ARTTIS employs an integration of cutting-edge machine learning techniques and real-time data processing to continuously learn from the environment during surveillance operations.

The system uses a neural network architecture that can analyze massive datasets sourced from multiple inputs including cameras, sensors, and existing databases.

It adapts to new information without human intervention, continuously refining its algorithms based on the latest threat patterns and behaviors encountered.

The use of an agile AI model allows ARTTIS to adjust its parameters dynamically, offering enhanced accuracy in threat detection by minimizing false positives and enhancing response times.

Moreover, its cloud-based infrastructure ensures it can be deployed over various sites, offering scalability to meet varying operational demands.

ARTTIS provides unparalleled accuracy in threat detection by continuously adapting to new data, reducing false alarms and enhancing security response times.

Its ability to operate autonomously in dynamic settings without manual updates gives security agencies a tactical advantage and optimal resource allocation to focus on legitimate threats.

The technical underpinnings of ARTTIS are feasible with current advancements in AI and machine learning, particularly with the growing accessibility to cloud computing resources for on-demand data processing.

The main barriers include the need for extensive datasets to train the models effectively and integration with existing surveillance infrastructure.

Regulatory challenges, especially in data privacy and security, require careful navigation, yet they are surmountable with proper compliance frameworks.