Why the AI-Native Generation Needs a Different Cybersecurity Model

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For a new generation of AI-native individuals, who experience technology not as a static tool but as a living system that observes, learns, predicts, and adapts, cybersecurity becomes not just a technical safeguard but a foundational condition of trust, agency, and resilience.

Renen Hallak, CEO of VAST Data, says that for AI-native youth, cybersecurity “isn’t just about protecting their devices,” but about protecting “their identity, their behavioral patterns, and their long-term or even short-term data trails.” 

The expanded threat surface, where risk resides in inference rather than intrusion, is forcing security leaders to rethink their foundational assumptions. Increasingly, the attack surface is no longer hardware at all.

When the Threat Surface Becomes Intelligent

When the primary threat surface shifts from devices to intelligent systems, Corey Thomas, CEO of Rapid7, explains that “the tech changes, and so does your geography, your data jurisdiction, and the chain of trust across each function.” Devices, he notes, are predictable, whereas intelligent systems are “dynamic and highly adaptive.”

This transition has measurable consequences. Gartner estimates that by 2027, more than 40% of data breaches will involve misuse of generative or agentic AI systems rather than traditional software vulnerabilities. 

In this environment, Thomas says that trust hinges “less on access control or patching and more on model integrity, data provenance, and output reliability.”

For AI-native generation, cybersecurity failures may never resemble a breach notification. Instead, risk appears as persistent behavioral nudging, distorted feedback loops, or algorithmically reinforced biases; invisible, cumulative, and difficult to challenge.

Data Exposure and the Permanence of Memory

The danger is not simply that data is collected, but that it can be endlessly recombined and reinterpreted. Hallak warns that “if we expose our data to the world, we won’t be able to control it anymore,” particularly as AI becomes increasingly adept at finding disparate data sources and assembling them into coherent narratives that can “retrace basically everything we’ve done.”

Research from the Oxford Internet Institute shows that anonymized datasets can be re-identified with over 90% accuracy when cross-referenced at scale. For children, whose identities are still forming, this creates a profound imbalance between youthful experimentation and lifelong traceability.

This is why Hallak insists that foundational data architecture must act as a primary security control. 

As AI systems evolve into autonomous agents, he says that “different agents should have different data access abilities.” A personal agent should operate with complete visibility into its owner’s life, while a workplace agent must be strictly confined to professional data. The distinction matters: if my employment ends tomorrow, the knowledge and data generated in that role remain the organization’s property—not an extension of my personal identity.

Without such distinctions, personal histories will be permanently entangled with institutional systems long after those relationships end.

From Generative AI to Agentic Societies

Generative AI has revolutionized the way content is produced; agentic AI will change how societies function. While generative systems focus on producing text, images, and video, agentic systems perform tasks, collaborate with other agents, and increasingly act in the physical world.

McKinsey estimates that AI agents could automate or augment up to 70% of white-collar tasks by 2030, with robotics extending similar capabilities into construction, logistics, and maintenance.

Hallak believes this shift will be transformative, noting that “agentic AI will have a much bigger impact than what generative AI did in the past.” 

For AI-native youth, this means growing up alongside systems that not only inform them but also act on their behalf, shaping educational pathways, social exposure, and opportunities. In this context, cybersecurity must encompass explainability, contestability, and psychological safety, in addition to technical safeguards.

Traceability as a Requirement for Trust

The scale of AI-generated content makes provenance non-negotiable. As Hallak says, “If it took a human a year to produce something earlier, now a machine can do it in a day.” This triggers a content deluge that overwhelms existing norms and demands entirely new systems for storage, retrieval, and—critically—verification.

The World Economic Forum estimates that by 2026, more than 90% of online content could be partially or fully AI-generated. Without lineage and reproducibility, distinguishing fact from fabrication becomes increasingly tricky. Hallak says that for AI to be deployed in high-stakes environments, “it needs to be verifiable,” with the ability to reconstruct what a system knew, when it knew it, and how a response was generated.

For AI-native youth, this capability underpins trust, particularly in education, where studies show that students are significantly less likely to challenge information presented confidently by automated systems, even when it is incorrect.

Digital Psychological Safety by Design

Cybersecurity for AI-native youth must also account for mental and emotional resilience. Thomas notes that “we are all products of the technology we grew up with,” and that every new wave brings both skepticism from older generations and boundary-pushing from younger ones.

However, the rapid proliferation of AI makes it unrealistic to assume that all systems meet expectations for safety or data quality. Thomas says that adoption must be accompanied by greater understanding, including the ability to recognize when AI systems hallucinate and to make informed decisions about sharing personal data.

Research published in Nature reveals that adolescents exposed to opaque recommendation systems are less able to distinguish between machine authority and human expertise. Conversely, studies from MIT and Stanford demonstrate that users trained to understand probabilistic AI outputs are up to 60% less likely to over-trust automated systems, suggesting that psychological safety emerges from comprehension rather than restriction.

Why Minors Require a Different Security Model

Most cybersecurity frameworks still assume a uniform user profile. Thomas points out that “general-purpose frameworks rarely address the specific needs of minors,” despite strong evidence that children face distinct risks.

UNICEF and OECD research indicate that minors have limited capacity to assess the long-term consequences of data and are more susceptible to manipulation. Neurodevelopmental studies confirm heightened sensitivity to reward-based engagement mechanisms, a core feature of many AI systems. In an AI-mediated world, treating children and adults identically does not produce fairness; it creates vulnerability.

Building Youth Protection into National Infrastructure

These challenges are especially acute in the GCC, where national ID systems, AI-powered education platforms, and digital citizen services are expanding rapidly. Thomas notes that AI-driven threats have “emerged with full force across all industries, including education,” and that recent cybersecurity incidents in the region have cost organizations an average of $8.5 million per breach.

International case studies indicate that systems designed with built-in transparency, consent layering, and continuous monitoring reduce long-term data misuse by more than 30% compared to those where safeguards are added later. Hallak emphasizes that enforcement depends on control, adding that “you can’t enforce the rules unless you’re in control of the infrastructure end to end.”

Whoever succeeds in pairing sovereign infrastructure with effective governance, he suggests, will gain a lasting competitive advantage.

An Industry Still Catching Up

Is the cybersecurity industry moving fast enough? Thomas believes there is still “an adoption curve” in how organizations deploy AI and train their teams to understand it. Surveys of CISOs indicate that fewer than half feel confident in their ability to comprehensively secure AI systems.

The gap, Thomas says, is not only technical but institutional. Greater knowledge sharing about architectures, governance models, and both successes and failures will be essential to ensure AI risks remain manageable rather than systemic.

The safety of the AI-native generation will depend less on warnings or restrictions and more on how intelligence itself is designed. It is an architectural decision—one that fuses data integrity, human development, and societal trust at the deepest level.

In a world that never forgets, protecting the right to evolve may be the most consequential security challenge of all.