Artificial intelligence has transformed the way we work, offering unprecedented productivity gains. However, this silent revolution has brought a critical challenge for leadership: shadow AI.
This phenomenon occurs when employees use artificial intelligence tools without the knowledge, approval, or supervision of the technology department or company management. If you are a leader who feels the pain of not being able to monitor the use of AI in your team, this content is for you.
The unauthorized use of artificial intelligence is a real threat to information security, regulatory compliance, and corporate reputation.
Understanding shadow AI is the first step to regaining control and ensuring that process management occurs in a safe and structured manner.
Throughout this content, you will understand how shadow AI impacts processes, productivity, and the main risks for organizations.
What is Shadow AI and why is it a problem?
The concept of shadow AI follows the logic of the already known “shadow IT,” but with much deeper implications.
While shadow IT involves the use of non-certified software, shadow AI focuses specifically on artificial intelligence tools and platforms, such as large language models (LLMs) and image generators.
In this context, the use may seem harmless: an employee uses AI to write an email, summarize a document, or analyze a spreadsheet.
The problem arises when these interactions involve strategic corporate data or customer information.
The main pain point of current management is the loss of visibility. When each employee uses a different tool, without clear guidelines, the company loses consistency in processes and in the quality of deliverables.
This can lead to anything from rework to decisions based on incomplete or misinterpreted information.
Furthermore, the lack of monitoring makes it difficult to control how data is processed and what risks are involved, putting the company on a collision course with data protection laws, such as the LGPD in Brazil and the GDPR in Europe.
The dangers of poor governance in Artificial Intelligence.
The absence of robust governance for artificial intelligence exposes organizations to severe risks that go far beyond simple operational inefficiencies.
The financial and reputational impacts can be devastating for companies that ignore the proliferation of shadow AI within their ranks.
Data leaks and security vulnerabilities
The most imminent risk of shadow AI is data leakage. By entering internal data into free external tools, employees can unintentionally share confidential content outside the company’s controlled environment.
When employees use free AI tools via personal accounts, they enter sensitive data into these platforms. Furthermore, organizations that suffer AI-related breaches lack adequate access controls.
The 2025 Cost of Data Breaches report shows that 20% of all data breaches now involve shadow AI, while 27% of organizations report that more than 30% of their AI-processed data contains private information, ranging from customer records to trade secrets.
This significantly increases the attack surface and compromises critical organizational assets.
Companies that invest in data security and clear usage policies are able to reduce this type of vulnerability.
Regulatory non-compliance
In many sectors, regulatory compliance is non-negotiable. The use of shadow AI can lead to serious violations of privacy and data protection.
Fines for non-compliance with the GDPR, for example, can reach 20 million euros or 4% of the organization’s global revenue.
In Brazil, the LGPD (Brazilian General Data Protection Law) imposes strict sanctions for the improper handling of personal data, something that frequently occurs when customer information is entered into unapproved AI prompts.
Organizations that suffer violations often lack a formal AI governance policy in place. This is not just a compliance issue; it’s an indicator that leadership has not laid the necessary groundwork for secure AI operations.
On the other hand, according to Gartner research, 55% of organizations have a formal AI Council or dedicated Oversight Committee, comprising over 1,800 executive leaders.
This movement demonstrates the transition from informal initiatives to consolidated governance structures. Companies that adopt these committees tend to incorporate more robust practices for risk monitoring, stakeholder accountability, and continuous review of AI use in their operational models.
Reputational damage and biased decisions
Relying on unauthorized AI models can impact the quality of decision-making. Without proper governance, the results generated by these models may not align with the organization’s objectives or ethical standards.
Biased data and AI hallucinations can lead to poor strategic choices and damage the company’s reputation with the market and consumers.
The main causes of shadow AI
Understanding why employees adopt shadow AI is essential for developing effective mitigation strategies. Generally, there are structural and cultural reasons behind this adoption.
Accessibility and ease of use
AI tools are more accessible than ever.
Platforms like ChatGPT, Claude, and Copilot offer free or low-cost access, allowing any employee to start using AI in minutes, without IT involvement.
Open-source AI models further reduce barriers to entry, allowing teams to experiment without formal oversight.
Pressure for efficiency
Organizations face constant pressure to improve efficiency.
Employees see AI as a quick solution for automating repetitive tasks, generating content, or analyzing large volumes of data. When formal tool approval processes are slow, employees bypass the system to deliver results faster.
Lack of information on AI education.
Many employees do not understand the risks associated with the use of AI, especially with regard to data security. Without proper training, they may upload sensitive data to public tools without understanding the implications.
Investing in data culture and continuous training is becoming increasingly important.
Lack of governance
Many companies still lack clear policies on the use of AI.
Without explicit guidelines, employees don’t know what is allowed and what is not, leading to uncontrolled adoption.
Furthermore, when AI tools are incorporated into existing software (such as Microsoft Teams, Salesforce, or Tableau), employees may be using AI without even realizing it.
Relevant governance data
Recent data on AI adoption and maturity in companies highlight the magnitude of the AI governance challenge that leaders face.
The numbers reveal a complex scenario: while AI adoption has accelerated significantly, organizations’ ability to govern these technologies remains substantially behind.
AI maturity vs. governance capacity
According to McKinsey’s 2026 AI Trust Maturity Survey, the average maturity in Responsible Artificial Intelligence (RAI) increased to 2.3 in 2026, compared to 2.0 in 2025.
However, this progress masks a critical problem: while technical and risk management capabilities advance, organizational governance and oversight structures lag significantly behind. Only about 30% of organizations reach a maturity level 3 or higher in AI strategy, governance, and controls.
This gap is particularly concerning because governance is a direct indicator of an organization’s ability to extract sustainable value from AI.
Organizations that invest $25 million or more in Responsible AI initiatives report significantly higher maturity scores and are much more likely to realize material benefits from AI.
The gap between awareness and execution: the reality of implementation.
One of the most concerning phenomena in AI governance is the gap between risk awareness and effective action. While 98% of organizations expect their AI governance budgets to increase, signaling recognition of the importance of the topic, actual execution remains slow and uneven.
Absent executive leadership
According to the 2026 AI trust survey, only 28% of CEOs directly assume responsibility for overseeing AI governance.
This lack of clear executive leadership is a reliable predictor of governance implementation failure and explains why many governance initiatives fail.
Without executive sponsorship, AI policies end up being implemented in a fragmented and inconsistent manner, often relegated to compliance departments that lack the authority or resources to enforce policies across the organization.
Barriers to implementation: knowledge and training
Knowledge and training gaps are the main barriers to the implementation of responsible AI. This is not surprising: most risk, compliance, and IT professionals have not received formal training in AI governance and will learn as they go, creating a cycle of delay and inefficiency.
Furthermore, the technical nature of modern AI governance requires compliance teams to understand concepts such as model drift, data lineage, explainability, and continuous monitoring—topics that are well outside the traditional scope of compliance.
The reality: accelerated regulation vs. slow preparation.
A critical development in 2026 is the acceleration of AI regulation. With the EU AI Act coming into effect later this year, organizations will face expectations from regulators for verifiable technical evidence, not just verbal assertions about compliance.
This marks the beginning of regulatory maturity for AI, which will accelerate rapidly across all jurisdictions.
One immediate consequence is that the EU AI Act, based on documentation of AI systems inventories, will make this a central compliance function for many EU-based companies.
Organizations will need to demonstrate what types of AI models they use, what data these models are based on, how decisions are made, who is responsible for risk management, and how quality and performance are monitored.
This regulatory framework will challenge many organizations that lack visibility into their AI usage. Consequently, shadow AI becomes a serious risk because compliance will be impossible if organizations don’t know which AI tools their employees are using.
Audit expectations: from verbal to technical
Another critical point in 2026 is the shift in audit expectations. Currently, only the most mature AI teams systematically document their models.
When the new laws come into effect, this will become both the norm and a regulatory requirement.
Audits will now require model cards (documentation of the model architecture, intended use, performance metrics, risks, limitations, and characteristics of the training data) and data lineage (tracing the complete lifecycle of the model data, including sources, transformations, access controls, and usage by the model).
This is especially critical for high-risk AI systems; it’s impossible to guarantee the security or integrity of a model without understanding how data flows through it.
As a result, organizations will need to maintain centralized catalogs of AI models, track versions, document risks, and establish formal governance processes for model changes.
How can leaders monitor and mitigate Shadow AI?
Banning the use of artificial intelligence is not a viable solution to mitigate shadow AI.
These tools are already part of the daily work routine and are likely to become increasingly prevalent. The challenge for managers is to find the perfect balance between innovation and control. A company with AI governance feels secure because it knows exactly which tools are being used, by whom, and with what data.
1. Establish a flexible governance framework.
AI governance should not be an obstacle to innovation. Develop a framework that accommodates the accelerated nature of AI adoption while maintaining safety measures.
This includes clear guidelines on what types of AI systems can be used, how sensitive information should be handled, and what training employees need on AI ethics and compliance.
2. Implement guardrails and access controls.
Simply having a written policy isn’t enough; execution is key. Implement technical guardrails, such as firewalls, to block unauthorized external platforms and secure sandbox environments so teams can test AI applications without exposing real company data.
Companies with greater digital maturity tend to integrate these practices into work management and operational workflows.
3. Monitor usage and educate the team.
Utilize network monitoring tools to track application usage and establish access controls. Additionally, ongoing education is vital. Establish regular communications to inform employees about the risks of shadow AI.
When employees understand the implications of using unauthorized tools, they are more likely to seek approved alternatives or consult IT before adopting new solutions.
4. Define clear KPIs for AI.
Less than 20% of organizations track well-defined KPIs for generative AI solutions. Without clear metrics, it is impossible to measure the impact of AI on production processes or justify investments in security.
Establish measurable goals, such as policy compliance rates, data breach incidents avoided, and audit response times. Companies that work with performance indicators are better able to track process progress and reduce operational risks.
5. Create a responsible adoption program.
Instead of simply blocking tools, offer approved and secure alternatives. Work with AI vendors to implement contracts that ensure compliance with LGPD and GDPR.
Establish a streamlined approval process for new tools, allowing employees to use AI securely without excessive bureaucracy.
Conclusion: Corporate security begins with governance.
Shadow AI is a symptom of a workforce eager for innovation and productivity, operating in an environment where corporate guidelines have not kept pace with technology.
For leaders who feel lost, the way forward is not blind restriction but intelligent governance. By implementing clear policies, technical controls, continuous education, and well-defined metrics, companies can transform the risk of shadow AI into a secure and sustainable competitive advantage.
A company with robust AI governance not only feels secure; it truly is secure. Its data is protected, its operations are aligned with regulations, and its employees have clarity on how to use AI responsibly.
Artificial intelligence will continue to shape the future of work. The question that remains is: will your company be guided by this transformation or will it be overtaken by it?
How to strengthen AI governance with more organized and secure processes.
In this context, platforms like Runrun.it help companies organize workflows, centralize demands, document processes, and increase the operational visibility of teams. This facilitates the creation of more consistent governance over the use of technology and artificial intelligence in day-to-day operations, reducing risks and increasing operational efficiency.
Furthermore, Runrun.it allows the creation of AI Agents directly into the workflow, helping teams automate processes, accelerate operations, and support decisions without losing control, traceability, and operational context.
Agents can operate in different scenarios, such as:
AI Agents in Marketing
- Generate content briefs (blog, social media, email marketing)
- Writing complete texts for posts, articles, or campaigns.
- Create optimized titles and descriptions (SEO)
- Suggest content ideas based on the task theme.
- Review and improve existing texts.
- Adapting language (formal, informal, technical, etc.)
- Translate content into other languages
- Generate an art brief for designers
- Describe creative pieces for campaigns.
- Adjust the tone of your communication according to your audience.
AI Agents for Task Management
- Automatically update task titles and descriptions.
- Moving tasks between workflow stages
- Conduct web research on specific topics.
- Summarize the content of the attached links and documents.
- Extracting insights from long-form materials
- Compare information from different sources.
- Create automatic comments on tasks.
- Generate standard responses for customers or the team.
- Classify tasks as urgent.
AI Agents in HR
- Perform initial screening of resumes. the
- Highlighting candidates who are most aligned with the job opening
- Generate professional profile summaries
- Compare candidates based on defined criteria.
- Create contact or follow-up messages for candidates.
- Generate interview questions
This approach allows for a more structured integration of AI into the company’s routine, avoiding the decentralization typical of shadow AI.
Create your account and try it for free!
FAQ — Shadow AI, Governance, and Artificial Intelligence in Enterprises
What is Shadow AI?
Shadow AI is the use of artificial intelligence tools by employees without company approval, supervision, or governance. This includes platforms such as ChatGPT, Claude, Copilot, Gemini, and other solutions used outside of corporate policies.
Why does Shadow AI pose a risk to businesses?
The main risk lies in the lack of control over data, processes, and decisions. The unmonitored use of AI can lead to leaks of sensitive information, compliance issues with LGPD and GDPR, operational inconsistency, and increased rework.
How can you identify if Shadow AI exists in your company?
Some common signs include frequent use of external AI tools, lack of process standards, difficulty tracking decisions, and inconsistency in team deliverables. Companies with poor operational visibility often have more difficulty detecting this scenario.
Does banning AI tools solve the problem?
No. Artificial intelligence is already part of the corporate routine and is expected to grow in the coming years. The most efficient approach is to create clear policies, establish governance, and offer secure alternatives for the responsible use of AI.
How to create efficient governance for AI?
Effective governance depends on clear policies, monitoring of the tools used, definition of responsibilities, continuous team training, and the structured and secure integration of AI into the company’s workflows.
How does Runrun.it help with AI governance?
Runrun.it helps companies centralize the use of AI by creating native agents that execute tasks within their own workflow. This facilitates activity traceability, process standardization, and control over automation and the use of artificial intelligence.
What is the difference between using AI in a disorganized way and using it integrated into the workflow?
When AI is used without control, each employee ends up creating their own processes, tools, and criteria, increasing risks and inconsistencies. However, when AI is integrated into the operational flow, the company gains traceability, documentation, control over data, and greater efficiency in the execution of activities.
References:
McKinsey – State of AI trust in 2026: Shifting to the agentic era
RIMS – 4 Trends in AI Governance for 2026
IBM Think – What is shadow AI?
Menlo Security – 2025 Report Uncovers 68% Surge in “Shadow” Generative AI Usage
Knostic – The 20 Biggest AI Governance Statistics and Trends of 2025
Kiteworks – IBM 2025: 97% AI Breaches Lack Controls, Shadow AI +$670K
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