INTELLIGENT BRANDS // Enterprise Security

F5 ’ s 2024 State of AI Application Strategy Report reveals that while 75 % of enterprises are implementing AI , 72 % report significant data quality issues and an inability to scale data practices . Data and the systems companies put in place to obtain , store and secure it are critical to the successful adoption and optimisation of AI .

“ AI is a disruptive force , enabling companies to create innovative and unparalleled digital experiences . However , the practicalities of implementing AI are incredibly complex and without a proper and secure approach , it can significantly heighten an organisation ’ s risk posture ,” said Kunal Anand , EVP and CTO at F5 . “ Our report highlights a concerning trend : many enterprises , in their eagerness to harness AI , overlook the need for a solid foundation . This oversight not only diminishes the effectiveness of their AI solutions but also exposes them to a multitude of security threats .”

As enterprises build out a new stack to support the widening array of AIpowered digital services , the study highlights challenges they face across the infrastructure , data , model , application services and application layers that must be overcome for widespread scalable adoption .

The promise and reality of Generative AI

Organisations are enthusiastic about the prospects of Generative AI ’ s business impacts . Respondents named it the most exciting technology trend of 2024 . However , only 24 % of organisations say they have implemented Generative AI at scale .

Although the use of Generative AI is on the rise , the most common use cases often serve less strategic functions . The most common use cases that respondents say they ’ ve already deployed include copilots and other employee productivity tools ( in use by 40 % of respondents ) and customer service tools such as chatbots ( 36 %). Tools for workflow automation ( 36 %) were named the highest priority AI use case , however .

Roadblocks to scaling AI in infrastructure and data layers

As enterprise leaders examine challenges to deploying AI-based applications at scale , they cite three main concerns encountered at the infrastructure layer :

• 62 % cite the cost of compute as a major concern to scaling AI

• 57 % cite model security as a primary concern . To address this , enterprise leaders expect to spend 44 % more on security over the next few years as they scale deployments

• More than half of respondents ( 55 %) cite performance across all aspects of the model as a concern

Cybersecurity remains a key concern and consideration

According to the study , cybersecurity is a principal concern for those tasked with delivering AI services . Factors such as AIpowered attacks , data privacy , data leakage and increased liability rank among the top AI security concerns .

When asked how they plan to defend against these threats to secure AI implementations ( or are already doing so ), respondents are focused on app services such as API security , monitoring and DDoS and bot protection :

• 42 % state they are using or planning on using API security solutions to safeguard data as it traverses AI training models

• 41 % use or plan to use monitoring tools for visibility into AI app usage

• 39 % use or plan to use DDoS protection for AI models

• 38 % use or plan to use bot protection for AI models p

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