Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In this article API integrations have become the backbone of modern digital interactions, yet they also introduce vulnerabilities that can be exploited if left unchecked. The convergence of artificial intelligence (AI) and application programming interfaces (APIs) offers a promising solution to what many refer to as the “risk visibility gap.” This critical gap is defined as the difference between known API vulnerabilities and the unseen or unmonitored risks that arise from their use.

From supporting customers through intelligent chatbots to auto-generating code, GenAI is revolutionizing enterprise operations. But as Spiderman coined, with great power comes great responsibility. This article will look at generative AI security, including trends and best practices to stay on top of.

Artificial Intelligence (AI) is super-charging customer service, amplifying personalized product recommendations, and accelerating workflows that enable humans to focus on higher-value tasks. However, AI cannot deliver desired productivity improvements to financial organizations without foundational security protection in place. In this blog, I recap several best practices that empower financial institutions to leverage AI safely and responsibly.

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.

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.

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 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.