder about how their personal data was being used, stored and protected.

Therefore, there was a need to regulate the digital environment. In Brazil, the General Data Protection Law – LGPD (no. 13.709/2018) came into force in August 2020 with the purpose of guiding public and private organizations on the correct treatment that should be given to third party personal information.

The LGPD is inspired by the European Union's General Data Protection Regulation (GDPR) and has clear guidelines for the collection, storage and use of personal data.

Any company or app developer that handles personal information needs to adhere to the law's guidelines. The intention is balancing technological innovation with the protection of individual rights of each user. 

Therefore, applications must be transparent about their data policy. Users must explicitly consent to the use of this information, which must be protected by those who collect it.

If you are thinking about creating an app, you need to be aware of the LGPD guidelines. It is necessary to know how to adapt the production of the product to the law and follow privacy and security measures. 

Next, we will delve deeper into this subject and teach you everything you need to know. 

Good reading!

Who does the LGPD apply to?

How does LGPD impact apps?

The changes that the General Data Protection Law introduced in Brazil impacted the entire digital environment, including websites and applications.

The LGPD requires the creation of a Terms of Use and Privacy Policies with clear and detailed information to the user.

The document must inform what data will be requested by the app and the purpose of this collection. All of this must be explained in a transparent and understandable way. 

Users need to be aware of how your data will be used before agreeing to collection, either to: 

  • E-mail marketing
  • Personalized Ads
  • Sharing

Another requirement is the possibility of users to delete their data at any time.

LGPD for apps: who leads the process?

The LGPD defines three important roles in the data processing process: 

Controller

It is the person responsible for making decisions about how personal data will be collected, processed and used. 

The controller determines what information will be requested and the usefulness of each of them. It also ensures that data processing complies with the LGPD, including respect for the rights of data subjects.

Operator 

It is the person who processes the information, that is, the collection, storage, processing and use of data in accordance with the guidelines established by the controller.

This role can be performed by a third-party company, hired by the controller to process the data on its behalf.

In charge

Also known as Data Protection Officer, is the figure responsible for ensuring compliance with the LGPD within the organization. It acts as a kind of communication channel between the controller, data subjects and the National Data Protection Authority (ANPD)

Companies that handle large volumes of personal data or that carry out high-risk activities in relation to data protection are required to appoint a person in charge.

It is essential to understand these roles as they can vary depending on the situation.

What are the penalties for those who fail to comply with the LGPD?

It is also important to highlight that the administrative sanctions provided for by the LGPD came into force effective August 2021

In case of non-compliance with the rules established by law, the ANPD can apply various penalties, ranging from warnings to fines with amounts that can reach 2% of the company's revenue, with a maximum limit of R$50 million. 

Furthermore, other sanctions may be imposed for those who do not follow the rules: 

  • Publication of the infraction
  • Blocking or deleting data, 
  • Partial database suspension 
  • Partial or total prohibition on carrying out data processing activities

What do I need to do to ensure LGPD in apps?

Now that you understand how it impacts applications, let's talk about what is required to ensure your product's legal compliance:

Mapping

Data mapping is the starting point for those who want to comply with the LGPD in their applications. 

The step involves a detailed process of identification and documentation of all information that the application collects and processes. Here's a step-by-step guide that might help:

  1. Sort the collected data into categories, such as personally identifiable data (name, address, telephone number), location, user behavior, among others. This helps you understand the nature of the data you handle.
  1. Determine where the data is obtained from. They can be provided directly by the holders, generated by the application (user activity records) or from external sources, such as integrations with social networks.
  1. Identify why each category of data is being collected. This will help ensure that all information has a legitimate and justifiable purpose.
  1. Ensure that each type of data collection complies with a specific legal basis. For example, the information may be necessary to perform a contract, comply with a legal obligation or with the consent of the data subject.

Transparency

After mapping all the data and its purposes, it's time to translate this information into transparent and accessible privacy policies for users. 

At this time, avoid using legal and complex language, make a clear, accessible and easy-to-understand communication. To do this, it is important:

  • Explain, in a transparent and detailed way, why user data is being collected and how it will be used. 
  • Include information about any targeted advertising and data sharing with third parties. 
  • Ensure users can easily access your privacy policies, directly from the app, via links or policy summary on the settings screen.

User consent

The app must explain why the data is needed, and users have the right to withdraw consent for use at any time. For that:

  • Be transparent about this option.
  • Provide a simple process for anyone who wants to withdraw consent. 
  • Offer in-app privacy controls, allowing users to adjust their privacy preferences and choose what data they want to share.

Suitable prototype

From the beginning of app development, it is important to consider GDPR compliance. Even when creating a prototype, consider data privacy and security practices. 

For example, when creating your app workflow, integrate consent requests and explanations about data collection at relevant points in the user experience. This helps make GDPR compliance a natural part of interacting with the app.

Adding security measures from the beginning of app development is also a great idea. You can use data encryption, authentication, and other secure development practices.

Monitor and update privacy practices

Compliance with the LGPD is not a one-time task, but rather an ongoing process. As your app evolves and new features are added, it's important to keep your privacy and security policies up to date. 

To do this, conduct regular assessments to ensure practices remain compliant, even after updates.

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Artificial intelligence (AI) is reshaping the way the financial sector operates, from risk analysis to the automation of complex processes. More than a trend, AI has become a strategic tool for financial institutions that want to increase their efficiency, reduce costs and offer personalized experiences. Within this scenario, the use of AI agents for finance has been gaining ground as a practical and accessible application for companies of all sizes.

Financial dashboard with automated charts and visuals representing artificial intelligence

AI Software Development in the Financial Sector

Creating AI-based solutions in the financial context requires robustness, security, and adaptability. Developing this type of software requires an architecture that is prepared to handle large volumes of data, continuous learning, and the ability to provide accurate insights.

In addition, systems need to be able to handle sensitive data, integrate with multiple sources (such as banks, brokerages, and ERPs), and adapt quickly to regulatory changes in the industry. Flexibility and modularity are core elements of any AI architecture for finance.

Integration with Existing Infrastructures

Much of AI’s success in the financial sector depends on its integration with legacy systems. This includes internet banking platforms, CRMs, payment gateways, and compliance tools. Using NoCode platforms such as make up or N8N allows you to create effective connections without the complexity of traditional development.

By the way, if you want to experience in practice how to integrate financial flows with AI, No-Code Start-Up provides a free N8N course with full video on YouTube. It's a great opportunity to explore real automations and understand how to structure secure and intelligent integrations in an accessible way.

With this approach, banks and fintechs can activate intelligent flows based on real data, such as automatic sending of alerts, personalized segmentations and recommendations based on consumer behavior.

Challenges in AI Development for the Financial Sector

Despite the enormous potential, there are challenges that need to be considered. Among the most relevant are:

  • Data quality: models are only effective if fed by clean and organized data.
  • Explainability: It is essential to understand how the AI arrived at a particular recommendation.
  • Cultural resistance: Traditional teams may resist adopting automation and algorithm-based decisions.

As highlighted by Deloitte, the combination of data governance, team training and ethical monitoring of AI is essential to mitigate risks and generate consistent results.

Security and Regulatory Compliance

The financial sector is one of the most regulated in the world. Therefore, all AI applications must comply with standards such as LGPD, GDPR and Central Bank regulations.

The adoption of good practices Data Privacy by Design, end-to-end encryption and role-based access control are just some of the basic requirements. Platforms such as Xano offer robust infrastructure with a focus on security for those who want to develop financial backends with AI.

Digital security illustration with padlock and financial data, symbolizing protection and compliance in AI application

Software Scalability and Resilience

As AI becomes a critical part of operations, it is necessary to ensure that systems are scalable and resilient. This means being able to grow as demand dictates, without compromising performance or security. Cloud computing and the adoption of microservices are essential strategies in this journey.

Companies like Goldman Sachs and Bank of Brazil have already demonstrated, in different contexts, how AI models can be deployed gradually, safely testing hypotheses before scaling to the entire operation.

AI Agents for Finance: Use Cases and Applications in the Financial Sector

1. Automated credit analysis

Companies like Credits use AI to evaluate hundreds of variables — including banking history, spending habits, and public data — to offer personalized credit. This reduces default rates and expands access to credit in a fairer way. According to McKinsey, automation can reduce analysis time by up to 70%.

2. Fraud prevention

O Bradesco and other institutions have implemented machine learning models that detect fraud based on behavioral patterns. When a transaction deviates from the pattern, the system triggers an automatic block or sends an additional verification to the user. According to Visa, the use of artificial intelligence helps prevent fraud totaling approximately US$14T25 billion.

3. Automated investment management

Robo-advisors like the ones from XP Investments use algorithms that analyze investor profiles, financial goals and market conditions to assemble and rebalance portfolios autonomously. CB Insights highlights that these systems are democratizing access to quality financial services, previously restricted to large investors.

4. AI-powered customer service

O Itau has incorporated AI into its digital channels, allowing customers to renegotiate debts, request second copies of bills or consult invoices using natural language. This reduces response time, improves customer experience and frees up human teams for more complex cases. According to Accenture, up to 80% of first-level banking interactions can now be automated using artificial intelligence.

5. Cash flow forecast

Financial management startups use AI agents for finance that integrate data on accounts payable and receivable, seasonality and market trends to predict cash flow for the coming months with high accuracy. Based on this information, more assertive decisions can be made. Harvard Business Review reinforces that this approach reduces the margin of error in financial projections and improves strategic planning.

The Role of AI Agents for Finance

Among all the applications, the AI agents for finance stand out for their versatility and accessibility. They function as intelligent “copilots”, performing tasks such as:

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The Future of AI in the Financial Sector

Artificial intelligence in the financial sector is no longer a distant promise — it is present in strategic decisions, customer service, and risk management. The adoption of technologies like AI agents for finance represents a leap forward in digital maturity. As technical challenges are overcome and platforms become more accessible, companies of all sizes will be able to use AI not only to automate, but to evolve.

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How AI is changing the market can be observed in practically all sectors of the economy, and this change is intensifying every day. Artificial intelligence (AI) is being recognized as a disruptive force that is profoundly reshaping the global market. From simple tasks to complex decisions, it has been integrated into processes in various sectors, transforming the way people work, consume and manage businesses.

Furthermore, when observing the effects of this transformation, it becomes clear how much the job market is being reconfigured: new opportunities arise, some professions lose ground and others adapt or are reborn with the support of technology, which demonstrates how AI is changing the market in a broad and profound way.

How AI is changing the job market

AI is accelerating the automation of repetitive and operational tasks. AI systems are already being used to efficiently perform:

  • Customer service with chatbots.
  • Predictive data analysis for sales and marketing.
  • Automated financial and audit processes.
  • Inventory control and logistics.

These changes not only reduce operational costs, they also require the workforce to be retrained for new roles, which reinforces how AI is changing the job market with great intensity.

Representation of people and artificial intelligence collaborating in different professions

Professions affected by artificial intelligence

According to PwC's report on the future of work (source), it is estimated that up to 30% of human tasks could be automated by the mid-2030s. This data shows, in practice, how AI is changing the job market and skills requirements.

Some of the roles most impacted by AI include:

  • Telemarketing operators
  • Administrative assistants
  • Data Analysts (some tasks being replaced by generative AI)

On the other hand, new functions emerge, such as:

  • Prompt Engineers
  • Automation experts with NoCode
  • Conversational Experience Designers
  • Intelligent Agent Managers

Those AI agents, for example, have been increasingly used in companies seeking to automate decisions and perform tasks with minimal human intervention. According to an analysis of the The Verge, large companies such as OpenAI, Google and DeepMind are investing heavily in the development of these systems, which can already act independently in complex corporate processes. They are designed to operate autonomously, learn continuously and integrate with other technologies — which makes them key players in the ongoing digital transformation.

What's Happening Now: How AI is Changing the Marketplace in Numbers

The AI market is experiencing exponential growth. The sector is estimated to surpass US$ 500 billion in value by 2027. There is a global race for innovation, with startups, large companies and governments investing heavily in:

  • Generative models (like ChatGPT)
  • Robotic Process Automation (RPA)
  • Artificial intelligence applied to health, education, law and agribusiness

This movement demonstrates how AI is being positioned as a strategic asset for growth and competitiveness.

Suggested reading:

AI Agent and Automation Manager Training

What are the negative aspects of AI in the job market?

Despite promising advances, important challenges also arise:

  • Structural unemployment: functions terminated without sufficient time for requalification
  • Digital inequality: not everyone has access to technological education
  • Technological dependence: companies become hostages of platforms and algorithms
  • Ethical and privacy issues: inappropriate use of data and biased automated decisions

These factors require public policies, business leaders and civil society to debate limits, transparency and responsibilities in the use of technology.

People using AI tools in a modern workplace

Opportunities and the future of work with AI

The key is in the conscious adaptation. The future of work will be driven by:

  • Continuous learning and professional requalification
  • Mastery of AI tools and NoCode platforms
  • Creating new business models based on data and automation
  • Development and management of autonomous AI agents

Increasingly, professionals and companies will need to adopt a stance proactive and experimental, turning AI into an ally.

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AI and entrepreneurship: new market frontiers

Artificial intelligence is not only transforming the traditional job market, it is also paving the way for new business models. Digital entrepreneurs are using AI to create scalable products such as intelligent assistants, recommendation systems, and data-driven SaaS platforms. No-code tools combined with AI agents are enabling the emergence of lean, highly automated, and highly personalized startups.

A great example is the AI-based micro-SaaS, which solve very specific problems and can be created by a single person. Platforms like Bubble, FlutterFlow and Make, integrated with OpenAI models, make this scenario not only possible, but accessible.

For those who wish to explore this new territory, we recommend SaaS IA NoCode Training, designed to transform ideas into digital products using the power of artificial intelligence.

How AI is changing the market and shaping the future

Artificial intelligence is changing the market in an irreversible way. It is not only a technological revolution, but also a social, professional and economic transformation. The question is no longer “if” AI will impact your work, but “how will you position yourself in this new era”.

The good news is that there have never been so many accessible tools for those who want to learn AI in practice.

See all No Code Start Up training and courses

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