Shadow AI: what it is, the risks to data governance and how to act

Shadow AI is the use of artificial intelligence tools inside the company without the knowledge, approval or oversight of IT, data or compliance. It is not a distant risk, nor a topic for information security alone. Recent surveys indicate that almost half of Brazilian professionals already use some AI at work without telling anyone, and most cybersecurity leaders suspect, or already have evidence of, unauthorized use inside their own company. In practice, Shadow AI is the buyer who pastes a supplier spreadsheet into a public assistant to ask for a quick classification. It is the master data analyst who dumps the entire materials base into a generative AI tool to tidy up descriptions. It is the sales team that summarizes contracts and proposals by pasting the whole content into any chat. All of this is happening today, inside companies that invest in security and compliance, and they still don't know what is going out the back door.

How Shadow AI shows up in companies' day to day

Anyone who has taken part in an SAP project or any ERP implementation knows how pressure for quick results leads teams to look for shortcuts. Before, the shortcut was a parallel spreadsheet. Today, the shortcut is a public AI. The pattern repeats in purchasing, in supply chain, in finance and in customer service. An analyst receives a base of ten thousand poorly described material items and, instead of waiting for the data team to handle it, pastes everything into an AI tool and asks for automatic standardization. A buyer uploads contracts and supplier evaluations to some assistant, without checking whether the tool stores or reuses the content sent. A manager generates executive reports from sales and margin data without asking whether that can leave the company.

The problem is not the intent. Almost always the person is trying to be more productive, to solve something the company's formal structure hasn't solved yet. The problem is that supplier, customer, material and financial results data, which are exactly the company's master data, start to circulate outside any control, with no traceability, with no idea where they are stored and for how long.

Why Shadow AI is, at the root, a master data problem

The first reaction to Shadow AI is usually to treat it as an information security topic. That makes sense, but it is an incomplete reading. What is being exposed, almost always, is master data: customer master, supplier master, product technical sheet, commercial terms, cost structure. These are the data that support purchasing, sales, credit and risk decisions. If that data is poorly classified, duplicated or out of date, the problem already existed before AI showed up. The difference is that now it leaves the company at a speed and scale that no manual process reached.

This connects with a point I tend to repeat in governance projects: artificial intelligence does not improve bad data by magic. If the supplier master is fragmented, with the same supplier registered three times with different CNPJs (Brazil's company registration number) and addresses, an AI used without control doesn't identify that duplication, it just processes what it received and returns an answer that looks authoritative and masks a structural data problem. The biggest risk is not only in leaking information outside. It is in decisions made on bad master data, processed without audit and without an owner.

Who should take ownership of this problem inside the company

A common mistake is to dump Shadow AI entirely on the IT or information security desk. IT helps, a lot, by blocking high-risk tools and monitoring traffic. But without a process, without a data owner and without a clear rule about what can leave the company, the problem comes back through another door. The answer I have seen work in mature companies combines four fronts: data and governance leadership defines which information is sensitive and which are the critical master data; information security handles technical control and monitoring; compliance and legal translate those rules into the language of the LGPD (Brazil's General Data Protection Law) and the AI Legal Framework (Brazil's legal framework for artificial intelligence), since the fine for a personal data leak can reach two percent of the company's revenue, capped at fifty million reais per violation; and the leadership of each business area, purchasing, supply chain, sales, finance, applies the rule in the team's day to day, because that is where the shortcut is born.

The data steward and data owner, roles that already exist in more mature governance programs, gain one more reason to exist. Whoever owns the supplier master should also be the one who approves, or not, which type of AI tool can touch that data.

Which tools and practices reduce the risk

Blocking all AI use rarely works. A good share of the companies that banned AI tools still have employees using those same tools on the side, only with no control at all, because prohibition without an alternative pushes the behavior under the rug. The most effective path combines a few fronts: offering a corporate AI alternative, with authentication and usage traceability; keeping an up-to-date data catalog that makes clear which datasets are sensitive; having data quality and classification tools that identify duplication before the data gets near any AI; and keeping simple governance policies, with a practical example per area, because a generic thirty-page policy doesn't change the behavior of someone up against a tight deadline.

In the governance and master data market, tools like SAP MDG, Informatica, Stibo Systems, Reltio, Semarchy, Profisee and Ataccama keep fulfilling the role of organizing and maintaining reliable master data, which reduces precisely the kind of messy base that leads someone to reach for an AI shortcut. For catalog and metadata, solutions like Collibra, Alation and Microsoft Purview help give visibility into where each piece of sensitive data is and who can access it.

How to prepare to work on this front

Anyone who works or wants to work with data governance finds in Shadow AI one of the hottest topics of the coming years, because it forces the company to mature fast in policy, in data classification and in culture. The path goes through understanding the fundamentals of governance and master data, the data owner and data steward roles, the LGPD and, more and more, the AI Legal Framework. Certifications like DAMA's CDMP help structure that knowledge, and hands-on knowledge of data classification and quality makes a real difference when applying it in a real company.

The opportunities this wave is opening

A company that matures fast on Shadow AI doesn't do it alone. Demand grows for professionals who translate technical risk into business language, put together an applicable data policy and train teams to use AI without exposing sensitive master data. This shows up in data governance job openings, in data steward and data owner positions, in specialized consulting and in compliance areas that have started to require data knowledge alongside traditional legal knowledge. For those who already work in record maintenance, in data quality or in MDM, it is a good time to broaden their scope to the AI governance side, because the technical base is the same: understand the data, know where it lives and who can touch it.

Main questions about Shadow AI

What is Shadow AI, in one sentence? It is the use of artificial intelligence tools in the workplace without the company's knowledge, approval or control.

Is Shadow AI the same as Shadow IT? They are relatives. Shadow IT is the use of any system not approved by the company. Shadow AI is a specific case, which gained weight of its own because it involves tools capable of processing and retaining a large volume of sensitive data at a much higher speed than a traditional system.

Is Shadow AI only an information security problem? No. At the root, it is a governance and master data problem, because what is normally being exposed or poorly processed is customer, supplier and product master data and financial information.

How do I know if my company has Shadow AI? Common signs include spikes in traffic to public AI tools, teams that sped up deliveries without a formal process change, and the absence of any approved corporate AI tool, which pushes everyone toward outside solutions.

Does banning AI tools solve the problem? Rarely on its own. Prohibition without an alternative tends to push use under the radar. The most effective path combines a safe corporate alternative, a clear policy, well-classified data and continuous team education.

MDM Academy: training for the new phase of data governance

MDM Academy is 4MDG's school of governance and master data, created to train professionals who move beyond theory and apply governance, data quality and MDM in the real routine of a company, whether in purchasing, in supply chain, in IT or in SAP projects. Topics like Shadow AI reinforce why this training matters now: there is no point understanding artificial intelligence without understanding master data, information classification and governance processes, because that is where the risk lives, and also the opportunity. Those who want to go deeper on this path can get to know MDM Academy or join the Data Club, a community that brings together record maintenance, master data and governance professionals to exchange real experience about the day-to-day problems of companies.

Conclusion

Shadow AI is not a passing fad, nor a problem that gets solved with a technical block alone. It is a symptom of something older: companies that have not yet treated master data with the seriousness it deserves find, in generative AI, a new and much faster way to expose that old problem. The good news is that the path to solving it is already known to those who have worked in data governance for years: define an owner, classify information, standardize the master record, create a clear policy and train people able to apply it in practice. The question left for each leader is simple. If a team in your company is using AI without control right now, do you know what kind of master data is walking out the door?