93% of businesses acknowledge that AI must be "fully integrated" into their processes to maximise their return on investment.

49% perform a privacy risk assessment to monitor their privacy programs.

48% of enterprises report implementing specific security controls for AI deployments.

85% of businesses face challenges in scaling and operationalising AI across their businesses.

Gartner predicts that Spending on AI-optimised servers will double that of traditional servers in 2025, reaching $202 billion.

Only 1.1% of the vulnerabilities in AI products were entirely unrelated to APIs.

21.5% of AI vulnerabilities are indirectly tied to APIs, including flaws in third-party integrations.

Half of respondents said their organisation will divert capital allocations from other budget areas to fund AI initiatives.

98.9% of AI vulnerabilities are API related.

89% of AI-powered APIs relied on insecure authentication mechanisms, like static keys.

Machine learning-based discovery tools often identify 31% more API endpoints than those reported by enterprises.

12% of enterprises are waiting for security controls to be ready before deploying AI.

77.4% of API-related vulnerabilities in AI products are directly API-related, such as weak API authentication, inadequate rate limiting, and broken access controls.

79% of businesses that deploy AI lack a way to control, manage and sustain it effectively.

47% of organisations cite adversarial advances powered by generative AI (GenAI) as their primary concern.

AI vulnerabilities increased by 1,025% from 2023 to 2024.

57% of AI-powered APIs were externally accessible.

Only 11% of AI-powered APIs implemented robust security measures, such as bearer tokens with expiration times.

Wallarm tracked 439 AI-related CVEs in 2024.

Nearly 2 in 5 organisations are counting on cost savings from AI-driven efficiencies to pay for the technology.