Telecom networks have always been over the target of cyberattacks, and this statistic demonstrates that it’s growing – there has been a staggering 12% rise in fraud losses in 2023 compared to 2021, with a massive amount of $38.95 billion lost globally.
This escalating problem asks for a solution. Artificial Intelligence integrates into telecom networks as technology grows, transiting the telecom security landscape. While AI brings colossal potential for network optimization and service innovation, it also introduces new vulnerabilities and challenges.
Explore this blog to gain insights into evolving risks in the telecom sector and discover five effective strategies for telcos to employ AI in combating fraud and ensuring sustained profitability.
The Evolving Threat Landscape in Telecom
Generally, telecom networks have always been prone to security threats like malware attacks, data breaches, and hacking. Attackers steal customer data, damage infrastructure, and launch attacks on other systems by targeting these networks.
The increasing value of telecom data for attackers is a growing concern. Telecom networks carry vast customer information, call records, and internet traffic data. This data can be incredibly valuable for attackers, who can utilize it for identity theft, fraud, or targeted attacks.
Although AI benefits telecoms, at the same time, it adds a new layer of complexity to the security situation. So, it asks for precise and careful security management. Telecom companies can enforce solid security measures to protect training data utilized for AI models. This can encompass data encryption, access controls, and regular audits, ensuring data integrity and preventing unauthorized modification.
5 Proven Strategies for Telecom Security in the AI Age
Telecom companies must adopt a proactive approach in this ever-changing threat landscape. Here are five proven strategies that can support them in leveraging AI for enhanced telecom security and profitability.
AI for Threat Detection and Prevention
AI can be a powerful way to identify and mitigate security threats. With AI-powered fraud management solutions, enterprises can evaluate vast network data to find anomalies that may indicate a potential issue. AI analyzes network traffic patterns and identifies suspicious activities such as:
Unusual Call Patterns
AI uses algorithms that detect if the user starts making more international calls than normal or calls to high-risk destinations, which deviates from their smartphone usage’s normal behavior. It may provide evidence of SIM Box fraud with its perpetrators transferring calls through illegally operated “SIM boxes” that bypass the legal termination charges.
CLI Spoofing
AI may examine intercepted call metadata to find a spoofed associated call. This happens when the voice matches the recorded name when making a call and prevents impersonators from getting users to answer the call or divulge their data.
Phishing Attempts
AI can scrutinize users' email and SMS content by understanding their language patterns and vocabulary to spot phishing activities that aim to leak out private information.
By continuously monitoring network activity and identifying these anomalies, AI-powered telecom fraud management systems can help prevent financial losses and protect customers from falling victim to scams.
Secure the AI Supply Chain
The security of AI-powered telecom IT solutions is only as strong as the security of the total development lifecycle. This includes securing the algorithms, the data used to train them, and the presently running infrastructure. Telecom companies must ensure that renowned vendors with robust security practices create AI algorithms for their use.
Additionally, the data should be vetted carefully, and it should be used to train AI models so that they are free from vulnerabilities and any kinds of biases. The AI systems' infrastructure should be secure and hardened to fight potential attacks.
AI-powered User Authentication
Increasingly, conventional user authentication methods like passwords are proving vulnerable to hacking, posing significant risks to security despite their widespread use.With AI, more secure and easy authentication methods can be created to resist attacks. Multi-factor authentication (MFA) is one such powerful method that AI can further enhance.
AI can dynamically adjust authentication requirements for MFA depending on user behavior, location, and device. AI can be utilized to authenticate users through facial or voice recognition. Moreover, with the help of AI, telecom companies can find anomalies in login attempts, reducing the risk of unauthorized access.
Continuous Security Monitoring
As the cybersecurity landscape evolves, so do the threats. This is a significant reason why constant network monitoring practices are essential for telecom companies. It involves monitoring the networks for improved performance, finding vulnerabilities, and safeguarding from suspicious activity.
AI can be a valuable medium for constant security monitoring. It can automate security tasks and free humans for more complex issues. Telecom companies can stay ahead of the competition by continuously monitoring their security posture and adapting their strategies accordingly. For instance, AI systems can be programmed to take automated actions - in response to detecting frauds, like suspending compromised SIM cards and preventing further loss.
Employee Security Awareness Training
Although AI-powered solutions are the most advanced, the human element remains crucial in telecom security. Employees unaware of security risks can unknowingly introduce vulnerabilities into a network.
Telcos must invest in comprehensive security awareness training for all their employees. This training should educate employees on the latest security threats, including those specific to AI. Additionally, it should teach employees best practices for secure behavior, such as recognizing phishing attempts and using strong passwords.
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