The rapid growth of generative AI (Gen AI) has significantly transformed enterprise technology over the past two years. This surge in demand for Gen AI is accompanied by heightened security risks, as the urgency to innovate can lead to compromises in security protocols.

As malicious actors increasingly leverage Gen AI to enhance their attacks, securing enterprise applications has become more critical than ever. It is essential to implement robust security measures to protect the underlying infrastructure that supports these applications.

The landscape of Gen AI is evolving, with the emergence of autonomous AI agents expected to become widely adopted in the near future. While these AI agents currently have limited use in production environments, their integration poses substantial security challenges. Enterprises will need to manage the identities of potentially thousands or millions of AI agents, ensuring they have controlled access and that their actions are predictable.

To protect against emerging threats, enterprises must adapt their security practices to meet the unique challenges presented by Gen AI. For instance, attacks like prompt injection can expose sensitive information or trigger unintended actions within applications. Without adequate protection for the underlying systems and databases that support Gen AI, enterprises leave themselves vulnerable to severe cyberattacks.

Identity-related breaches are a leading cause of cyberattacks, granting unauthorized access to critical systems and sensitive data. Therefore, identifying and securing various user identities and their access rights is a top priority. The strategies used to secure identities within the Gen AI infrastructure often mirror those employed in other secure environments.

Key components of Gen AI-powered applications include application interfaces, learning models, and enterprise data, all of which require stringent security measures. APIs serve as gateways to interact with Gen AI systems, making their protection vital to prevent unauthorized access. Furthermore, algorithms that analyze data to generate insights must be securely trained on both public and proprietary enterprise data to ensure their effectiveness.

Additionally, the environments where these AI applications are deployed, whether on-premises or in the cloud, must be fortified with strong identity security measures. Robust security protocols are necessary to safeguard the backend systems that underpin Gen AI applications.

Implementing effective identity security controls is crucial, especially since many identities hold significant privileges within the Gen AI infrastructure. If these identities are compromised, it could expose the enterprise to various security threats. High-risk identities include business users, data scientists, developers, and DevOps engineers, all of whom play a role in managing and scaling the backend infrastructure.

Adopting a Zero Trust security model is vital in modern IT environments, where machine identities often outnumber human ones. Security measures should extend beyond basic authentication and role-based access control (RBAC), ensuring that compromised accounts do not create extensive vulnerabilities.

To enhance identity security, organizations should consider several strategies. These include enforcing strong multi-factor authentication for all user access, regularly auditing and rotating credentials, and implementing zero standing privileges to minimize unnecessary access rights. Additionally, auditing sessions and monitoring user behavior are essential for maintaining security and compliance.

Yuval Moss, CyberArk’s Vice President of Solutions for Global Strategic Partners, emphasizes that security controls should be scalable and user-friendly, balancing security needs with user experience and productivity.