AI adds security challenges
On the security side, Cisco found that 97% of companies have some protection for data used in AI models, and 68% have the ability to detect attacks on those AI models.
“Organizations are also not fully prepared to guard against the cybersecurity threats that come with AI adoption,” Cisco stated. “As higher volumes of data, including confidential and sensitive data, is processed by AI, the incentive for malicious actors to launch attacks against these systems becomes greater while the stakes for organizations get higher.”
Further, with one quarter of leaders saying their organizations have limited awareness or are unaware of security threats specific to AI workloads, more education is needed for organizations and their employees to work with AI securely, Cisco stated.
The encouraging part is that 77% of organizations are at least implementing advanced encryption or end-to-end encryption to protect the data utilized in AI models, Cisco found.
Some other interesting tidbits from Cisco’s AI readiness index include:
Machine learning has the highest rate of deployment at 35%.
However, predictive and generative AI have the highest rates of in-progress deployment at 41% and 40% respectively, Cisco found.
AI deployment in an organization increases power consumption.
Complex computations and data processing tasks inherent to AI models demand more energy from the underlying hardware, especially GPUs and data centers. Companies should think about deploying tools and technologies that can provide higher network bandwidth, better performance and scale, and consume less power, Cisco warned.
However, less than half (44%) of respondents say they are highly prepared with infrastructure dedicated to optimizing power for AI deployments. Another 55% of respondents say they are not prepared or ‘somewhat’ prepared. The adoption of technologies that help deliver more output while consuming lesser power will become a competitive differentiator as adoption of AI increases, Cisco stated.
Skills gaps and a lack of resources remain challenging.
Close to half of respondents said their organizations are moderately well resourced (47%), with an almost even split between those feeling very well resourced (29%) and those who are under resourced or unsure (24%). Those at companies with more than 1,500 employees are slightly more likely to feel under resourced, and media and communications, education and natural resources are the industries with the largest issues in this area. In addition, 37% of respondents ranked comprehension and proficiency of AI tools and technologies as their primary skill gap.
Effective data analytics tools go hand in hand with AI applications and overall data strategy.
More than two-thirds of global respondents (67%) positively rated the ability of their analytics tools to handle complex AI-related data sets. However, 74% of respondents said their analytics tools are not fully integrated with data sources and AI platforms being used, Cisco stated. In fact, 31% of respondents said their tools were not integrated (4%) or somewhat integrated (27%) at best.