45% of cybersecurity professionals in the United States cited phishing as their greatest risk in 2025.

59% of business and IT leaders warned that AI cyber threats are advancing faster than their security team’s expertise to deal with them in 2025.

16% of cybersecurity professionals in the United States reported that their organizations are fully prepared to handle AI-enhanced attacks in 2025.

50% of cybersecurity professionals in the United Kingdom identified phishing as the top identity-based threat in 2025.

9 working weeks per year are spent on vendor security reviews and risk assessments, compared to 7 weeks the previous year.

95% of leaders believe that AI and automation have improved security team effectiveness in 2025.

61% of leaders reported spending more time proving security rather than improving it in 2025.

Only 16% of legal teams have total visibility into how their portfolios are managed

49% of organizations reported an increase in AI-generated phishing, 48% reported an increase in AI-powered malware, and 47% reported an increase in AI-driven identity theft or fraud in the past year.

76% of organizations have a domain management strategy in place

27% of cybersecurity professionals reported fully implemented zero-trust frameworks at Black Hat USA in 2025.

88% of senior legal professionals believe that AI-enabled systems are driving an increase in infringement activity

At Infosecurity Europe, 18% of cybersecurity professionals reported fully implemented zero-trust frameworks in 2025.

50% of cybersecurity professionals in Germany reported that their organizations lack a dedicated Privileged Access Management (PAM) solution in 2025.

In 2025, 77% of callback numbers used AI-generated voices, while 69% of vishing attacks were financially motivated, requesting bank detail changes, fraudulent refunds, or transfers.

33% of respondents say AI agents shared sensitive or inappropriate data

40% of organizations reported that they already use AI for threat hunting in 2025.

20-30% of AI-generated code over-engineers for improbable edge cases, causing performance degradation and resource waste.

90-100% of AI-generated code contains excessive inline commenting, which dramatically increases computational burden and makes code harder to check.

60-70% of AI-generated code lacks deployment environment awareness, generating code that runs locally but fails in production.