The majority (83%) of employees across industries want their jobs to remain hybrid, Accenture reports. Yet nearly 50% of CIOs feel their cybersecurity initiatives aren’t keeping pace with their digital transformation efforts, according to research by ServiceNow and ThoughtLab. Neither are their cybersecurity budgets. Combining artificial intelligence (AI) and machine learning (ML) for IT operations (AIOps) can help.

At ScienceLogic, we’ve built our customer operations to ensure our customers have an excellent end-to-end customer experience, with a rock-solid plan for improving our customers’ ability to meet and exceed their desired business outcomes. And well, we must be doing something right: ScienceLogic has been recognized in this year’s TrustRadius ‘Best of’ Awards—through our customer’s direct feedback.

Artificial intelligence for IT Operations (or AIOps) has been playing an expanding role in helping SREs, DevOps, and developers effectively navigate the challenges around application and infrastructure complexity, pace of change, and data volume that characterize the operations landscape.

The past decade has seen organizations embrace AI and data analytics at scale. In 2022, IBM found that 35% of organizations have embraced AI—a 4% increase from 2021. The trend of AI adoption will continue to play out in the next several years across virtually every organizational function. At the vanguard of this movement is AIOps, which sees AI used to improve IT operations (ITOps).

On October 3, 2022, the U.S. Cybersecurity & Infrastructure Security Agency (CISA) issued Binding Operational Directive (BOD) 23-01, Improving Asset Visibility and Vulnerability Detection on Federal Networks. The directive requires federal civilian executive branch (FCEB) agencies to deliver a series of procedures, reports, and process validations for continuous and comprehensive asset visibility by April 3, 2023. Thereafter, agencies must maintain compliance with the directive.