Cisco defines AI security framework for enterprise protection

Threats and harms: Adversaries exploit vulnerabilities across both domains, and oftentimes, link content manipulation with technical exploits to achieve their objectives. A security attack, such as injecting malicious instructions or corrupting training data, often culminates in a safety failure, such as generating harmful content, leaking confidential information, or producing unwanted or harmful outputs, Chang stated. The AI Security and Safety Framework’s taxonomy brings these elements into a single structure that organizations can use to understand risk holistically and build defenses that address both the mechanism of attack and the resulting impact.

AI lifecycle: Vulnerabilities that are irrelevant during model development may become critical once the model gains access to tooling or interacts with other agents. The AI Security and Safety Framework follows the model across this entire journey, making it clear where different categories of risk emerge and how they may…