The New Threat Landscape: AI-Native Apps and Agentic Workflows

As businesses transition from AI experiments to scalable implementations, they face new security challenges, particularly with AI agents that automate tasks. These agents expose vulnerabilities, such as data poisoning and prompt injection, which threaten AI model integrity and data security. The increasing complexity of AI systems, compounded by federated identity gaps and rapid developments in AI components, necessitates a new approach to risk management and governance. Despite the potential of AI to enhance problem-solving, its unpredictable nature complicates the detection of vulnerabilities and non-compliance with data privacy regulations. The importance of integrating security early in AI workflows is emphasized, with tools like Snyk’s AI Trust Platform offering solutions for secure and scalable AI-native applications. As AI becomes embedded in enterprise software, organizations are urged to adopt AI Trust, Risk, and Security Management (AI TRiSM) practices to maintain security and operational integrity.