Definition
Securing AI algorithms involves implementing robust measures to protect artificial intelligence models and their underlying data from malicious attacks and unintended vulnerabilities. This process encompasses safeguarding training data, defending against adversarial attacks that manipulate model inputs, and ensuring the integrity and privacy of the AI's outputs. In practice, it means building defensive layers into the AI development lifecycle, from data preprocessing to model deployment and ongoing monitoring. Protecting AI algorithms is crucial for maintaining trust, preventing data breaches, and ensuring AI systems operate reliably and ethically in critical applications. For instance, securing a facial recognition algorithm prevents attackers from subtly altering images to bypass security checks or impersonate individuals. This differs from general cybersecurity which focuses on IT infrastructure, by specifically addressing the unique attack vectors targeting AI models themselves. Learners will encounter concepts related to securing AI algorithms in advanced cybersecurity certifications and specialized AI governance courses.