Securing AI Infrastructure: Top Risks and Best Practices | Infographic
The widespread adoption of AI across various business operations has definitely enhanced an organization’s productivity and efficiency, however, the same AI infrastructure has also created a huge attack surface and is prone to various kinds of cyber threats.
The WEF’s 2025 Global Cybersecurity Outlook already highlighted that 66% of respondents believe AI and Machine learning are the biggest factors that can compromise the security of an organization within the next 12 months. Vulnerabilities in Perplexity AI’s Android app and a Cyberattack on Deepseek’s R1 model are also the latest examples of how these AI tools and systems are vulnerable to cyberattacks and how easily they can be exploited, compromising user data and security.
Therefore, the first and foremost priority of organizations that are using AI solutions is to manage AI infrastructure security risks.
The following infographic highlights different types of security risks to AI infrastructure, including data theft, model theft, injection attacks, and others. It also explores the best practices to secure this AI infrastructure by implementing zero trust security measures, strong authentication and authorization process, API security, continuous monitoring, and more.
AI is the backbone of several businesses now, and it is difficult to keep it away from core business operations. at the same time, it is also necessary to protect the data, devices, and networks powering AI solutions.
Check out the Infographic to learn more.
