AI Adoption Soars, But Security Lags Behind
▼ Summary
– 96% of organizations are deploying AI models, but only 2% are highly ready to scale AI securely across operations, according to F5’s research.
– 77% of companies show moderate AI readiness but lack governance and cross-cloud security, while 21% fall into the low-readiness category.
– 70% of moderately ready organizations actively use GenAI, with AI present in about one-third of their applications, while highly ready organizations expect portfolio-wide saturation.
– 65% of organizations use two or more paid AI models alongside open-source alternatives, with Meta’s Llama, Mistral AI, and Google’s Gemma being top open-source choices.
– Only 18% of moderately ready organizations have deployed an AI firewall, and hybrid environments create governance gaps, increasing risks without proper control frameworks.
Businesses are rapidly embracing AI technologies, but most lack the security measures needed to protect their investments. A staggering 96% of organizations now deploy AI models, yet only 2% are fully prepared to scale these solutions securely. This gap between adoption and governance leaves companies vulnerable as they race to integrate machine learning into their operations.
Recent research involving 650 IT leaders and 150 AI strategists from large enterprises reveals critical trends in AI implementation. While 77% of businesses show moderate readiness, many struggle with governance and cross-cloud security. Another 21% lag significantly, putting them at a competitive disadvantage as industries increasingly rely on AI-driven solutions.
Generative AI is already in active use across 70% of moderately prepared organizations, with nearly all others in the testing phase. On average, a quarter of enterprise applications now incorporate AI functionality. However, adoption varies widely, high-readiness companies embed AI throughout their portfolios, while low-readiness firms limit deployment to isolated experiments or fewer than 25% of apps.
The reliance on multiple AI models is another key finding. 65% of organizations use at least two paid models alongside open-source alternatives, averaging three models per company. Popular paid options include GPT-4, while open-source favorites range from Meta’s Llama variants to Mistral AI and Google’s Gemma. Yet this diversity introduces risks, particularly when governance frameworks are weak.
Security remains a pressing concern. Just 18% of moderately prepared businesses have implemented AI firewalls, though 47% plan to within a year. Fewer than a quarter practice continuous data labeling, a critical oversight that heightens vulnerability to adversarial attacks. Hybrid cloud environments further complicate governance, exposing workflows and sensitive data.
To bridge these gaps, experts recommend a three-pronged approach:
Diversify AI models strategically, combine paid and open-source tools while strengthening oversight to minimize risks.
Companies that achieve high readiness gain a clear advantage, scaling AI safely while driving innovation. Those that delay face mounting operational hurdles, compliance issues, and stalled growth. As AI reshapes industries, the divide between leaders and laggards will only widen, making security and scalability non-negotiable priorities.
(Source: HelpNet Security)
