Risks of using artificial intelligence in a company
However, along with the benefits of new technology, the risks of using artificial intelligence, which many companies underestimate, are also growing. AI is becoming not only a driver of efficiency but also an object whose use requires mandatory monitoring and protection.
Classification of risks of using AI
It’s important to understand the key risks companies face when implementing and using artificial intelligence. Most commonly, these are:
- information security risks;
- legal and regulatory risks;
- reputational risks;
- operational and management risks;
- personnel and organizational risks.
Information security risks
All artificial intelligence models operate with data, which means the potential attack surface for the information being used automatically expands. In this case, the key threat posed by AI to business lies in the lack of control over the information transfer channels through which data leaves the company’s managed boundaries.
For example, when using AI services, employees may submit fragments of contracts, client databases, financial indicators, technical documentation, or correspondence in requests. Without centralized control, such actions go unnoticed, and the data effectively leaves the security perimeter, where the company loses control and cannot guarantee its confidentiality or subsequent deletion.
Important: An additional risk is the unauthorized use of corporate information for model training. Without strict access policies and filtering of information transmission channels, this creates a risk of leakage of trade secrets and personal data.
Legal and regulatory risks
When using AI models, personal data of employees, partners, or clients may be sent along with the general information pool. This violates legal requirements regarding the non-dissemination of such information. Further dissemination of personal data by the AI model may even lead to regulatory restrictions and fines.
Reputational risks
Even a technically sound model can cause serious reputational damage to a company. In practice, reputational risks associated with AI implementation can manifest themselves in several scenarios:
- generation of discriminatory, incorrect, or ethically unacceptable responses by the model;
- publication of false or misleading statistical information and research on behalf of the company, including in client communication channels;
- Unauthorized disclosure of customer and partner data when using AI tools.
If such incidents are discovered in a public and open business environment, they can negatively impact the company’s reputation and the perception of employee expertise.
Operational and management risks
A common mistake in the application of artificial intelligence is that the technology is implemented piecemeal, without monitoring its use, and by employees themselves. In business management, this creates additional burdens, as AI implementation fails to consider all stages of management processes, and parts or entire processes are replaced by automated decisions without proper oversight. This leads to blind reliance on the conclusions of AI models without critical evaluation by responsible employees, and a decline in the quality of management decisions.
As a result, the risks of using artificial intelligence extend beyond information technology and information security and directly impact operational resilience, business continuity, and the quality of management decisions.
Personnel and organizational risks
Most often, these are presented as a shortage of specialists capable of evaluating the work of AI, uncontrolled shadow use of AI by employees, and a decrease in personnel responsibility for decisions made.
Understanding the risks helps companies move from the chaotic use of artificial intelligence to systemic management that combines security and the benefits of advanced technology.
Key risk scenarios when using AI
The risks associated with using artificial intelligence in business stem from the typical operating algorithms of these services. Users often don’t understand how each specific model uses data, where it’s stored, and to whom it’s ultimately accessible.
| Risk scenario | Risk Description | What data is at risk? | Potential impact on business |
|---|---|---|---|
| Transferring confidential information to public AI services | Employees using public AI tools outside the corporate perimeter without control over data transmission and storage channels | Personal data, trade secrets, financial indicators, results of internal discussions, and intellectual property | Uncontrolled data leaks, loss of privacy, reputational damage, and violation of information security policies |
| Training models on sensitive data | The possibility of using user data directly or indirectly to further train AI without transparency from the provider | Personal data, industry and commercial information, sensitive business data | Regulatory sanctions, legal liability, and violation of industry compliance requirements |
| Loss of control over data storage and reuse | Lack of information about the storage location, timeframe, access, and reuse of data transferred to AI services | All categories of transferred data, including request logs and cached responses | The emergence of “invisible” leak points, data going beyond the controlled perimeter |
| False answers and false recommendations | AI is generating logical but erroneous conclusions due to hallucinations, data distortion, or a lack of industry context | Management, financial, legal, and operational data | Making wrong decisions, financial losses, and strategic miscalculations |
| Lack of transparency and explainability of models (Black Box) | The inability to understand the logic behind AI decisions and verify their validity | Solutions in financial, legal, personnel, and management processes | Lack of auditability, difficulties with regulatory compliance, dilution of responsibility, and growth of uncontrolled risks |
Why is this dangerous for business?
The use of AI is dangerous not in itself, but when combined with a high level of trust and a lack of human oversight. Without mandatory verification of results, restrictions on the use of the technology in critical scenarios, and monitoring mechanisms, a company risks making strategic decisions based on unreliable or unverified AI-generated information, without realizing the scale of the threat.
Strengthening Cyber Threats with AI
Artificial intelligence is radically transforming the cyberthreat landscape. While AI was previously viewed primarily as a tool for enhancing defense effectiveness, it is now becoming a fully-fledged amplifier of attack capabilities. Attackers are using AI to increase the speed, accuracy, and scalability of attacks, leading to a qualitative shift in the threat model and a reduction in the effectiveness of traditional defenses.
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A New Level of Generic Phishing and Social Engineering
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Automated attacks and lowering the barrier to entry for attackers
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Supply Chain Attacks and the Risks of AI Code Generation
How to Manage the Risks of AI Applications
Effective risk management of artificial intelligence requires a systematic approach that views AI not as an isolated IT tool, but as part of business processes and information security frameworks.
To manage the risks of using AI, it is necessary:
- Consider AI systemically, not as a separate IT tool, but as part of business processes and the information security framework, integrated into the overall risk management model.
- Formalize the rules for using the technology through a corporate information security policy that defines the purposes of use, areas of responsibility, and data protection requirements.
- Establish permitted and prohibited use cases. Provide a clear definition: what types of data can be transferred to AI systems, in which business processes the use of AI assistants is permitted, and where mandatory human involvement and additional verification of results are required.
- Monitor information transmission channels. Use DLP systems and behavioral analytics to detect data leaks, bypass security policies, and mitigate the risks of “shadow AI.”
- Choose a secure AI architecture, prioritizing on-premises or enterprise AI models deployed within a secure perimeter.
- Address the human factor through employee training, awareness raising, case studies, and regular reminders about the risks and rules of using AI.
- Integrate AI into security management, transforming technology from a source of risk into a manageable tool that improves business efficiency without compromising information security.
Conclusion
Artificial intelligence is now applicable to almost every area of business. Therefore, to utilize this modern technology, it is necessary to establish an effective and secure AI risk management framework that is beneficial to the enterprise.
