Para 2025, el mercado de software de inteligencia artificial (IA) se expandir\u00e1 de los $33 mil millones de 2021 a $64 mil millones, seg\u00fan un nuevo informe. Y la seguridad cibern\u00e9tica es la categor\u00eda de gasto en IA de m\u00e1s r\u00e1pido crecimiento, experimentando un incremento en el gasto a una tasa de crecimiento anual compuesto (CAGR) del 22,3 %.<\/p>\n
By 2025, the artificial intelligence (AI) software market will expand from 2021’s $33 billion to $64 billion, according to a new report. And cybersecurity is the fastest-growing category of AI spend, experiencing a rise in spending of 22.3% compound annual growth rate (CAGR).<\/p>\n
That’s according to the “Global AI Software Forecast 2022” from Forrester Research. “Cybersecurity is the fastest AI software growth category, with a focus on the real-time monitoring of and response to attacks,” the report states. The next two categories, customer and human capital management (22%) and process optimization, knowledge, and data intelligence (18.3%), also have cybersecurity elements, so the impact on security tool makers could be even more significant.<\/p>\n
This comports with the emphasis companies have placed on their AI-enhanced software and services. For example, credit behemoth Visa revealed it has spent a half-billion dollars on\u00a0data analytics and AI<\/a>\u00a0in the past five years. It’s using those tools, along with conventional cybersecurity measures, to keep the fraud rate at what Visa calls historic lows despite e-commerce growth.<\/p>\n Organizations can deploy AI for cybersecurity anywhere there’s\u00a0repetitive actions and expected behavior<\/a>, including\u00a0attack surface management<\/a>, extended detection and response (XDR), and user and entity behavior analytics (UEBA). Forrester calls out SentinelOne as a prime example of an XDR success story, pointing out the company’s\u00a0120% year-over-year revenue growth<\/a>\u00a0in fiscal 2022. In March, SentinelOne\u00a0added identity threat detection and response<\/a>\u00a0to its platform when it acquired Attivo Networks.<\/p>\n An AI tool can learn what normal activity from a particular device or account is and then flag when that endpoint is acting outside of the norm. Such automated detection<\/a>\u00a0is invaluable, considering the impossibility of staffing sufficiently to have human eyes watching every part of the network. And researchers are finding ways to\u00a0apply large language models<\/a>\u00a0like GPT-3 to practical tasks, such as tracing networks of exploit forums. To provide some perspective on such developments, Dark Reading\u00a0released a report<\/a>\u00a0in September, “How Machine Learning, AI & Deep Learning Improve Cybersecurity,” about how to assess a vendor’s AI claims and define its success criteria.<\/p>\n One hitch in AI’s gallop is the challenge of setting up a system so that it flags what is necessary for human analysts to assess without creating alert fatigue. A survey earlier in 2022 revealed that almost half (46%) of IT security staff said their AI systems created\u00a0too many false-positive alerts<\/a>\u00a0for them to address. An optimist would see the\u00a0false-positive problem<\/a>\u00a0as an opportunity for growth, however, opening up a new market for fine-tuning services.<\/p>\n For more insights, visit the Forrester Research blog entry<\/a>\u00a0about the report.<\/p>\n