AI models continuously learn from data, which they collect from various sources. The growing presence of artificial intelligence is not just a matter of efficiency and innovation. But did you know that cybercriminals are now adopting new strategies? Instead of directly attacking people or systems, they are increasingly distorting the data sources that AI models rely on for learning.
This new approach results in AI systems transmitting misleading information, which can have severe business and strategic consequences. This phenomenon is particularly dangerous in the corporate world, where more and more decisions are based on AI-driven insights.
Companies that use AI-powered decision-support systems can become vulnerable to manipulation if they fail to ensure the integrity of the data they feed into these systems—this is known as data poisoning. Attackers can create fake market trends that lead businesses to make poor investment decisions. Similarly, if customer data is distorted, a company may misinterpret its consumers’ needs or risks. It is worth considering that even without AI, a single employee manipulating an Excel sheet can have serious consequences. There are numerous solutions to this issue, particularly anomaly detectionand blockchain-based data protection.
It is crucial to understand that anomaly detection aims to identify irregularities in network traffic or system operations—signals that may indicate security incidents or data quality issues. Fortinet offers advanced AI and machine learning-based solutions in this field. These technologies can continuously monitor network traffic, detect unusual patterns, and alert system administrators in real time to potential threats.
This issue ties into the question of what kind of corporate culture is capable of effectively integrating and protecting AI-based solutions. Organizations that develop robust cybersecurity strategies—not just for their AI systems—can gain a significant competitive advantage. Cybersecurity is not merely about technological advancements; it is also about ensuring that companies understand risks and implement strategies to reduce the chances of data manipulation. The NIS2 framework can be a valuable tool in achieving this goal.
Ensuring data quality is not just a technical issue but a strategic priority. This requires data security protocols that guarantee the reliability and integrity of information. If a company fails to pay attention to proper data management and protection, it does not matter whether data-driven decisions are distorted by AI or a malicious employee—by the time the issue is discovered, the company may already have suffered significant losses.
AI is not a magic tool—it is a technology whose efficiency and reliability depend heavily on the quality of the data it uses and the security mechanisms that protect its integrity. However, achieving this requires proper training and awareness.
How AI is Already Transforming Workflows
A study conducted among AI users highlights that AI is no longer just a tool for text generation or data analysis—it now supports increasingly complex business processes:
- 85% use AI to start their workday—e.g., generating email summaries, prioritizing daily tasks.
- 85% use AI to prepare for the next workday, leading to better scheduling and task management.
- AI is 51% more likely to be used for information analysis, such as filtering and evaluating large volumes of text data.
- AI is 49% more commonly applied for customer analysis and processing feedback, enabling businesses to respond to market needs faster and more accurately.
These examples demonstrate that AI is not just a helpful tool—it is a strategic asset that can bring profound changes to a company’s operations, but only if the data is reliable.
Leadership Support is Key
According to research, early AI adopters are:
- 61% more likely to hear about AI’s importance from company owners or senior executives.
- 40% more likely to receive guidance from direct supervisors on how to use AI.
- In organizations where leaders actively promote AI integration, employees are 53% more likely to feel that AI can genuinely transform their workflows.
What Factors Contribute to the Successful Integration of AI in Businesses?
According to experiences from PwC, KPMG, and EY, the corporate culture and leadership strategy that support AI adoption include the following elements:
✅ Innovation-driven culture – Companies should encourage the adoption and application of new technologies.
✅ Strategic planning – AI implementation must be aligned with business goals and strategies.
✅ Ethical application – Ensuring AI is used responsibly and adheres to data protection regulations.
✅ Trust-building and human-centric approach – Leaders should support employees in using AI and provide necessary training and resources.
Developing and continuously monitoring data security and data quality strategies is, therefore, a critical activity. Implementing AI will be a complex data management challenge involving data quality control, security assurance, and protection against potential data manipulations. Future competitive advantage will not only come from using AI but from ensuring the accuracy, reliability, and security of the data businesses rely on.
NIS2 Compliance – A Practical Guide for Companies
The experts at www.oditsolutions.hu support companies with a „NIS2 Compliance” guide, which will soon be available online. This document helps businesses navigate compliance requirements and demonstrates how to turn obligations into competitive advantages through real-world examples.
📢 Stay tuned for the upcoming study in the Wolters Kluwer Hungary ESG supplement, along with a downloadable guide for further assistance!