Enterprises are now rapidly adopting generative AI, AI agents, machine learning models, and AI-powered applications, securing these technologies has become a critical cybersecurity priority. This infographic explores the 10 essential AI security controls enterprises need to protect AI systems, manage emerging risks, and establish security across the entire AI lifecycle.
A strong AI security framework should provide visibility into where AI is being used, protect sensitive and regulated data, control access and agent permissions, detect malicious prompt manipulation, and continuously monitor AI activity. Organizations should also evaluate risks introduced through AI models, APIs, open-source components, plugins, datasets, and third-party AI services.
For enterprises adopting AI at scale, security cannot be limited to the model itself. Effective enterprise AI security requires controls across the entire AI lifecycle, from data and models to AI agents, applications, tools, and outputs.
These AI security best practices provide a practical starting point for organizations looking to strengthen AI governance, reduce security exposure, improve AI risk management, and build secure, trusted, and responsible AI environments.
Enjoyed reading this infographics? Stay updated with our latest exclusive content by following us on Twitter and LinkedIn.
