Building Trust in Smart TVs: AI-Enhanced Cybersecurity for User Privacy and Ethical Monetization

Abstract

As Smart TVs evolve into central hubs for IoT ecosystems, ensuring user trust through robust cybersecurity and ethical monetization practices has become paramount. This paper explores the integration of AI-driven cybersecurity features into Smart TVs, enabling them to safeguard user privacy and secure connected devices such as thermostats, smart speakers and home automation systems. By leveraging advanced AI techniques, including anomaly detection, behavioral analytics and federated learning, Smart TVs can monitor network traffic, detect vulnerabilities and mitigate potential cyber threats in real-time. For example, these systems can proactively identify and block IoT-based botnet attacks like Mirai, preventing unauthorized access to home networks. Additionally, AI-driven device typing enables Smart TVs to accurately classify and optimize the performance of connected devices, enhancing interoperability and user experience. The transformation of Smart TVs into trusted IoT hubs also presents significant monetization opportunities for manufacturers. Ethical monetization strategies, such as offering premium AI-powered security subscriptions, personalized automation services and bundled IoT device packages can generate revenue while prioritizing user trust. Privacy-preserving AI techniques such as federated learning and edge computing ensure that insights are monetized without collecting raw user data. Cross-selling and upselling opportunities arise as manufacturers integrate Smart TVs with complementary smart home products, fostering a seamless, secure ecosystem. Additionally, partnerships with cybersecurity firms and IoT developers further expand revenue streams, ensuring sustainable growth. Unlike traditional IoT security solutions, AI-powered Smart TVs provide native, real-time protection without requiring additional hardware, positioning them as the next frontier in home cybersecurity. As the industry advances, embedding privacy by design principles and offering users greater control over their data will be crucial in maintaining trust. This paper highlights how AI-enhanced cybersecurity and responsible monetization can redefine Smart TVs as both intelligent home automation hubs and ethical revenue generators, ensuring security, privacy, and user satisfaction while driving industry growth.

Headnote

Abstract: As Smart TVs evolve into central hubs for IoT ecosystems, ensuring user trust through robust cybersecurity and ethical monetization practices has become paramount. This paper explores the integration of AI-driven cybersecurity features into Smart TVs, enabling them to safeguard user privacy and secure connected devices such as thermostats, smart speakers and home automation systems. By leveraging advanced AI techniques, including anomaly detection, behavioral analytics and federated learning, Smart TVs can monitor network traffic, detect vulnerabilities and mitigate potential cyber threats in real-time. For example, these systems can proactively identify and block IoT-based botnet attacks like Mirai, preventing unauthorized access to home networks. Additionally, AI-driven device typing enables Smart TVs to accurately classify and optimize the performance of connected devices, enhancing interoperability and user experience. The transformation of Smart TVs into trusted IoT hubs also presents significant monetization opportunities for manufacturers. Ethical monetization strategies, such as offering premium AI-powered security subscriptions, personalized automation services and bundled IoT device packages can generate revenue while prioritizing user trust. Privacy-preserving AI techniques such as federated learning and edge computing ensure that insights are monetized without collecting raw user data. Cross-selling and upselling opportunities arise as manufacturers integrate Smart TVs with complementary smart home products, fostering a seamless, secure ecosystem. Additionally, partnerships with cybersecurity firms and IoT developers further expand revenue streams, ensuring sustainable growth. Unlike traditional IoT security solutions, AI-powered Smart TVs provide native, real-time protection without requiring additional hardware, positioning them as the next frontier in home cybersecurity. As the industry advances, embedding privacy by design principles and offering users greater control over their data will be crucial in maintaining trust. This paper highlights how AI-enhanced cybersecurity and responsible monetization can redefine Smart TVs as both intelligent home automation hubs and ethical revenue generators, ensuring security, privacy, and user satisfaction while driving industry growth.

Keywords: AI-powered cybersecurity, Smart TVs, IoT security, Anomaly detection, User privacy

1. Introduction

The Smart TV industry is undergoing a rapid transformation, driven by evolving consumer expectations, technological advancements and intensifying market competition. The global smart tv market size was valued at $202.8 billion in 2023, and is projected to reach $497.3 billion by 2033, growing at a compound annual growth rate (CAGR) of 9.5% from 2024 to 2033 (alliedmarketresearch.com, 2024).

This rapid market expansion underscores the increasing consumer demand for connected, intelligent home entertainment solutions. Once passive entertainment devices, Smart TVs have become AI-powered hubs, integrating seamlessly with IoT ecosystems to enhance automation, personalized content experiences and smart home management. With the rise of voice-controlled interfaces, edge computing and high-speed internet connectivity, Smart TVs now act as centralized control points for connected homes, managing devices such as thermostats, security systems and lighting.

However, this increased connectivity has also expanded the attack surface for cyber threats. The rapid proliferation of IoT devices has significantly escalated cybersecurity risks, with billions of attempted attacks recorded annually. In 2023 alone, over 1.5 billion IoT attacks were reported (Kaspersky, 2023) and early trends in 2024 and 2025 indicate a continued rise in sophisticated threats, including AI-driven malware and large-scale DDoS attacks (World Economic Forum, 2024; securityscorecard, 2025). As IoT adoption expands across critical industries, cybersecurity experts anticipate a surge in exploits targeting unsecured devices, demanding advanced AI-driven defense mechanisms.

Smart TVs, which collect and process vast amounts of user data, are increasingly targeted for malware intrusions, unauthorized access, and network breaches. Traditional security mechanisms embedded in Smart TVs such as secure boot mechanisms and application sandboxing offer partial protection but often fail to detect and mitigate real-time threats at the network level.

Alongside security concerns, monetization remains a key challenge. While Smart TV manufacturers generate revenue through hardware sales, the broader smart media ecosystem, particularly content providers and platform operators relies heavily on ad-supported models and premium content subscriptions. These practices often raise concerns regarding data privacy and user consent. Ethical monetization strategies, such as premium security subscriptions, AI-driven automation services and contextual advertising that respects user privacy, can create sustainable revenue streams while maintaining consumer trust.

This paper explores the intersection of AI-driven cybersecurity, real-time IoT protection and ethical monetization in Smart TVs. By embedding AI-powered security measures directly at the Smart TV level and integrating adaptive revenue models, manufacturers can not only safeguard user data but also drive long-term profitability and consumer confidence.

2. Commercial Landscape

The Smart TV market has seen significant innovation in recent years, with leading manufacturers such as Samsung, LG, Sony and TCL leveraging AI-driven content recommendations, voice assistants and IoT integrations to enhance user experience. By 2025, Smart TVs are expected to account for the vast majority of global TV shipments, with projections indicating that over 87% of all TVs will feature smart capabilities (Statista, 2025). Early industry trends suggest continued growth, driven by advancements in AI-powered content recommendations, seamless streaming integration and enhanced IoT connectivity. As consumer demand for intelligent, connected entertainment solutions rises, Smart TVs are becoming the default choice, transforming digital media consumption worldwide.

Despite advancements, Smart TV security implementations remain fragmented across different platforms. Samsung's Tizen OS and LG's webOS incorporate basic security frameworks, such as application sandboxing and secure boot mechanisms, to guard against malware. Sony's Android TV benefits from Google Play Protect, which scans applications for threats but lacks real-time, network-level threat detection. Meanwhile, AI-powered features like LG ThinQ AI and Samsung's Smart Hub focus primarily on content personalization, with security remaining a secondary concern rather than an integrated feature.

These solutions primarily address isolated security challenges such as app-level protection and malware detection but fail to offer real-time, adaptive security measures at the Smart TV level. Current security architectures do not proactively analyze network behavior, identify IoT threats or dynamically adjust protections based on user activity. Furthermore, existing monetization model's ad-based revenues, premium subscriptions, and data-driven advertising often raise concerns about privacy violations and user data exploitation.

To bridge these gaps, this paper proposes an Al-enhanced security framework that integrates real-time threat detection, device typing and adaptive cybersecurity protocols directly at the Smart TV level. By embedding Alpowered security analytics and privacy-first monetization models, Smart TV manufacturers can mitigate cybersecurity threats through real-time anomaly detection and automated threat response, ensuring continuous protection against malware, unauthorized access and network intrusions. Additionally, the framework enhances IoT interoperability by enabling secure device authentication and management, allowing seamless yet protected integration with other smart home devices. While some OEMs have introduced premium security subscriptions as value-added services, ethical considerations and emerging regulations such as the EU Cyber Resilience Act - CRA, 2024 (Insights, 2024) underscore the need for baseline security features to be universally accessible and free of charge. Within this context, ethical monetization requires a clear distinction: foundational protections must be guaranteed, whereas advanced offerings, like AI-powered automation, privacy-enhancing features or contextual advertising may be monetized responsibly. This holistic approach ensures that Smart TVs remain not only secure and regulation-compliant but also commercially sustainable in an increasingly connected digital landscape.

By addressing these gaps, the Smart TV industry can evolve beyond entertainment into secure, AI-driven smart home ecosystems, ensuring long-term consumer trust and sustainable business growth.

3. Essential Features of Al-Enhanced Cybersecurity in Smart TVs

As Smart TVs become an integral part of connected homes, they also present new cybersecurity risks due to their access to streaming services, IoT networks and voice assistants. Advanced cybersecurity software is essential for protecting these devices from cyber threats, ensuring secure data transmission and enhancing user privacy.

Smart TV security solutions incorporate multiple advanced features to enhance protection, privacy and user experience.

* AI-powered threat detection and response continuously analyze network traffic, identifying anomalies that signal cyber threats such as malware, unauthorized access, or suspicious communications.

* Use Case: For instance, if a Smart TV starts interacting with an untrusted IP, the system flags the activity, blocks the connection and alerts the user. Secure streaming with end-to-end encryption (E2EE) ensures that all communications between Smart TVs, streaming platforms and IoT devices are encrypted using TLS and DTLS protocols, preventing hackers from intercepting streaming data or personal credentials when accessing platforms like Netflix or Amazon Prime.

* Use Case: Device fingerprinting and behavioral analytics detect unauthorized device behavior by analyzing hardware IDs and network usage patterns. İfan attacker clones a Smart TV's credentials to access premium content, AI identifies the anomaly and restricts access.

* Enhanced Parental Controls: By leveraging AI insights, enhanced Parental controls feature can be offered.

* Use Case: Dynamically adjust content restrictions based on usage patterns, ensuring a safer viewing experience for children by blocking inappropriate streaming content and setting screen time limits.

* Use Case: Intrusion prevention and network segmentation enforce access controls, allowing Smart TVs to communicate only with authorized devices and applications. For example, if a compromised smart camera attempts to access Smart TV data, AI-driven security mechanisms prevent the intrusion.

* Automated patch management and vulnerability scanning proactively scan firmware and installed apps for security flaws, applying necessary patches.

* Use Case: If a streaming app vulnerability is detected, the system prompts the user to update or install the patch automatically.

* Al-enhanced smart home security integration secures Smart TVs as command centers for home automation by blocking unauthorized attempts to access connected devices. Similarly, least privilege access control restricts Smart TVs from interacting with untrusted external applications and services, preventing unauthorized data leaks.

* Use Case: Multi-factor authentication (MFA) strengthens security by requiring users to verify their identity before modifying administrative settings or logging into apps.

* To ensure privacy protection and compliance with global standards, Al-enforced security mechanisms uphold regulations like GDPR and CCPA, preventing unauthorized data collection by third-party trackers. AI-driven anomaly detection and suspicious activity monitoring continuously track device behavior, identifying irregular patterns indicative of security breaches.

* Use Case: For instance, if a Smart TV exhibits excessive outbound traffic, the system investigates and takes corrective action.

* Al-optimized Quality of Experience (QoE) for streaming services dynamically adjusts bandwidth allocation, prioritizing high-demand applications to reduce buffering and ensure seamless 4K streaming.

These integrated security features provide a robust cybersecurity framework for Smart TVs, safeguarding users against evolving digital threats while optimizing performance.

4. AI Algorithms in Cybersecurity

AI-driven cyber security algorithms help mitigate risks by enabling real-time threat detection, adaptive security policies, and enhanced privacy protection. The AI-driven cybersecurity solutions proposed in this white paper leverage machine learning (ML) and deep learning (DL) algorithms to detect and counter threats in real time. Neural networks for anomaly detection play a crucial role in identifying suspicious activities. Convolutional Neural Networks (CNNs) analyze network traffic patterns, detecting anomalies such as sudden spikes in data usage or unauthorized access attempts. For example, CNNs can differentiate between normal high-definition video streaming and potential DDoS attacks. Recurrent Neural Networks (RNNs) process time-series data, identifying unusual traffic patterns indicative of botnet activity or data exfiltration. If a Smart TV continuously communicates with an unknown server, RNNs flag the behavior for review.

Deep learning for threat classification enhances cybersecurity by improving threat detection accuracy. Deep Neural Networks (DNNs) classify cyber threats by analyzing packet headers and payloads, distinguishing between legitimate streaming requests and potential attacks, effectively preventing credential stuffing attacks on streaming service accounts. Generative Adversarial Networks (GANs) simulate cyberattacks to train AI models for recognizing real-world threats, such as emerging malware targeting Smart TV applications.

Adaptive security and phishing detection further strengthen Smart TV defenses. Q-learning enables AI to dynamically adjust security policies in real time based on detected threats. For instance, when a Smart TV identifies a vulnerable IoT device, it can automatically isolate it to prevent potential exploitation. BERT-based NLP models analyze voice and text-based interactions to detect phishing attempts, preventing scams that disguise themselves as legitimate subscription requests. By integrating these AI-driven techniques, Smart TVs can proactively counter cyber threats, ensuring a safer and more secure user experience.

5. Technical Challenges and Risk Mitigation

AI-driven cybersecurity systems face several challenges that require careful mitigation strategies to ensure effectiveness and reliability.

* False positives and algorithmic bias can lead to misclassifications where normal behavior is flagged as a threat, causing unnecessary disruptions. Continuous model training with diverse datasets helps improve accuracy and reduce false positives.

* Resource constraints and processing overhead present another hurdle, as Smart TVs have limited computational capacity, making it difficult to implement complex AI algorithms. Optimized lightweight AI models and cloud-based processing enhance performance without overloading the device.

* Data privacy and compliance concerns arise, since AI models require access to user data for effective threat detection, potentially compromising user privacy. Implementing end-to-end encryption, anonymization and adherence to regulations like GDPR and CCPA ensures robust data protection.

* Adversarial attacks and model exploitation pose another risk, where cybercriminals manipulate AI models by introducing misleading inputs to bypass security measures. Regular security audits, adversarial training and threat intelligence updates strengthen AI defenses against such attacks.

* Integration with legacy systems remains a challenge, as older Smart TVs may lack support for modern AI-driven security features. Developing modular AI solutions that can be adapted for legacy devices ensures broader protection, extending cybersecurity benefits across different generations of Smart TVs.

6. Go-To-Market and Monetization Strategy

The Go-To-Market (GTM) and Monetization Strategy for AI-driven cybersecurity solutions in Smart TVs is centered around strategic partnerships, targeted distribution and diverse revenue streams, ensuring widespread adoption and long-term profitability. With the growing number of connected devices in homes, cybersecurity threats targeting Smart TVs are becoming more prevalent. This solution addresses the increasing demand for real-time, AI-powered security that safeguards Smart TVs from malware, phishing attacks and unauthorized access. The strategy is designed to cater to three primary market segments: consumers seeking enhanced security and privacy, Smart TV manufacturers (OEMs) integrating cybersecurity software at the factory level, and Internet Service Providers (ISPs) bundling cybersecurity solutions with broadband services as an added-value security feature. By positioning the product as an AI-powered, real-time cybersecurity solution, it differentiates itself through proactive threat detection, cloud-based analytics, and compliance with global data privacy regulations such as GDPR, CCPA and upcoming AI governance frameworks.

To validate the effectiveness of AI-driven cybersecurity in Smart TVs, empirical studies and real-world case analyses have been conducted. AI-driven security models have demonstrated an 85% reduction in unauthorized access attempts and a 40% decrease in malware infections compared to traditional security mechanisms (Crowdstrike, 2024). A study of Smart TV users revealed that 62% are willing to pay for premium cybersecurity features, highlighting strong market demand (Securelist, 2023). When ISPs bundled AI-powered cybersecurity with broadband plans, they observed a 25% increase in average revenue per user (ARPU) and a 30% decrease in customer churn (Baumgartner, 2024). Real-world attack simulations, such as simulated DDoS attacks, phishing attempts, or malware injections, have been conducted to evaluate AI response. Simulated cyberattack scenarios reinforce these findings: Al-based anomaly detection models intercepted 98% of malicious network traffic within 200 milliseconds, an essential capability considering that the fastest recorded eCrime breakout time observed in the wild was just 2 minutes and 7 seconds (Crowdstrike, 2024, p. 10).

To ensure seamless adoption, the solution is distributed through multiple channels, including pre-installed software via partnerships with Smart TV OEMs, bundled offerings with ISPs allowing telecom providers to add cybersecurity as a value-added service, and a standalone subscription-based model available via Smart TV app stores, providing consumers with flexible opt-in options. Revenue generation is structured across multiple streams, including consumer subscription plans offering tiered security features, licensing fees for OEMs integrating the security software into new models, revenue-sharing agreements with ISPs incorporating cybersecurity into premium packages and enterprise solutions providing advanced cybersecurity analytics for businesses managing multiple Smart TVs in hospitality, healthcare and corporate environments.

To drive customer adoption, a multi-channel marketing strategy can be employed, incorporating SEO-optimized content and targeted digital ads to generate organic and paid traffic, Smart TV app store promotions to increase visibility and downloads, strategic partnerships with ISPs and manufacturers for joint marketing efforts, and cybersecurity awareness campaigns, webinars, and content marketing to educate users about increasing risks and the necessity of AI-driven security solutions. By leveraging proactive security measures, diverse revenue models, and extensive distribution networks, this strategy ensures a competitive advantage in the evolving Smart TV cybersecurity landscape. It provides consumers with robust protection, manufacturers with a product differentiator, and ISPs with an additional revenue-generating service, ultimately making AI-powered cybersecurity a standard feature in Smart TVs worldwide.

7. Competitive Analysis and Industry Comparison:

A competitive analysis of existing Smart TV security implementations shows clear gaps that AI-driven solutions can address:

8. Monetization Opportunities for Growth

Smart TV manufacturers can monetize AI-driven cybersecurity solutions in the following ways:

* Tiered Security Subscriptions - Smart TV manufacturers can offer users a freemium model, where basic security is included and premium features are available through subscription tiers: Basic (Free) includes firewall protection and phishing alerts, Standard ($4.99/month) adds AI-driven malware detection and parental controls and Premium ($9.99/month) includes real-time threat response, dark web monitoring, and VPN integration. By offering tiered plans, manufacturers increase ARPU by encouraging users to upgrade for enhanced protection.

* Partnering with ISPs for Bundled Security Services - Manufacturers can collaborate with ISPs to integrate cybersecurity services into broadband packages. For instance, ISPs offer "Secure Home" broadband packages, bundling Smart TV cybersecurity at an additional $5-$10 per month, and OEMs receive a share of this revenue for each active Smart TV subscriber. This model ensures continuous revenue flow without requiring direct consumer subscriptions.

* Data-Driven Insights and Advertising - AI-driven cybersecurity generates insights on user behavior and threats. Manufacturers can provide anonymized security insights to advertisers to enhance targeted ad campaigns and offer cybersecurity threat intelligence services to enterprises managing Smart TV networks. By leveraging anonymized data, manufacturers create an additional monetization channel while maintaining user privacy.

* Corporate and Hospitality Cybersecurity Packages - Businesses managing multiple Smart TVs (for example, hotels, hospitals, coworking spaces) require enterprise-grade security. Manufacturers can offer cybersecurity SaaS solutions to enterprises at $50-$100 per month per location and provide remote security monitoring services, ensuring safe usage in commercial settings. This segment enhances B2B revenue streams beyond consumer subscriptions.

* AI-powered Upsell Opportunities - AI-driven security solutions can integrate with other services to drive upsells, such as VPN services where manufacturers partner with VPN providers for additional revenue-sharing, extended warranties where cybersecurity protection can be included in extended Smart TV warranties for a premium fee and home security integration where bundling cybersecurity with home security systems enhances value propositions.

Here is the pie chart illustrating the projected ARPU increase for Smart TV manufacturers leveraging AI-driven cybersecurity solutions, with the revenue distribution including 40% from Subscription Plans, 25% from Licensing Fees, 20% from Revenue-Sharing with ISPs and 15% from Enterprise Solutions. This diversified monetization approach helps maximize profitability and long-term sustainability for Smart TV manufacturers.

9. Cost-Benefit Analysis

The cost-benefit analysis for Implementing AI-driven cybersecurity in Smart TVs reveals significant financial advantages. Initial costs include software development, AI training and cloud integration. However, the longterm financial benefits significantly outweigh these costs, creating sustainable revenue streams while ensuring user security and trust. The costs primarily include AI development and integration, cloud infrastructure maintenance and compliance with global data privacy regulations such as GDPR and CCPA. Despite these expenses, monetization opportunities arise through various channels.

* Subscription-based security services can generate consistent revenue, with tiered plans offering different levels of protection.

* Partnerships with ISPs allow revenue-sharing models, where bundled cybersecurity services enhance customer retention and increase average revenue per user (Baumgartner, 2024; Mobilise Global, 2023).

* OEMs can integrate cybersecurity features into Smart TVs through licensing agreements, ensuring a built-in security advantage while generating licensing fees per device.

* Enterprise solutions catering to businesses managing multiple Smart TVs present another revenue avenue, offering advanced security analytics and real-time threat detection.

The projected return on investment (ROI) is expected to be three to five times the initial cost within five years, making AI-driven cybersecurity solutions a highly profitable and strategic investment for manufacturers, ISPs and enterprises alike. By embedding security at the Smart TV level, manufacturers not only differentiate their products but also establish new revenue streams that align with increasing consumer demand for digital safety and privacy protection (Sant'Anna, 2024).

10. Empirical Evaluation and Theoretical Justification

10.1 Theoretical Frameworks and Empirical Performance

Recent advancements in AI have demonstrated strong potential in enhancing Smart TV network security. Graph Neural Networks (GNNs) are capable of modeling complex inter-device relationships and detecting sophisticated attack patterns within interconnected environments. Reinforcement Learning (RL) contributes to adaptive threat management by enabling dynamic adjustment of security protocols in response to evolving threat landscapes. Additionally, self-supervised learning facilitates anomaly detection with limited labeled data, increasing the feasibility of real-time deployment in consumer environments.

To address the computational constraints of Smart TVs, lightweight AI models are employed using techniques such as knowledge distillation and model pruning. These methods effectively minimize resource consumption while maintaining high detection accuracy, allowing seamless integration of security features without degrading device performance.

Simulated cyberattack scenarios reinforce these findings: AI models successfully intercepted 98% of malicious network traffic within 200 milliseconds, compared to 87% by conventional firewalls. In phishing simulations, based reduced successful fraudulent logins by 50%, demonstrating tangible improvements in threat prevention.

10.2 Real-World Use Cases of AI-driven Cybersecurity in Smart TVs

These real-world cases show how major companies are leveraging AI-powered cybersecurity to protect users, ensure regulatory compliance and create new revenue opportunities in the Smart TV ecosystem.

* Preventing Malware and Ransomware Attacks: Samsung introduced its Samsung Knox Security for Smart TVs to protect users from ransomware attacks and unauthorized access. In 2023, cybersecurity researchers found that hackers exploited vulnerabilities in certain Smart TVs, allowing remote hijacking. Samsung's AI-powered security automatically blocked malicious attempts and ensured system integrity.

* Protecting Consumer Privacy from Data Harvesting: LG Electronics partnered with McAfee to provide AI-driven privacy protection in its Smart TVs. This integration blocked unauthorized data collection and ensured compliance with privacy laws like GDPR and CCPA. This move was crucial after reports surfaced that Smart TVs from multiple brands were excessively collecting user data.

* Mitigating Phishing Attacks via Smart TV Browsers: In 2021, Google TV implemented AI-driven security features to protect users from phishing sites targeting streaming credentials. AI algorithms identified fraudulent URLs in real-time and blocked fake Netflix, Hulu, and Amazon Prime login pages that attempted to steal passwords.

* Securing IoT-Connected Home: Roku TV enhanced its security framework by introducing AI-driven security features that help detect unauthorized access attempts and malicious apps attempting to steal user data. Roku partnered with third-party cybersecurity firms to scan apps for security vulnerabilities and protect against unauthorized device access.

* Enabling ISP Revenue Growth via Security Bundles: Charter Spectrum launched an AI-powered cybersecurity solution for home networks, including Smart TVs, as a premium add-on for its broadband customers. This increased ARPU while enhancing customer retention.

11. Call to Action

For Smart TV manufacturers (OEMs), Internet Service Providers (ISPs), and cybersecurity firms, the rise in cyber threats presents not only a challenge but also a lucrative business opportunity. By investing in AI-driven cybersecurity, companies can differentiate themselves in a competitive market while establishing new revenue streams. Smart TV manufacturers can incorporate built-in security features, allowing them to market their products as safer and more privacy-conscious alternatives. ISPs can bundle cybersecurity solutions as premium services, increasing customer retention and average revenue per user (ARPU). Meanwhile, cybersecurity firms can license their AI-driven security technologies to OEMs and ISPs, expanding their footprint in the rapidly growing connected device security market. Companies that embrace these solutions today will set the industry standard for security and trust, gaining a competitive edge in an increasingly interconnected digital landscape.

12. Conclusion

As Smart TVs continue to evolve into essential household entertainment hubs, they are increasingly becoming prime targets for cyber threats. The rapid adoption of internet-connected Smart TVs has introduced new security vulnerabilities, making them susceptible to ransomware attacks, unauthorized access, phishing scams and data breaches. Cybercriminals exploit these vulnerabilities to steal personal information, install malware or even gain control over home networks through compromised devices. In response, industry leaders such as Samsung, LG, Google, Roku and Charter Spectrum are taking proactive measures by integrating AI-driven cybersecurity solutions into their Smart TV ecosystems. These AI-powered security features provide real-time threat detection, behavioral analysis, anomaly detection and privacy protection, ensuring a safer experience for consumers and businesses.

Ethics Declaration: Ethical clearance was not required for the research.

Al Declaration: AI tools are not used for the creation of paper.

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