51% of organizations surveyed focus on recruiting the right talent to manage AI-specific cybersecurity threats.

41.9% of organizations surveyed perceived generative AI applications as a risk, ranking third compared to legacy systems at 23.5% and endpoint devices at 30.6%.

43% of organizations are using specialized AI security tools.

35% of respondents cited difficulty ensuring quality and reliability of AI-generated code.

5% of developers are actively transitioning from human-first to AI-first design for APIs.

56% of respondents cited a lack of control over AI model security used for code generation.

47% of respondents cited difficulty understanding and securing AI-generated code.

24% of developers actively design APIs with AI agents in mind.

38% of healthcare organizations identified generative AI or AI tools as a cybersecurity concern, a new category in this year’s study.

45% of respondents cited the potential for new API vulnerabilities tied to AI-generated code.

51% of developers worry about unauthorized or excessive API calls from AI agents, making it their number one security concern.

49% of developers are concerned about AI systems accessing sensitive data they shouldn't see.

36% of developers lack trust in AI systems.

7% of developers primarily design APIs for AI agents/machine consumption.

33% of developers have ethical, legal, and compliance concerns about AI tools.

13% of developers design APIs equally for humans and AI agents.

41% of developers, architects, and executives rely on AI to generate API documentation.

68% of developers, architects, and executives rely on AI to improve code quality.

23% of organizations identify leveraging AI/ML capabilities for business insights or automation as a main driver behind the use of APIs.

26% of organizations are adopting governance frameworks to establish rules for AI use in development.