As AI scales in 2026, leaders confront security risks, agentic systems, identity without humans, and the discipline required to make intelligence safe to scale.
If the first act of 2026’s technology story exposed the pressure points—strained power grids, the retreat of AGI hype, and the quiet shift toward edge intelligence—the second act is where consequence takes center stage. The question is no longer whether AI reshapes enterprises, but how safely, efficiently, and accountably that reshaping occurs once experimentation gives way to scale.
As electric utilities buckle under compute demand, as data sovereignty redraws infrastructure boundaries, and as “plug-and-play AI” fantasies collapse under real-world complexity, a more disciplined narrative is emerging. Intelligence is moving closer to where work happens. Automation is becoming agentic. And with that shift comes a new reality: systems now act, decide, and interact at machine speed—often beyond direct human supervision.
In this chapter, technology leaders move past macro trends and into the machinery itself. They speak not about vision decks, but about attack surfaces hidden in prompts, identities without human owners, energy-aware AI architectures, and enterprises preparing for a future where thousands of non-human actors continuously probe, transact, and collaborate. This is the part of the story where the stakes sharpen—and where building responsibly becomes the defining innovation.
The New Attack Surface Is the Conversation Itself
For Elia Zaitsev, CTO of CrowdStrike, the most important shift is already underway—and it’s happening in plain sight.
Prompt injection, he argues, is not a niche vulnerability but a frontier security problem. Just as phishing once defined the email era, prompt manipulation is defining the AI era. Hidden instructions embedded in seemingly benign inputs can override safeguards, hijack autonomous agents, exfiltrate sensitive data, or subtly manipulate outcomes. The interaction layer—the back-and-forth between humans, agents, and models—has become the new perimeter. Prompts, in this world, are the new malware.
By 2026, Zaitsev expects AI Detection and Response (AIDR) to become as foundational as endpoint detection once was. Organizations will need real-time visibility into prompts, responses, agent actions, and tool calls—not for postmortems, but for containment. The goal is not to slow innovation, but to ensure that AI remains a force multiplier rather than a systemic risk.
This shift forces a broader evolution in how security teams operate. Legacy SOCs, designed around human-scale response times, are already outpaced by adversaries using automation and AI to move at machine speed. The answer, Zaitsev suggests, is not replacing analysts, but elevating them—transforming defenders from alert handlers into orchestrators of an agentic SOC.
In this model, fleets of intelligent agents reason, decide, and act across the security lifecycle, always under human command. Success depends on prerequisites that sound less like hype and more like hard engineering: shared environmental context for humans and machines, agents trained on years of expert decisions, validated benchmarks, customizable workflows, and coordinated agent-to-agent collaboration. Analysts are not disappearing. They are being augmented—freed to focus on judgment, strategy, and impact.
Identity Without a Pulse
Nowhere is the tension between power and risk more acute than in identity.
Zaitsev’s warning is blunt: by 2026, AI agents and non-human identities will dwarf human ones. Each agent will operate like a privileged super-user, armed with OAuth tokens, API keys, and continuous access to data that was once siloed. These entities will be both extraordinarily capable and extraordinarily dangerous.
Identity systems built for humans will not survive this shift. Security teams will need instant visibility, real-time containment, and—critically—the ability to trace every agent action back to a human decision. When an AI agent wires money to the wrong account or leaks intellectual property, “the AI did it” will not be an acceptable explanation. Identity security, in this era, means protecting entities that do not have a pulse.
Convergence Becomes Reality
While security leaders wrestle with control, data and infrastructure leaders are grappling with unification.
Francisco Mateo-Sidron, Head of EMEA at Cloudera, sees 2026 as the year convergence finally stops being aspirational. Cloud and on-prem environments are no longer opposing camps but components of a single, seamless ecosystem. Workloads can run anywhere—public cloud, private data center, sovereign environment—without forcing users to care where “anywhere” actually is.
Unified control planes bring these environments into one operational view, shifting attention away from location and toward security, compliance, and performance. Cloudera’s “Anywhere Cloud” strategy and Data Services 2.0 are emblematic of this shift: governance and orchestration become the abstraction layer that matters.
At the same time, Mateo-Sidron points to a quieter but more consequential transformation: energy efficiency is becoming the new performance metric for AI. The era of raw compute bravado is giving way to energy-aware intelligence, where efficiency per kilowatt-hour matters more than sheer scale. Smaller, domain-specific, and edge-optimized models are gaining favor as organizations balance capability with sustainability.
“Energy gravity,” as he describes it, is reshaping global compute strategies—pulling workloads toward regions with cleaner and cheaper power. Hybrid GenAI orchestration and energy transparency are no longer nice-to-haves; they are operational necessities.
The Agentic Enterprise Takes Shape
Beneath these infrastructure shifts lies a deeper change in how organizations operate.
Both Mateo-Sidron and Gopinath Polavarapu, CDAO of JAGGAER, describe the rise of agentic, data-first enterprises. Here, AI agents do more than analyze—they maintain, correct, secure, and optimize data systems themselves. Self-managing pipelines detect bias, address schema drift, and tune performance in real time.
Polavarapu frames this as a turn toward pragmatic AI. After years of pilots and proofs of concept, organizations are confronting an uncomfortable truth: AI cannot compensate for messy, siloed, or outdated data. The impressive demo collapses when it meets a real workflow. 2026 becomes the year enterprises close those gaps—or abandon grand claims altogether.
That pragmatism also fuels the rise of sector-native intelligence. Generic, one-size-fits-all models are giving way to tightly trained systems built for specific domains—financial compliance, clinical records, supply chains—where fluency in context consistently outperforms scale. Smaller models, when well trained, are proving more valuable than larger ones pointed vaguely at everything.
The Coming Storm of Non-Human Interaction
Kev Breen, Senior Director of Cyber Threat Research at Immersive, describes the future in more visceral terms.
As generative AI moves from novelty to infrastructure, the industry is entering what he calls a cybersecurity storm. By 2026, it will not just be humans testing systems, but thousands of autonomous agents—authorized and malicious alike—relentlessly probing every API, interface, and assumption.
This reality exposes two fault lines. The first is identity. OAuth and existing standards were built for humans and static clients, not ephemeral agents spun up by the millions. To secure this future, identity protocols must evolve to treat agents as first-class actors, with full traceability, clear chains of trust, and least-privilege permissions. An agent should have a unique identity—more like a VIN than a username—restricted to exactly what it needs, and nothing more.
The second fault line is visibility. As infrastructure becomes more distributed and ephemeral, traditional monitoring tools are too heavy and too blind. Breen points to the maturation of eBPF-based observability—technologies that run directly in the kernel, providing deep, real-time insight into system behavior with minimal overhead. This level of visibility is not optional. It is a prerequisite for operating securely in an environment where agents never sleep.
Cutting Through the Buzzwords
With so much motion, it is no surprise that language has begun to fray.
Mateo-Sidron is skeptical of the term “full AI autonomy,” arguing that it ignores the realities of governance, compliance, and accountability. What enterprises are actually building are agentic systems that act independently within strict boundaries, continuously monitored and governed by humans. Autonomy without oversight is not innovation; it is negligence.
Breen is even blunter. The phrase that needs to retire, he says, is “AI-powered.” At this point, it signals nothing. AI is becoming a utility—like electricity or the internet—assumed rather than advertised. The real differentiator is no longer the presence of AI, but the outcomes it enables, or even the deliberate absence of it in spaces that prioritize privacy and authenticity. Stop selling the tool. Sell the result.
Polavarapu adds “full agentic orchestration” to the list of inflated promises. While task-specific agents are already delivering value, the vision of multiple agents coordinating complex workflows autonomously remains unreliable. The frameworks are not mature enough, and trust—especially around access control and compliance—is still fragile. The substance will come, but not on the marketing timeline.
A Lesson for the Next Generation
Asked what young professionals should carry forward, the answers converge on one theme: substance over spectacle.
Polavarapu urges them to focus on real friction, not shiny tools. Incremental improvements are forgotten. Solving painful problems—delivering twenty percent gains instead of two—changes careers and organizations alike.
Breen echoes that sentiment from a technical angle. AI can generate code effortlessly, but architecture remains stubbornly human. Deployment, integration, security, and system-level thinking are where complexity lives. The future belongs not to those who can prompt, but to those who can build resilient systems—and who learn by doing, breaking, and fixing the machinery that keeps everything running.
The Shape of What Comes Next
Taken together, these perspectives sketch a future that is less flashy, more demanding, and ultimately more honest.
Technology’s next act is not about replacing humans with machines, but about redefining their relationship. It is about building systems that assume intelligence everywhere—and therefore design for accountability everywhere. It is about convergence, efficiency, and discipline. And it is about recognizing that the most important breakthroughs of 2026 may not be the loudest ones, but the ones that quietly make innovation safe to scale.
