Artificial Intelligence is rapidly becoming part of modern business operations, helping organizations automate processes, analyze data, improve decision-making, and deliver new digital services. However, as organizations adopt AI technologies, they are also creating a new cybersecurity attack surface that requires careful attention.

AI applications can interact with sensitive business data, internal systems, users, and third-party services. If these systems are not designed and secured properly, weaknesses in AI applications can create new opportunities for attackers to access information, manipulate outputs, or abuse connected resources.

The Expanding AI Attack Surface

Traditional applications typically have well-defined inputs, outputs, and access controls. AI-powered applications can introduce additional layers of complexity, including models, prompts, training data, external knowledge sources, plugins, APIs, and automated actions.

A vulnerability in any of these components can potentially affect the security of the overall system. For example, an AI application with excessive permissions or access to sensitive information could create significant risk if an attacker is able to manipulate its behavior or bypass intended security controls.

Emerging AI Security Risks

As organizations integrate AI into their applications and workflows, security teams should consider risks that may not exist in traditional applications:

  1. Prompt Injection: Attackers may attempt to manipulate AI systems through carefully crafted inputs to influence their behavior or bypass intended instructions.
  2. Sensitive Data Exposure: AI applications may process confidential business information, customer data, or internal documents, making appropriate data protection and access controls essential.
  3. Excessive Permissions: AI systems connected to APIs, databases, or business tools should only receive the permissions required for their intended functions.
  4. Insecure Integrations: Third-party models, plugins, APIs, and external services can introduce additional dependencies and security risks into an organization’s environment.

Building a Secure AI Strategy

Organizations should treat AI security as part of their broader cybersecurity strategy rather than as a separate concern. Security controls should be considered throughout the AI lifecycle, from design and development to deployment and ongoing monitoring.

Regular security assessments, strong access controls, data protection, monitoring, and security testing can help organizations identify weaknesses before they become exploitable. AI systems should also be regularly reviewed as their capabilities, integrations, and use cases evolve.

Securing AI Without Slowing Innovation

AI adoption is unlikely to slow down, and organizations should not have to choose between innovation and security. The goal is to build security into AI initiatives from the beginning and continuously validate the controls protecting them.

At Pentest Brigade, our security approach focuses on helping organizations understand emerging risks as their technology landscape evolves. By identifying weaknesses across AI applications, integrations, access controls, and supporting infrastructure, organizations can adopt AI with greater confidence while reducing the security risks associated with this rapidly evolving technology.