AI security can refer to two different instances. First is the practice of protecting artificial intelligence systems and the data they process from cyber threats, malicious attacks, and unauthorized access. This involves implementing measures to secure AI models, algorithms, and infrastructure, ensuring the integrity, confidentiality, and availability of AI-driven applications. AI security also encompasses safeguarding the data used to train and operate these systems, preventing data poisoning, model inversion, and other adversarial attacks. By fortifying AI systems against potential vulnerabilities, organizations can maintain trust in their AI capabilities and ensure reliable, safe, and ethical use of artificial intelligence technologies.

The other definition is the application of artificial intelligence technologies to enhance the protection of systems, networks, and data from cyber threats. AI Security involves using machine learning algorithms, pattern recognition, and other AI techniques to detect, prevent, and respond to security incidents more effectively. These technologies can identify anomalies, predict potential threats, automate responses, and improve the accuracy of threat detection by learning from vast amounts of data. By leveraging AI, organizations can enhance their cybersecurity measures, quickly adapt to emerging threats, and minimize the risk of cyberattacks, thereby ensuring the integrity, confidentiality, and availability of their digital assets.