BTR: SASE Emerges as the Strategic Control Plane for the AI-Driven Enterprise

Kevin Sheu, Versa

WASHINGTON, DC, UNITED STATES, October 28, 2025 /EINPresswire.com/ -- The speed of AI adoption has upended long-held assumptions about enterprise infrastructure. What once functioned as a passive transport layer is now a dynamic, decision-making environment that determines how securely and efficiently organizations can train models, move data, and deliver insight at scale.

“Artificial intelligence is changing how we have to think about the network itself,” said Kevin Sheu, vice president of product strategy at Versa, in a recent BizTechReports Executive Vidcast. “Security, networking, and AI can’t operate as separate disciplines anymore. Your network has to support security, your security has to support the network, and both have to support AI traffic.”

As organizations race to embed AI into their workflows, the Secure Access Service Edge (SASE) model—an architecture that merges networking and security into a unified, cloud-delivered framework—is rapidly becoming the foundation for enterprise resilience. Sheu’s analysis of this transformation reveals not only how SASE changes the strategic landscape, but also how it redefines the organizational response, the economics of risk, and the leadership journey that underpins successful adoption.

SASE Redefines the Balance Between Innovation and Exposure

Artificial intelligence has altered the economics of both innovation and exploitation. Its acceleration across industries has created new attack surfaces even as it empowers defenders with real-time visibility and predictive analytics. “AI presents both opportunities and challenges—and both sides are growing fast,” Sheu said. “Attackers are using AI to become more sophisticated, and defenders are using it to gain visibility and predictive capability.”

For enterprise technology executives, this means the network is no longer background plumbing—it is the strategic control plane mediating agility and exposure. For threat actors, it represents an expanded battlefield where lateral movement, data exfiltration, and model manipulation can occur across multi-cloud and edge environments.

SASE changes this power dynamic by unifying software-defined wide-area networking (SD-WAN) and Security Service Edge (SSE) functions such as zero-trust network access, secure web gateways, cloud access security brokers, and data loss prevention. The result is a single policy and telemetry fabric that enables organizations to move fast without losing visibility.

“What used to be philosophies like zero trust and convergence are now operating principles,” Sheu said. “SASE makes it possible for performance and protection to rise together rather than collide.”

By removing the friction between speed and safety, SASE allows enterprises to innovate faster without amplifying exposure. Unified connectivity and security mean that new digital services, AI workloads, and remote operations can be launched with greater confidence and fewer dependencies. This acceleration, however, introduces its own demands: governance, culture, and collaboration must evolve just as quickly to sustain it.

This new balance between innovation and risk reshapes how enterprises must organize internally. To fully realize SASE’s potential, technology strategy must extend beyond architecture into governance, culture, and collaboration.

Building the Connected Response

The shift toward SASE-driven operations demands more than a technology upgrade—it requires a fundamental reorganization of responsibility. Enterprises have long separated their network, security, and IT infrastructure teams into silos, each with different incentives and success metrics. AI-driven transformation has erased those boundaries.

“Networking, security, and AI operations can’t function as separate priorities anymore,” Sheu said. “Each now depends on the others for performance, protection, and insight—if one fails, the rest fall behind.”

Without tighter alignment between these disciplines, enterprises risk building AI capabilities on unstable ground. Security gaps multiply, performance suffers, and innovation slows under the weight of disconnected systems.

Managing this interdependence extends into operational technology (OT) environments, where industrial systems and IoT devices generate massive volumes of data. Manufacturing lines, logistics hubs, and utilities increasingly rely on AI at the edge to drive predictive maintenance and operational efficiency. Yet those same networks often run on legacy protocols and fragmented oversight.

Sheu argues that OT must be integrated into the enterprise network—not treated as a separate island. “We’re seeing threats that move seamlessly between on-prem, cloud, OT, and remote environments,” he said. “They’re connected—whether organizations are ready for that or not.”

Forward-looking organizations are responding by establishing shared governance models and cross-domain visibility across IT and OT systems. The “crawl, walk, run” approach that Sheu advocates begins with AI-assisted monitoring—humans in the loop—and evolves toward automation as confidence and clarity increase.

This organizational evolution is not only about tightening controls; it is about building the connective tissue that allows enterprises to operate as cohesive, intelligent systems. And that transformation inevitably brings questions about cost, return on investment, and long-term risk economics.

Turning Convergence into Justifiable Value

The traditional economics of cybersecurity—layered defenses, redundant tools, and ever-growing budgets—are becoming unsustainable. AI’s data intensity has multiplied the scale of network management while exposing gaps in legacy architectures. Fragmentation has driven up operational costs and created blind spots that adversaries are quick to exploit.

SASE introduces a new economic calculus for risk management by collapsing multiple technologies into a unified, cloud-based platform that improves efficiency, visibility, and scalability. “The line between security and networking is disappearing,” Sheu said. “We talk about ‘AI for SASE’—embedding intelligence into security—and ‘SASE for AI,’ meaning the network must be re-architected to support the intelligence we’re deploying.”

This dual framework—AI for SASE and SASE for AI—offers a practical lens for calculating return on investment. The benefits appear across four measurable dimensions:

Operational Efficiency: Automation and AI copilots reduce manual workload and response time.

Performance and Productivity: Intelligent traffic engineering ensures AI workloads stay close to data sources, minimizing latency.

Resilience and Risk Reduction: Unified visibility and consistent policy lower incident costs and limit the blast radius of attacks.

Scalability and Agility: Cloud-delivered infrastructure removes the need for piecemeal hardware upgrades and local patching cycles.

But Sheu emphasizes that SASE adoption should not be justified on cost alone. “This is about enabling innovation safely,” he said. “AI and automation will deliver value only if the network can keep up—and stay protected.”

The financial logic of SASE, then, is intertwined with its strategic logic because enterprises can no longer afford to separate resilience from growth. That realization is transforming how boards and executives approach technology investments—and how they collaborate with their CIOs and CISOs to lead the journey ahead.

Toward Intelligent Convergence

For senior executives, SASE should represent more than an IT modernization—it signals a governance realignment in which technology strategy and business strategy advance together. The question is not whether to converge security and networking, but how to manage that transformation responsibly and sustainably.

“This is a journey, not a switch you flip,” Sheu said. “It’s about setting the pace—moving quickly in incremental steps, guided by resilience and clear accountability.”

As a result, business leaders will play a pivotal role in ensuring that SASE-driven transformation remains outcome-oriented rather than tool-driven. They must partner with technology leaders to:

Define enterprise-wide guardrails that align AI and security policies with corporate risk appetite.

Invest in shared language and training, so IT, networking, and OT teams understand mutual dependencies.

Adopt metrics that connect risk management to business performance, measuring not only cost savings but continuity, trust, and innovation capacity.

Institutionalize governance councils that balance automation speed with oversight discipline.

Sheu framed the leadership challenge succinctly: “Innovation without resilience is fragility. The companies that connect those dots—security, networking, and AI—will move faster and more safely than their competitors.”

The most successful journeys, he added, begin not with a technology deployment but with an enterprise mindset shift—from protecting networks to enabling intelligent connectivity across every business function.

The Bottom Line

SASE is no longer a niche architecture—it is becoming the operating model for the AI-enabled enterprise. It defines how organizations structure teams, allocate capital, and measure resilience in a world where data, intelligence, and risk move at digital speed.

Enterprises that embrace convergence as both a technical and cultural mandate will be better positioned to thrive in an AI-driven marketplace. Those that hesitate risk building on brittle foundations where innovation outpaces control.

“Every company will develop its own version of AI for infrastructure and infrastructure for AI,” Sheu said. “The ones that bring those efforts together—guided by governance, performance, and trust—will define the next chapter of digital transformation.”

Click here to read the Q&A based on this interview.

Airrion Andrews
BizTechReports
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