The principle All Input is Evil originates from the field of software development and computer security. It is a behavior that encourages developers to view all input as potentially insecure or hostile. In this article, we discuss the different kinds of inputs and what is needed to secure them.

Penetration testing for artificial intelligence (AI) systems is crucial for ensuring their security and reliability, addressing unique challenges such as adversarial attacks, data dependency, and ethical considerations. While effective, these tests only provide a snapshot of the current security posture and do not guarantee future safety, necessitating continuous monitoring and adaptation.

Protecting AI models and their training data is essential for companies to maintain their competitiveness and comply with regulatory requirements. This article outlines various technical and organizational measures, including data encryption, access controls, data anonymization, and employee training, to ensure a comprehensive security strategy.

As AI transforms businesses, prioritizing employee awareness on security best practices is crucial to alleviate concerns and build trust among the workforce. Effective AI Security Awareness Programs incorporating AI learning modules can create a culture of transparency and accountability.

Protecting AI training data from data poisoning attacks is crucial for maintaining the reliability and performance of AI systems. This article explores various strategies, with a particular emphasis on verifying external data sources to ensure data integrity and prevent malicious interference.