This article explores the role of AI powered IoT security and its industry impact. It covers AI’s applications in threat detection, access control, authentication, network security, vulnerability detection, and predictive maintenance. Additionally, it addresses limitations of AI in IoT security, including data requirements, false positives/negatives, AI system vulnerabilities, and implementation costs.

The article defines sustainable AI as AI systems that adhere to ethical business principles and outlines two key aspects of sustainable AI: making AI itself sustainable and using AI as a tool to promote sustainable development.

In today’s fast-paced business landscape, digital transformation has become an omnipresent reality cutting across industries. Enterprises are increasingly realizing the immense potential of leveraging data, but simply capturing and storing data is not enough to fully unlock its value. The real power lies in seamlessly integrating data into every aspect from development to delivering value to your customer, creating a dynamic Intelligent Ecosystem. Forward-thinking executives are focusing on prioritizing digital technologies that power digital transformation trends to empower enterprises with the competitiveness needed in this evolving business landscape.

Quantum computing is a type of computing based on quantum mechanics that employs qubits, which can represent both 0s and 1s simultaneously. The main difference between quantum and classical computing is that quantum computers can perform many calculations at once, making them more reliable for complex applications such as artificial intelligence (AI).

Machine learning has become a fundamental part of many large businesses and is allowing them to make data-driven decisions with greater accuracy. Sectors like banking and finance, healthcare, manufacturing, transportation, and retail are undergoing significant transformations due to the implementation of machine learning. The latest advancements in this technology have brought about a revolution that was once inconceivable.

Malware of different families often share specific behavioral patterns that can be studied and identified through Machine learning’s static and dynamic analysis. Static analysis involves the study of malicious files’ content without executing them. On the other hand, in dynamic analysis the behavioral aspects of malicious files are analyzed by executing tasks like function call monitoring, information flow tracking, and dynamic binary instrumentation. Through machine learning the static and dynamic artefacts of the malware can be used to predict the evolution of modern malware structure which can then empower systems to detect more complex malware attacks that otherwise are exceedingly difficult to predict by traditional methods.

While Virtual Reality (VR) has long passed the hype of being called “Futuristic Technology”, now the solutions built around it are solving many real-world problems. It has delivered use cases in many industries including healthcare, entertainment, and tourism, and has become a key component of many businesses’ new product development plans.

The wall between the real and virtual world is breaking down with the advancement in

Technology has always been transforming every aspect of human lives and healthcare. Proper implementation of technologies can improve the functioning of healthcare organizations. Advanced technologies like augmented and virtual reality are rapidly changing our way of living.