The silent risk of using AI in the electricity sector: when the customer's bill becomes exposed data.

The incorporation of Artificial Intelligence tools into the routines of the Brazilian electricity sector is already an operational reality. Integrators, investors, credit managers, trading companies, lawyers, engineering firms, consultancies, associations, cooperatives, consortia, and energy asset operators use AI systems to draft contracts, analyze invoices, review technical documents, interpret regulatory standards, produce reports, organize consumer databases, and build commercial proposals. 

This advancement represents gains in productivity and analytical capacity, but it also creates a new layer of risk: the exposure of personal data, customer documents, consumer unit information, invoices, contracts, financial data, business strategies, and documents protected by professional secrecy.

The General Data Protection Law – LGPD regulates the processing of personal data carried out by natural or legal persons, public or private, in physical or digital media, and is based on the protection of freedom, privacy and the free development of the personality of the natural person [1]. 

In the electricity sector, this concern takes on particular relevance because seemingly technical documents can reveal consumption habits, economic profile, location, ownership, tariff patterns, distributed generation data, payment data, default history, and strategic customer information. 

This article analyzes the legal and reputational risks of using AI in the electricity sector and proposes practical guidelines for anonymization, pseudonymization, minimization, and the use of hypothetical cases as a method of data protection.

The electricity sector is ceasing to be merely a sector of physical infrastructure and is becoming a data-intensive ecosystem. Electricity, previously analyzed predominantly from the perspective of generation, transmission, distribution, commercialization, and consumption, now also needs to be understood as an informational asset. 

Each invoice, consumer unit, load curve, power plant lease agreement, generation report, adhesion agreement, regulatory opinion, investment proposal, credit allocation, or connection process contains information relevant to business decision-making. In this context, Artificial Intelligence has come to occupy a significant space in the daily routine of professionals in the sector. 

AI assists in reading contracts, comparing invoices, preparing administrative defenses, organizing documents, building financial models, and interpreting regulations. ANEELin the production of technical content, in the preparation of notifications, in risk analysis and in the structuring of business models. 

The problem, therefore, is not the use of AI itself. The problem lies in the careless use of AI with real customer data.

The central question is no longer: "Can I use AI in my work?". The legally appropriate question becomes: "What data can I input into an AI tool, for what purpose, on what legal basis, in what technological environment, with what level of security, and with what possibility of identifying the data subject?". 

This shift in mindset is essential. AI should not be seen merely as a productivity tool, but as a potential environment for data processing. And, if personal data is processed, the LGPD (Brazilian General Data Protection Law) applies.

The LGPD (Brazilian General Data Protection Law) as a legal framework for the use of AI in the electricity sector.

LGPD defines personal data as information related to an identified or identifiable natural person; sensitive personal data as information about racial or ethnic origin, religious belief, political opinion, trade union membership, health, sex life, genetic or biometric data, when linked to a natural person; and anonymized data as that relating to a data subject who cannot be identified, considering the use of reasonable and available technical means at the time of its processing [1].

The law also adopts a broad concept of data processing, encompassing operations such as collection, reception, classification, use, access, reproduction, transmission, distribution, processing, archiving, storage, elimination, evaluation, control, modification, communication, transfer, dissemination or extraction [1]. 

This means that copying an invoice, contract, personal document, power of attorney, customer spreadsheet, generation report, or consumption history into an AI tool may constitute the processing of personal data.

In the electricity sector, seemingly technical data is often personal data or data capable of identifying someone. A consumer unit linked to a residence, an invoice with address, CPF (Brazilian tax identification number), consumption history, tariff pattern, installation number, meter number, distributed generation data, and payment information can reveal economic and behavioral aspects of a person. Even if the data does not seem intimate, it can identify or allow the identification of the data subject when combined with other elements.

Therefore, the use of AI must observe the principles of purpose, adequacy, necessity, free access, data quality, transparency, security, prevention, non-discrimination, accountability and responsibility [1]. 

In practical terms, this means that having a good tool is not enough. It is necessary to demonstrate that the use of the tool is necessary, proportionate, safe, and compatible with the purpose stated to the client.

3. Why is the electricity sector particularly exposed?

The documentation routine of the electricity sector combines technical, economic, legal, regulatory, registration, and asset data. An integrator may receive identity documents, proof of address, invoices, installed load data, property photos, electrical plans, financing contracts, and banking information. A shared generation manager may process consumer data, cost-sharing percentages, beneficiary units, average consumption, offset credits, and delinquency history. A trading company may analyze load curves, contracted demand, consumption history, CNPJ (Brazilian tax ID), legal representatives, price, collateral, guarantees, and contracting strategy.

For lawyers, consultants, and regulatory advisors, the risk is even more evident. Petitions, notifications, administrative defenses, power plant lease agreements, power of attorney agreements, corporate documents, technical reports, and communications with distributors may contain personal data, strategic information, and content protected by professional secrecy. When these documents are entered unfiltered into AI systems, the professional may expose the client to risks of privacy breaches, confidentiality violations, contractual breaches, and civil, administrative, or reputational liability.

Herself - Host ANEEL The agency maintains an institutional page on privacy and data protection, informing of its commitment to compliance with the LGPD and to the secure, transparent collection, processing, storage, and sharing of personal information in accordance with the rights of data subjects [2]. In 2026, the Agency also published its Privacy Governance Program, with guidelines related to maturity diagnosis, data inventory, contractual compliance, risk management, and incident response [3]. Although these documents are focused on the Agency's institutional activities, they signal that data protection has become integrated into the regulatory culture of the electricity sector itself.

4. Artificial intelligence does not outsource responsibility.

One of the biggest misconceptions in the use of AI is to imagine that the responsibility for the generated result belongs solely to the tool. It doesn't. AI can assist, suggest, organize, summarize, and accelerate analyses. But the decision to enter data, validate the response, sign the document, send the statement, guide the client, or use the result in an administrative, judicial, or commercial process remains with the professional or the company.

In the case of lawyers, caution is reinforced by duties of professional secrecy, technical diligence, and responsibility in handling client information. For integrators, consultants, managers, marketers, and investors, there are also contractual, commercial, and reputational duties. Depending on the activity, the inappropriate use of AI may violate confidentiality clauses, data protection terms, corporate agreements, investment contracts, compliance duties, or internal information security policies.

Brazil is still debating a specific legal framework for Artificial Intelligence. Bill No. 2.338/2023 was approved in the Federal Senate and sent to the Chamber of Deputies, remaining in bicameral processing [4]. While specific AI legislation is not definitively consolidated, the LGPD, the Civil Code, the Consumer Protection Code, sectoral regulatory norms, contracts and professional duties continue to be the main applicable legal parameters.

5. The main risks of the inappropriate use of AI in the electricity sector.

The first risk is the leakage or improper exposure of personal data. This can occur when a user enters real documents into tools without knowing how the data will be stored, used, shared, retained, or eventually employed to improve systems.

The second risk is the violation of contractual or professional confidentiality. Power plant lease agreements, power purchase agreements, commercial proposals, legal opinions, administrative defenses, extrajudicial notifications, consumer databases, and cost allocation spreadsheets may contain confidentiality clauses. The use of AI without prior evaluation can represent a breach of trust even when there is no public leak.

The third risk is the re-identification of the account holder. Even when the name is removed, the combination of city, distributor, consumer unit number, power plant capacity, invoice amount, protocol date, default history, and case characteristics can allow for indirect identification of the customer. This point is crucial: anonymizing is not just about changing the name to "Customer A".

The fourth risk is the generation of incorrect or unverified information by AI. In regulatory matters, this could be an erroneous reference to an article by REN. ANEEL Law No. 1.000/2021, Law No. 14.300/2022, a tax rule, or a court decision can compromise client guidance. The risk is not only one of privacy; it is also one of technical quality, professional responsibility, and legal security.

The fifth risk is the loss of control over strategic documents. Investor data, cash flow, CAPEX, OPEX, financing agreements, receivables, guarantees, connection schedules, technical opinions, asset valuations, and free market strategies may not be personal data in all cases, but they can be confidential and economically sensitive business information.

6. Anonymization, pseudonymization, and fictitious data: concepts that should not be confused.

Anonymization, under the LGPD (Brazilian General Data Protection Law), presupposes that the data subject cannot be identified, considering reasonable and available technical means at the time of processing [1]. Pseudonymization, on the other hand, reduces direct identification, but still allows re-identification through the use of additional information, key, auxiliary table or data cross-referencing. In practice, replacing “Maria Aparecida dos Santos” with “Client A” may not be anonymization. In many cases, it is only pseudonymization.

Inadequate example: "Customer A, residing at Rua X, nº 120, in Chapecó/SC, holder of UC nº 123456, has a 75 kW power plant connected to the local distributor, with an invoice of R$ 8.732,41 in April 2026." Even without the name, the set of information may allow identification.

Safest example: "A hypothetical customer, an individual, holder of a residential consumer unit in Southern Brazil, owns a medium-sized distributed microgeneration system and questions the charging of certain tariff components in a recent bill." The second version preserves the legal issue but reduces the exposure of the account holder.

The most recommended technique for everyday use of AI is to work with hypothetical cases, aggregated data, or sanitized versions. Instead of handing the tool the client's actual documentation, the professional should formulate the technical, legal, or commercial problem in an abstract way, sufficient to obtain analytical support without exposing identity, documents, or unnecessary strategic information.

7. How to anonymize information when working with prospective clients.

Anonymization must follow a layered logic. Simply deleting the name is not enough. It's necessary to remove, replace, generalize, or distort, in a controlled manner, anything that could directly or indirectly identify the client.

7.1. Replacing names with generic categories

Instead of using the name of a specific individual or company, it is recommended to use expressions such as "individual consumer," "hypothetical residential customer," "distributed generation integrator company," "legal entity in the productive sector," or "investor interested in distributed generation assets."

7.2. Suppression of documents and identification codes

The following information must be removed: CPF (Brazilian individual taxpayer registration number), RG (Brazilian national identity card), CNH (Brazilian driver's license), passport, CNPJ (Brazilian company taxpayer registration number) when associated with individual representatives, state registration number, consumer unit number, installation number, meter number, protocol number, administrative process number, contract number, bank details, signatures, phone numbers, emails, and full addresses.

7.3. Location Generalization

Instead of providing a full address, neighborhood, or small municipality, it is recommended to use expressions such as "municipality in the interior of Santa Catarina," "Southern Region of Brazil," "local distributor concession area," or "low-voltage consumer unit." When the distributor is legally relevant, it should be assessed whether it can be replaced by "local distributor" without compromising the analysis.

7.4. Altering exact values ​​by ranges or fictitious values

Precise figures can facilitate re-identification. Instead of "invoice of R$ 12.487,93", one can use "invoice exceeding R$ 10", "approximate value of R$ 12" or "fictitious value used only for simulation". For illustrative articles, training materials, and reports, it is recommended to state that the values ​​are hypothetical and have an exclusively didactic purpose.

7.5. Transforming real documents into hypothetical cases

Instead of inserting an actual invoice, the professional can formulate the case as follows: “hypothetical case: a Group B consumer, with approved distributed microgeneration, received a recent invoice with a questionable tariff component charge. The legal question is whether the charge is compatible with the applicable regulations.” This technique preserves the usefulness of the AI ​​without exposing the original document.

7.6. Avoid combining rare data.

The more specific the dataset, the greater the risk of re-identification. The expression "only ceramic industry in a given municipality, with a 1,2 MW plant, lease agreement signed in November 2025 and administrative proceedings against distributor X" can identify the client even without a name. The safer version would be: "industrial company with a medium-voltage consumer unit, linked to a distributed generation asset lease agreement, with contractual and regulatory controversy after a change of ownership".

8. Practical model for converting a real-world case into a safe case for AI.

High-risk version: “The company Alfa Energia Ltda., CNPJ nº 00.000.000/0001-00, located in Chapecó/SC, holder of UC nº 123456789, received an invoice from the distributor in the amount of R$ 37.842,19, referring to the month of April 2026, with a charge for TUSDg on energy injected by the 500 kW photovoltaic plant. Partner João da Silva signed a power of attorney for the office.”

Recommended version: “A legal entity in the productive sector, holding a consumer unit in Southern Brazil, recently received an invoice charging a tariff component on energy injected by a distributed generation system. The unit has a medium-sized photovoltaic plant. A legal analysis is requested, in theory, regarding the validity of the charge in light of applicable regulations, without considering personal data or identifiable documents.”

9. Recommended internal guidelines for companies in the electricity sector.

Companies operating in the electricity sector should adopt a specific internal policy for the use of AI. This policy should establish what can be included in open tools, what requires prior anonymization, what depends on a contracted corporate environment, and what is prohibited. The ANPD (Brazilian National Data Protection Authority) has a guidance document on information security for small data processing agents, with administrative and technical measures, a checklist, and a model for recording personal data processing operations [5]. This material is especially useful for integrators, small consultancies, offices, managers, and energy companies that do not yet have a robust privacy governance structure.

Among the recommended measures, the following stand out: prohibition of inserting sensitive personal data into open tools; prohibition of sending complete invoices without anonymization; mandatory removal of CPF (Brazilian individual taxpayer registration number), RG (Brazilian identity card number), address, UC (consumer unit), meter, protocol, and signature; preferential use of fictitious, aggregated, or anonymized data; mandatory human review of all results generated by AI; registration of the tools used; classification of documents by level of confidentiality; periodic training of teams; contractual clauses with suppliers; incident response policy; and a routine for the secure disposal of documents.

The ANPD also updated the Guidance Guide for Definitions of Data Processing Agents and the Data Protection Officer, clarifying concepts such as controller, operator, data protection officer and sub-operator [6]. The distinction is important because, in a chain involving an energy company, software provider, AI platform, law firm, technical consultancy and end customer, there may be multiple data processing agents with different responsibilities. The more complex the chain, the greater the need for clear contracts and a responsibility matrix.

10. AI vendors, platforms, and tools: what to check before using them.

Not all AI tools have the same level of security, governance, and transparency. Some operate in corporate environments, with contracts, user management, limited retention, administrative controls, and the possibility of not using the data for training. Others are open tools, with less clarity regarding storage, retention, reuse, and governance.

Before using or contracting an AI solution, it is recommended to check where the data is stored; whether there is international data transfer; whether the data entered can be used for training or system improvement; what the retention policy is; whether there is encryption; whether there are access controls; whether there are usage logs; whether it is possible to delete data; whether there is an operator contract; whether there is security documentation; and whether the tool allows governance compatible with the type of information handled.

The ANPD has treated AI and data protection as a relevant regulatory topic. The Authority's regulatory sandbox seeks to test innovations in a controlled environment and reconcile the protection of fundamental rights with responsible innovation [7]. In 2026, the ANPD concluded the leveling phase of the Regulatory Sandbox Project on Artificial Intelligence and Data Protection, with technical, legal and regulatory alignment for the safe and responsible development of the selected projects [8]. This movement reinforces the trend that innovation and data protection are not opposing agendas. On the contrary, reliable innovation depends on governance.

11. Use of AI by lawyers in the electricity sector

For lawyers working in the electricity sector, extra caution is necessary. AI can be extremely useful for organizing legal arguments, reviewing clauses, structuring petitions, building comparative charts, preparing legal opinions, simulating risks, and transforming technical documents into legal language. However, client documents should not be entered raw into uncontrolled tools.

Initial petitions, contracts, notifications, invoices, powers of attorney, corporate documents, technical opinions, and message exchanges may contain personal data, confidential information, and legal strategy. Best practice involves working with three layers: human reading and selection of what truly matters; anonymization, suppression, and generalization of information; and the use of AI only as argumentative support, always with final technical and legal review. AI can suggest solutions. It should not replace professional judgment.

12. Use of AI by integrators, managers, marketers and investors

For integrators, AI can assist in drafting proposals, commercial responses, feasibility simulations, organizing contracts, and preliminary analysis of approval documentation. The key is to avoid including personal documents, invoices, photos, addresses, financial data, and financing documents without anonymization.

For shared generation management companies, the risk involves customer bases, cost-sharing percentages, invoices, average consumption, offset credits, delinquency history, and customer communication data. For energy trading companies, the focus is on load curves, contracts, purchasing strategies, migration data to the free market, demand, price, collateral, guarantees, and legal representatives. For investors, the documents may reveal valuation, cash flow, CAPEX, OPEX, guarantees, receivables, lease agreements, assignment terms, and corporate structure.

The guideline is simple: the AI ​​should receive the problem, not the client's identity. It should receive the thesis, not the raw document. It should receive the structure of the question, not the entirety of the confidential contractual relationship.

13. Recommended contractual clause regarding the use of AI.

Service contracts in the electricity sector may soon include specific clauses regarding the use of artificial intelligence tools, data protection, and human review. A possible wording is as follows:

“Use of artificial intelligence tools and data protection. The parties acknowledge that the CONTRACTOR may use technological tools, including artificial intelligence systems, to support the organization of information, drafting of documents, preliminary analyses, document review, and optimization of technical, commercial, administrative, or legal activities, provided that applicable data protection, confidentiality, and information security standards are observed. The CONTRACTOR undertakes not to insert personal data, sensitive personal data, identifiable documents, confidential or strategic information of the CONTRACTING PARTY into open artificial intelligence tools without prior anonymization, pseudonymization, or adoption of technical and administrative measures compatible with the nature of the data processed. The use of artificial intelligence will not exempt the CONTRACTOR from responsibility for human review, technical validation, and final adaptation of the deliverables to the contracted scope.”

For more sensitive contracts, it is recommended to add that, whenever possible, information will be treated in an anonymized, aggregated, or hypothetical manner, preserving the purpose of the analysis without unnecessary exposure of data subjects, consumer units, documents, projects, contracts, individual values, or strategic data.

14. Practical checklist before using AI with customer data

• Does the data identify a natural person?
• Does the data allow for indirect identification of the customer?
• Is there a CPF (Brazilian individual taxpayer registration number), RG (Brazilian national identity card number), address, phone number, email, customer unit number, meter reading, or protocol number?
• Is there a complete energy bill?
• Does the contract include a confidentiality clause?
• Is there a power of attorney, signature, or personal document?
• Does the purpose require real data, or can I use fictitious data?
• Can I replace "client" with "hypothetical client"?
• Can I exchange exact values ​​for approximate ranges?
• Can I remove specific cities, distributors, dates, and numbers?
• Does the tool used allow for privacy and security controls?
• Will the result be reviewed by a qualified person before professional use?

If any response raises doubts, the best practice is to anonymize it beforehand and insert it later. Operational urgency does not justify unnecessarily exposing the client.

15. Conclusion

Artificial intelligence will be increasingly present in the electricity sector. It can accelerate diagnoses, reduce costs, expand analytical capacity, democratize technical knowledge, and increase the efficiency of integrators, traders, managers, investors, lawyers, and entrepreneurs. But this evolution cannot occur at the expense of customer privacy, confidentiality, and trust.

In the new energy market, trust will be as strategic as technology. Companies that know how to use AI with governance, anonymization, human review, and data protection will come out ahead. This is not only because they will be more compliant with the LGPD (Brazilian General Data Protection Law), but also because they will demonstrate institutional maturity in an increasingly complex, regulated, and information-driven sector.

The responsible use of AI in the electricity sector is based on a simple premise: the machine should not be given information that the client has entrusted to the professional under a confidentiality agreement, based on trust and for a specific purpose. AI should receive well-formulated legal, technical, and strategic problems. It should not unnecessarily receive the client's identity, documents, invoices, strategy, or personal data.

The future of the electricity sector will be digital, automated, and intelligent. But it will only be legally sustainable if it is also safe, transparent, and responsible.

The opinions and information expressed are the sole responsibility of the author and do not necessarily represent the official position of the author. Canal Solar.