Cyberattacks, outages, and AI-scale workloads are exposing the limits of legacy enterprise networks. As costs soar and threats evolve, IT services company Kyndryl says organizations must move beyond basic automation toward secure, AI-native infrastructure capable of autonomous operations, continuous visibility, and resilience across distributed environments.
Across industries, cyberattacks and IT outages are no longer rare events with minor impact – they’re multimillion-dollar business risks. In 2025, organizations reporting cyber incidents lost an average of roughly $3.7 million per event, with nearly half experiencing significant service outages as a result. Meanwhile, each minute of an unplanned network interruption can cost enterprises $9,000 per minute, or more. That’s over $540,000 per hour.
These costs underscore a critical reality for today’s enterprises: without modern, secure, AI-native networks as the foundation, businesses will never realize the promise of AI, cloud and edge computing. Think of it as racing a Formula 1 car on a dirt road or running a bullet train in the subway. Whichever metaphor you choose, the network must be robust and secure enough to handle today’s traffic.
Enterprises have long invested in automation to simplify network management – reducing manual tasks such as configuration, monitoring and troubleshooting. But as AI workloads scale, basic automation is no longer enough. AI-native networks are beginning to move toward true autonomy, where systems can detect faults, optimize traffic, and mitigate threats in real time, with minimal human intervention.
That same shift is reshaping network segmentation. Defining which applications and users can access specific network parts has traditionally been slow, complex and labor-intensive. In many organizations, it can take weeks to implement new policies.
AI-driven networks now analyze traffic patterns, behavior, and threat signals continuously, allowing security boundaries to adapt dynamically. This makes it possible to protect distributed, AI-powered network environments at the speed modern business demands.
Many enterprise networks are still stitched together from incremental upgrades, mergers and isolated optimization efforts. These “Franken networks” lack the consistency, visibility, and architectural flexibility required to support AI-driven operations.
Modern networks require comprehensive observability – the ability to understand not just what is happening, but why. Without unified visibility across hybrid environments, organizations are effectively flying blind: unable to predict failures, validate AI performance, or trace security incidents to their source.
As AI workloads scale and become more distributed, these weaknesses are amplified – exposing performance bottlenecks, misconfigurations, and operational blind spots that make true autonomy unworkable. Supporting AI at scale requires more than incremental improvements. It demands modernized networks built for high bandwidth, low-latency and architectural flexibility. Without these foundations, organizations face greater likelihoods of prolonged outages, rising operational risks and escalating financial losses.
Resisting attacks
On top of managing performance challenges, businesses also must be ready to resist – and recover from – the increasing frequency and sophistication of cybersecurity threats. A recent technology readiness report, which surveyed top enterprise leaders, showed cyberattacks as the number one concern related to external business risks. Yet only 37% of organizations surveyed felt prepared to manage these risks effectively.
In parallel, enterprises are deploying increasingly agentic AI systems – autonomous agents capable of reasoning, planning, and executing complex tasks. But while these systems unlock powerful new capabilities, they also introduce new risks. Bad actors are employing automated prompt injections, training data poisoning, model theft, and manipulation of learning systems to disrupt organizational operations. These approaches enable cyber criminals to expand the platform attack surface beyond what traditional defenses were designed to address.
Legacy “secure by design” approaches, built for predictable and rule-based environments, are no longer sufficient. Distributed AI traffic increases operational complexity, creates new blind spots and makes fragmented networks harder to defend. Each hybrid environment presents multiple attack surfaces for wrongdoers who can leverage AI to accelerate reconnaissance, exploit vulnerabilities, and launch highly adaptive, automated strikes.
It gets worse. Experts predict that quantum systems will begin encroaching upon encryption standards and general cybersecurity within the next five years. And cyber criminals already are pursuing “harvest now, decrypt later” strategies to steal encrypted data to exploit after quantum tools become available. To stay ahead, organizations must begin building cryptographic agility into their security architectures now.
Crypto-agility enables enterprises to adapt encryption methods quickly as standards evolve, reducing the burden of large-scale upgrades and minimizing disruption. Rather than relying on static defenses, organizations need security frameworks designed for continuous evolution as quantum-resistant technologies mature.
Technical debt used to be an inconvenience. Today it’s an active security liability. And as organizations migrate toward Zero Trust Network Access (ZTNA), SASE architectures and unified security and network operating models, they must build resilience into the infrastructure itself. As AI operations scale, modernized networks require layered defenses, deep telemetry, consistent policy enforcement, and continuous verification – from devices and applications from devices and applications to data centers and cloud platforms.
Paths forward
Progressive organizations are working with trusted experts to develop integrated command hub services that converge network and security operations into unified operating models. This integrated approach delivers real-time visibility, unified monitoring, and accelerated incident response across the entire IT landscape. Unified models also enable faster decision-making, fewer manual handoffs, automated response playbooks to accelerate detection and remediation, and more consistent governance as environments scale.
Ultimately, enterprises must begin to view AI, edge computing, cybersecurity and quantum readiness as interconnected priorities – not isolated initiatives. Each depends on the same foundation: a secure, modern and AI-native network capable of supporting autonomous systems, resilient operations, and continuous innovation.
But technology investments alone are not enough. Organizations must continue to invest in their people – equipping network, security, and operations teams with the expertise they need to deploy, manage, and govern increasingly autonomous and distributed environments. Without properly skilled people, even the most advanced technical infrastructures will fall short of their potential.
Organizations that modernize with this future-ready mindset will be better positioned to capitalize on their investments in AI and other emerging technologies. Those that delay risk being constrained by fragile infrastructure and rising security exposure as their investments evaporate. In the AI era, network modernization is no longer a technical upgrade. It’s a strategic imperative.
