Video monitoring has evolved significantly with the integration of artificial intelligence, becoming a proactive and predictive tool in the field of security. According to the study "Análise do Tamanho e Participação do Mercado de IA em Vigilância por Vídeo: Tendências de Crescimento e Previsões (2025-2030)“, the size of the AI market for video surveillance is estimated at US$ 5,98 billion in 2025 and is expected to reach US$ 11,76 billion by 20301
The combination of AI and security devices enables advanced analysis of different scenarios in real time, reducing false alarms and improving the detection of relevant events. However, the implementation of these technologies faces several challenges, from privacy and cybersecurity to scalability and cost
Reduction of false alarms
One of the main challenges of video monitoring with AI is the reduction of false alarms. Traditional security systems generated several unnecessary alerts due to environmental factors, how the movement of plants, animals or adverse weather conditions. AI has improved the accuracy of analysis with the use of advanced facial recognition algorithms, behavioral analysis and object classification. Furthermore, the incorporation of thermal cameras and radar sensors optimized detection in low visibility environments or extreme weather conditions, increasing the effectiveness of security systems
Regulatory compliance
Another major challenge is privacy and compliance. The ability of AI video monitoring systems to identify and track individuals has raised legal and ethical concerns. Regulations such as the General Data Protection Regulation (GDPR) in Europe and similar laws in other regions require companies to take measures to ensure data privacy. The implementation of techniques such as image anonymization and data encryption is essential to balance security with the protection of individual rights. Furthermore, the development of solutions that allow users to control access to their information is a growing trend in the sector
Cybersecurity
Cybersecurity is another critical aspect of AI video monitoring. Network systems can be vulnerable to cyber attacks, putting at risk the integrity of the data and the security of the facilities. To reduce these risks, it is essential to implement advanced security protocols, such as multi-factor authentication, end-to-end encryption and regular software updates. Furthermore, the adoption of edge systems that process data on the device reduces exposure to external threats and improves response speed
Implementation and scalability
Finally, the cost of implementation and scalability represent barriers for several end users. Although AI video monitoring solutions have proven to be highly effective, adoption remains limited due to a misconception of high acquisition and infrastructure costs. However, advances in hardware and software are enabling the development of more accessible and modular solutions, that can be progressively integrated according to the needs and budget of each organization. Optimize return on investment (ROI) through systems that not only detect threats, but also generate automated responses, it is essential to invest in video monitoring with AI
Thus, the integration of artificial intelligence in video monitoring has revolutionized the security sector, offering more efficient and accurate solutions. Meanwhile, it is essential to face the challenges related to privacy, cybersecurity, to the reduction of false alarms and scalability to ensure the responsible and effective use of this technology. As AI continues to evolve, it is expected that video monitoring systems will become increasingly intelligent, autonomous and accessible, establishing itself as an essential tool for safety in urban environments, industrial and commercial
