TrustPath | 5 hidden AI risks every Telecom needs to address now

Key takeaways:

Along with numerous benefits AI brings to the telecom industry, it also introduces several risks, including regulatory fines, legal liability, reputational damage, loss of customer trust, operational, and financial challenges.
In addition to these risks, there are hidden risks that telecom companies often discover only after adopting AI, which can be too late, as damage may have already occurred.
The hidden risks include AI model drift, AI bias, data privacy issues, adversarial attacks, and AI-driven network failures. However, all of these risks can be identified, prevented, or at least minimized.
Therefore, it is crucial to carefully evaluate AI vendors during the procurement process to minimize these risks as much as possible. Alternatively, you can reach out to us, because we can help you with this evaluation.

AI is everywhere and is penetrating almost all industries.

In our previous blog article The AI Revolution in Telecom: Maximizing Benefits While Mitigating Risks, we wrote about the most common ways telecom companies use AI in their operations to increase operational efficiency, automate processes, and leverage the most advanced technological achievements to achieve their business goals. You can read the full article on the link, and below, we will list the most common ways AI is used in telecoms:

for smart optimization and forecasting
for enhancing customer experience
for network optimization and self-healing
for fraud detections
for marketing and enhancing conversion rates

Likewise, we have learned that, along with all the benefits AI brings to telecom companies, it also comes with numerous risks. Telecom companies may face regulatory fines, legal liability, reputational damage, loss of customer trust, as well as operational and financial challenges.As a continuation of the topic of AI-related risks in the telecom industry, in this blog article, we will cover five hidden AI risks that every telecom operator must be aware of. These risks are not obvious, and many companies encounter them only after integrating AI - at which point, in many cases, it is too late, and enterprise companies have already suffered damage.A notable example is the case involving Lingo Telecom in 2024. The company transmitted AI-generated robocalls that mimicked President Joe Biden’s voice, urging New Hampshire voters to skip the Democratic primary. This misuse of AI technology led to a $1 million fine imposed by the Federal Communications Commission (FCC) on Lingo Telecom.Therefore, it is very important to understand all aspects of the risks AI brings, choose a secure and compliant AI vendor, and make the most of the benefits AI offers in the telecom industry. And we will help you with that. Let’s get started.

#1 - AI Model Drift

It is well known that AI models are trained on past events, meaning historical data. However, reality is different, and the real-world environment changes rapidly. Consumer habits and behaviors also evolve, and if AI models do not keep up with these changes, they quickly become inaccurate, providing incorrect information or behavior that does not meet user expectations. This phenomenon is called AI Model Drift, a type of quality degradation in AI caused by outdated training data.

How Can AI Model Drift Affect Telecom?

To better explain AI model drift in telecom, here are a few examples of how it can negatively impact telecom operations:

Network traffic prediction – as mentioned earlier and in our previous blog article, telecom companies often use AI to optimize network traffic. However, user data consumption habits change daily. For example, during the pandemic, data traffic was much higher due to lockdowns, as people spent more time at home. Now, the situation is different. This trend is crucial for telecom companies, and if they use AI to optimize network traffic, it is essential to update AI with the latest trends. Otherwise, AI might make incorrect optimizations, affecting the quality of service provided to customers.
Chatbot accuracy in customer interactions – most telecom companies use AI chatbots to improve customer support efficiency. If the AI model is not updated with new information about current offers, complaint resolution processes, or general updates within the telecom company, the chatbot may provide incorrect answers to customers, causing confusion and potential customer dissatisfaction.
Fraud prevention – as technology advances, fraud methods also evolve. If the AI model is not updated with the latest fraud techniques and prevention strategies, fraudsters can continue their activities, leading to significant financial and operational damage to telecom companies.

How to Avoid AI Model Drift Risks in Telecom?

‍There are several ways to minimize AI model drift in telecoms, including:

Continuous monitoring of AI models, which can be automated using various tools that track performance and detect behavioral changes in AI.
Regular model retraining, ensuring that the AI always has access to the latest data and trends.
Human oversight, which requires hiring experts to monitor AI performance and intervene when necessary.

#2 - AI Bias

AI models are trained on large datasets. If these datasets contain certain biases, AI may consider them correct and apply them in its operations. This means that AI does not create bias intentionally but rather learns it from the data. Therefore, it is very important to prevent bias to avoid potential harm.

How Can AI Bias Affect Telecom?

AI bias in telecom can appear when AI treats users based on race, gender, religion, nationality, or social status. Examples of AI bias in telecom include:

Prioritization of customer support – AI models may analyze historical data and favor certain groups of users based on their race, religion, or name, giving them faster access to customer support or connecting them more quickly with human agents.
Providing better services to certain groups – AI models may optimize network performance better in certain areas of a city or country, giving some groups better mobile network or internet services.
False fraud alarms – AI models may generate false fraud alerts based on a user’s name or location, preventing legitimate users from accessing the network or resolving their issues.

How to Avoid AI Bias Risks in Telecom?

‍Although bias has long been a major focus for AI model providers and is kept on being eliminated, companies should still implement the following:

Continuous bias testing – regularly evaluating AI model behavior to detect potential bias.
Control of training data – ensuring that datasets do not contain elements that could lead to AI bias.
Improving AI models – updating AI with the latest data or fine-tuning with new information.

#3 - Risks Related to Data Privacy

Since telcos are massive companies, the AI systems they integrate also handle large amounts of sensitive historical data, call recordings, request histories, and similar information. Any uncontrolled data leak can lead to serious problems for telecom companies.

How Can Data Privacy Affect Telecom?

As mentioned earlier, telecom companies manage vast amounts of personal data and have significant responsibilities toward both users and regulators. Potential problems include:

Non-compliance with legal frameworks – laws such as GDPR and CCPA strictly require personal data protection.
Unauthorized data sharing – AI systems may unintentionally share personal data during user interactions.
Increased risk of cyberattacks – when there is data, there is also a higher risk of cyberattacks. AI models are not perfect and have vulnerabilities that can be exploited.

How to Avoid Risks Related to Data Privacy?

‍We all understand how important privacy is, especially for enterprise companies like telecoms that manage millions of personal data records. To prevent data privacy issues, the following solutions should be applied:

Mandatory implementation of regulatory principles – ensuring maximum data protection in line with legal requirements to prevent potential damage.
Protecting data from AI – anonymizing data before sharing it with AI.
Data access controls – restricting access to data and preventing unauthorized use.

#4 - Adversarial attacks

AI models can be deceived, manipulated, and exploited using adversarial attacks, where hackers feed misleading information to AI models to force incorrect decisions.

How Can Adversarial Attacks Affect Telecom?

For telecom companies that use AI systems for fraud prevention, network optimization, or user security, adversarial attacks are one of the fastest-growing threats AI brings. Examples of adversarial attacks in telecom include:

Fraudulent bypass attacks – Hackers can manipulate fraud detection systems and convince AI to approve unauthorized transactions or activate SIM cards used for fraud.
Compromised network security – Hackers can trick AI into reducing network security restrictions, making it easier to penetrate the network and cause damage to telecom operations.
AI chatbot manipulation – Hackers can manipulate AI chatbots to disclose confidential information or perform unintended actions.

How to Avoid Risks Related to Adversarial Attacks?

‍As these risks continue to grow and become a major focus for hackers, protection methods are also evolving. Some ways to minimize adversarial attacks include:

Training AI models to recognize such attacks or integrating third-party tools that prevent adversarial attacks.
Regular AI security audits, such as penetration testing on AI-managed systems.
Real-time anomaly detection to continuously monitor system performance and security, minimizing potential damage if an attack occurs.

#5 - AI-driven network failures

As mentioned earlier, telecom companies use AI to optimize networks to provide the best user experience while improving network efficiency and saving money. However, AI is not perfect. Over-reliance on AI can lead to serious consequences if it is not under constant human supervision, ensuring real-time corrections for AI mistakes or oversights.

How Can AI-Driven Network Failures Affect Telecom?

There are many ways AI-powered systems can disrupt the normal functioning of a network, but here are a few examples.

Network downtime or performance limitations – AI may incorrectly optimize network bandwidth, relieving congestion in one area while overloading another. This can lead to complete network outages or reduced service quality for all or certain users.
False positive anomaly detection – We are all familiar with false positives, and in telecom, AI may misinterpret a false anomaly as real, reconfiguring the network to fix a system that is actually working fine. This could cause temporary network downtime.
Over-reliance on AI – AI is not perfect and cannot replace human judgment and experience. Some situations require human assessment, which AI cannot provide, leading to potential new network issues.

How to Avoid Risks Related to AI-Driven Network Failures?

‍Although AI helps optimize networks efficiently, every system makes mistakes, including AI. To minimize or completely avoid these issues, telecom companies should implement:

Mandatory human involvement in decision-making, ensuring AI does not make service-related decisions autonomously.
Security mechanisms that limit AI actions, preventing AI decisions from causing critical damage to infrastructure or the network.
Scenario-based AI training, where AI is trained to recognize situations that require human intervention for proper assessment and action.

The Future of AI in Telecom Companies

To conclude, AI brings numerous benefits to the telecom industry—saving money, increasing operational efficiency, and improving customer experience.

However, like any other technology, AI has its limitations that must be recognized and controlled. Some risks require only human supervision, while others need additional tools to monitor the system.

When choosing the right AI vendor, make sure to select one that can deliver the expected service quality and is compliant with legal regulations. We have already written about this in our article “Spotting red flags: How to evaluate AI vendors and avoid costly mistakes”.

If you need help in selecting an AI vendor, TrustPath can assist you in evaluating and choosing the best one. Contact us, and we will be happy to help!

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