In today’s digital landscape, the concept of digital resilience—defined as the capability to prevent, withstand, and recover from digital disruptions—has become a strategic imperative for businesses. With the advent of agentic AI, which encompasses a new era of autonomous systems that can plan, reason, and execute tasks with minimal human oversight, the need for enhanced resilience is more critical than ever. As these intelligent systems transition from experimental projects to integral components of business operations, they present both remarkable opportunities and significant challenges regarding the safeguarding of digital resilience. The rapid autonomy and scale of agentic AI can magnify the repercussions of even minor inconsistencies, security breaches, or fragmented data, highlighting the necessity for organizations to rethink their resilience strategies.
Despite a global forecast estimating AI investments to surpass a trillion dollars, a stark reality remains: fewer than half of business leaders express confidence in their organizations’ ability to ensure service continuity, security, and cost management during unforeseen events. This uncertainty, compounded by the complexities introduced by the autonomous decision-making capabilities of agentic AI, necessitates a fresh approach to digital resilience. One such approach is the adoption of a data fabric—a cohesive framework that integrates and governs information across all facets of a business. By dismantling data silos and facilitating real-time access to enterprise-wide data, a data fabric empowers both human teams and agentic AI systems to proactively identify risks, preempt problems, and recover swiftly when issues arise.
A critical aspect of enabling agentic AI within the realm of digital resilience is the emphasis on machine data. Unlike earlier AI models that primarily depended on human-generated data such as text or audio, agentic AI requires comprehensive insights from machine data—logs, metrics, and telemetry generated by devices and systems. For organizations to leverage agentic AI effectively, it is essential to ensure seamless access to this machine data flow. Without proper integration, organizations may hinder AI capabilities, overlook critical anomalies, or introduce errors. As highlighted by Kamal Hathi, Senior Vice President at Splunk, machine data acts as the ‘heartbeat’ of modern enterprises, essential for AI systems to understand context and adapt in real-time. To build a robust digital resilience framework, organizations must prioritize developing a data fabric that connects fragmented assets throughout security, IT, and business operations, thereby creating a unified system capable of managing risks and ensuring operational continuity.
Source: Designing digital resilience in the agentic AI era via MIT Technology Review