The evolving security threats to AI models are a growing concern. AI-powered cyberattacks are becoming increasingly sophisticated, making them harder to detect. Adversarial attacks manipulate input data to trick AI models into making incorrect decisions, while data manipulation and data poisoning compromise the integrity of training data to skew AI decisions.
Model theft is also a significant risk, allowing attackers to understand and exploit the weaknesses of proprietary AI models. Additionally, model supply chain attacks involve injecting malicious code or data into third-party libraries or training datasets.
To mitigate these risks, organizations must adopt robust AI security measures. This includes implementing data handling and validation protocols to ensure data integrity and authenticity, limiting application permissions to restrict access to sensitive data and systems, and vetting third-party AI solutions for security vulnerabilities.
Google's Secure AI Framework (SAIF) provides a comprehensive framework for addressing AI security risks. SAIF emphasizes the importance of strong security foundations, detection and response, automated defenses, and harmonized platform controls.
