As the use of artificial intelligence (AI) continues to expand in various industries, there is an increasing need to consider the security implications of incorporating AI-powered systems into the corporate infrastructure. In particular, the security risks associated with AI-centric systems, such as those powered by ChatGPT, differ significantly from those associated with traditional form-based computer infrastructure.

One of the key differences between AI-centric and traditional systems is the way in which data is processed. In traditional systems, data is typically stored in structured formats, such as databases, and accessed through predefined forms. Access to this data is controlled through role-based access control (RBAC) and other security mechanisms, such as digital certificates and access rights.

In contrast, AI-centric systems are designed to process unstructured data, such as natural language and images, in order to generate insights and responses. This requires a different approach to security, as traditional RBAC and access control mechanisms are not always effective in controlling access to unstructured data.

One of the key security risks associated with AI-centric systems is the potential for malicious actors to exploit vulnerabilities in the AI algorithms themselves. For example, attackers may be able to manipulate the data inputs to the AI system in order to generate misleading results, or even to take control of the system entirely. This is particularly concerning in applications such as financial fraud detection or autonomous vehicles, where the consequences of a security breach can be significant.

Another potential security risk associated with AI-centric systems is the use of third-party data sources. Many AI systems rely on large datasets of real-world data in order to generate accurate results. However, these datasets may contain sensitive or confidential information and may be subject to privacy regulations. It is important for companies to carefully vet their data sources and ensure that appropriate security and privacy measures are in place.

In addition to these risks, there are also practical considerations that companies must take into account when integrating AI-centric systems into their corporate infrastructure. For example, the hardware requirements for running AI algorithms can be significant and may require specialized hardware and software configurations. This can create additional security risks, as these systems may be more difficult to secure and maintain.

Despite these challenges, there are several steps that companies can take to ensure that their AI-centric systems are secure and reliable. One of the most important is to carefully vet and select vendors and partners that have a strong track record in AI security. Companies should also implement a robust security framework that includes RBAC, digital certificates, and access rights management.

It is also important to conduct regular security audits and vulnerability assessments to identify and address potential security risks. This includes both internal audits and third-party assessments, as well as ongoing monitoring and analysis of system logs and other data sources.

In conclusion, the integration of AI-centric systems into corporate infrastructure represents a significant shift in the way that data is processed and managed. This shift requires a new approach to security, one that takes into account the unique risks and challenges associated with AI-powered systems. By implementing robust security frameworks and working closely with experienced vendors and partners, companies can ensure that their AI-centric systems are secure, reliable, and effective. As the use of AI continues to expand, it will be critical for companies to remain vigilant and proactive in managing the security risks associated with this powerful technology.