ThreatQuotient released this year’s Evolution of Cybersecurity Automation and AI Adoption Report. This year’s study delves into how cybersecurity teams are operationalizing artificial intelligence, examining the key drivers behind AI deployment, from threat detection and response acceleration to predictive analytics and decision support. According to the report, 97% of cybersecurity professionals now regard automation, increasingly powered by AI technologies, as essential to business operations, a significant rise from 80% in the previous year.
The research, conducted by Opinion Matters, surveyed 750 senior cybersecurity professionals from large organizations with over 2,000 employees in the UK, USA, and Australia from five industry sectors. Now in its fifth year, the study charts the evolution of cybersecurity automation and explores AI adoption as cybersecurity professionals aim to tackle escalating cyberthreats faster and more effectively.
Everyone Wants Automation, but 96% Still Hit Roadblocks
Despite 49% of respondents obtaining net new budget allocation for cybersecurity automation this year – up from 39% last year – 96% still face persistent challenges, particularly around technology limitations, lack of trust in the outcomes of automated processes, and insufficient time to implement solutions. These remain the top three blockers to broader adoption. However, some sectors are beginning to report fewer issues, indicating that as automation matures and focus shifts on AI integration, operational friction is starting to ease.
Key performance indicators for automation are evolving toward a new operational standard, with a marked shift from employee wellbeing to quantifiable metrics. While previous reports identified employee satisfaction as the leading measurement criteria, this year 56% of respondents are prioritizing Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) as core indicators of effectiveness. This pivot reflects a growing emphasis on performance outcomes, as organizations seek tangible, data-driven improvements to validate rising investment levels in automation technologies.
AI Implementation in Cybersecurity
This study explores the primary motivations and influences affecting AI deployment. The results highlight that 56% of organizations prioritize AI adoption to improve productivity, while 44% focus on achieving greater efficiency. The top use cases for AI adoption are assisting triage and analysis of actions with a human in the loop (36%), followed by performing triage and analysis of actions through automation. Thirty-five percent of respondents use or plan to use AI for vulnerability assessments.
“With automation firmly established, organizations are now working to implement autonomous AI-powered cybersecurity,” said Leon Ward, Chief Transformation Officer at Securonix. “However, deploying AI in cybersecurity is not without its challenges. We found that increasing productivity is the leading driver for AI implementation as organizations aim to respond faster to threats from adversaries who are also using AI to advance attack speed and complexity.
Leadership Paradox with Executives Both Driving and Blocking AI
Respondents report that pressure from the board is the dominant driver for both efficiency and productivity, with 71% saying their senior leadership team is driving AI adoption. However, 28% of respondents also say gaining management buy-in is a top problem they encounter. This contradiction indicates that the strong focus on unlocking the competitive benefits of AI is being hindered by tactical uncertainty over specific use cases.
A lack of human skill, AI making incorrect decisions, and technology issues all feature key challenges, and they echo many of the difficulties seen in automation adoption. However, organizations are also concerned about ethical issues (31%), such as data privacy and algorithmic bias. A lack of accountability also features in their top three concerns about using AI. Only 23% said being left behind by competitors was a top three concern.
“Security operations must move past static, rules-based automation and embrace intelligent systems that learn, adapt, and make decisions in real time. This evolution empowers teams to detect threats faster, investigate with greater context, and respond with precision. By investing in AI-powered solutions, SOCs are better equipped to meet growing demands with speed, clarity, and confidence,” concludes Leon Ward.
The research also compares sectors, analyzes roles, and examines how cybersecurity automation and AI are being adopted across the three surveyed regions. To read the full report, visit https://www.threatq.com/cybersecurity-automation-adoption-report.
