Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Despite being one of the most highly regulated industries, healthcare businesses are disproportionately impacted by breaches. IBM’s independent research centre, Ponemon Institute’s report on the cost of a data breach, healthcare continues to top the list for 12 consecutive years. AI agents are infiltrating every sector, healthcare is no exception.

What’s happening under the hood of your AI systems? AI is now a crucial element of modern software applications, and if you don’t have visibility into its components, you’ll be left blind. Similar to a Software Bill of Materials (SBOM), an AI Bill of Materials, AI BOM, or AIBOM has become a crucial framework for documenting and securing this new and complex supply chain. This article is part of a series of articles on Shadow AI.

A new era of software engineering is emerging, with artificial intelligence (AI) at the forefront. As the 2025 Gartner Innovation Insight for AI-Native Software Engineering report states: “AI-native software engineering will require software engineering leaders to mitigate new risks and tackle new challenges.” Here are the key insights and perspectives that will help you navigate the new normal.

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.