Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The swift adoption of generative AI (GenAI) by the software industry has introduced a new area of focus for security engineers: threats targeting the various components of their AI applications. Understanding how these areas are vulnerable to attacks will become increasingly significant as the space evolves. In this series, we'll look at common threats that target the following components of AI applications.

In Parts 1 and 2 of this series, we looked at how attackers get access to and take advantage of the infrastructure and supply chains that shape generative AI applications. In this post, we'll discuss AI interfaces, which we define as the entry points and logic that determine how a user interacts with an AI application. These elements can include chat interfaces, such as AI assistants, and API endpoints for supporting services.

The AI ecosystem is rapidly changing, and with this growth comes unique challenges in securing the infrastructure and services that support it. In Part 1 of this series, we explored how attackers target the underlying resources that host and run AI applications, such as cloud infrastructure and storage. In this post, we'll look at threats that affect AI-specific resources in supply chains, which are the software and data artifacts that determine how an AI service operates.

APIs operate throughout the digital world to support mobile applications, enable cloud capabilities, power GenAI tools, and conduct invisible operations during every digital interaction. As the growth of API usage accelerates, Akamai’s 2024 API Security Impact Report shows that organizations find it difficult to align their security efforts with the expanding risk domain.

Large Language Models (LLMs) are becoming integral to SaaS products for features like AI chatbots, support agents, and data analysis tools. With that comes a significant privacy risk: if not handled carefully, an LLM can ingest and remix sensitive personal data, potentially exposing private information in unexpected ways. Regulators have taken note – frameworks like GDPR, HIPAA, and PCI-DSS now expect AI systems to implement auditable, runtime controls to protect sensitive data.

For many of us, the term “cloud security breach” conjures meticulous attack plans executed by sophisticated criminal syndicates. But in reality, “attacks” can be far more mundane: maybe some forgotten credentials, a few default permissions, or a user whose cleanup to-do list never got done. At the center of these incidents are standing privileges: long-lived access rights originally granted for legitimate tasks.