On both sides of the Red Sea, interconnected conflicts metastasize while Washington sends mixed signals.

Conflicts Are Converging Around the Red Sea. U.S. Policy Remains Muddled.

Even before France approved legislation banning social media for children under 15 last January, 13-year-old Benjamin was already wondering what life without social media would look like. “If we want to play football, we won’t be able to organize it. What will we do? Send letters instead?” he joked in an interview for Le Monde. His reaction captured the central challenge behind the growing wave of youth social media bans: Removing access is one thing; understanding what those platforms mean in children’s lives is another. Within weeks of Australia’s similar ban, the country’s eSafety Commissioner reported that platforms had restricted access to 4.7 million under-16 accounts. Two months later, though, one in five Australian teenagers under 16 was still using TikTok and Snapchat, according to a parental-control data company. But even if all children’s social media accounts were to disappear, do such bans actually make children safer online?Governments are moving ahead without answering that question as they follow Australia’s lead. Indonesia’s child-safety framework, which took effect in March, bars children under 16 from holding accounts on “high-risk” platforms. The U.K. government has announced plans to ban social media for under-16s, add default overnight social media curfews for 16- and 17-year-olds, and extend child-safety rules to cover risky AI features. And on 17 September, the European Commission proposed the EU KIDS Act, which would bar children under 13 from social media, set 15 as the EU-wide minimum age for opening an account independently, and require platforms to show that their services are age appropriate and safe by design.But based on my experience working on Child Online Protection initiatives with the International Telecommunication Union (ITU) across Southeast Asia and the Pacific, I know the bans don’t address the real problems. Instead, we should be paying more attention to the systems that generate harm in the first place—namely, recommender algorithms, engagement-maximizing design, opaque moderation, and extractive data practices.Account removals are not the same as online child safetyMy experience working on protecting children’s online safety has taught me three main lessons: First, the public institutions responsible for child online protection often lack the staff, budget, or technical capacity to enforce complex online safety policies. Indonesia is illustrative. A 2026 UNICEF evaluation found capacity constraints among service providers, long-term funding uncertainty, and a need for specialized personnel. At the local level, some staff lacked digital skills, while budget constraints left some areas reliant on external support. Second, many children, and often their parents, lack the digital literacy and critical thinking skills needed to navigate online risks safely. My policy research on child online protection in Indonesia, published earlier this year in Digital Society, found substantial gaps that account removals cannot repair: Many children lacked guidance on navigating the internet safely, and large numbers did not know how to report harmful experiences. And third, the platforms have limited independent oversight as they identify underage users, design age-verification systems, and report their own compliance. In Indonesia, platforms themselves are responsible for carrying out age verification, while the Ministry of Communication and Digital Affairs oversees compliance. TikTok’s appeals process for users flagged as underage, for instance, can require a government-issued ID and selfies, which is a problem because it involves collecting the additional personal data on an ID card, beyond that needed to confirm age. Will government regulators ensure that TikTok handles that data responsibly? The privacy paradox of proving ageEvery age-based ban creates an engineering problem: How can a platform reliably determine that a user is old enough, without intruding on other information? Governments and companies may use identity documents, parental authorization, app-store checks, or facial age estimation. Each approach has trade-offs among accuracy, privacy, accessibility, and resistance to circumvention. There are also technical issues. One tool, facial age estimation, draws on enormous databases but it is probabilistic, not exact, because people vary so much. It’s also been shown to misclassify both children and adults. The challenge should not merely be to “verify age.” It should be to prove that someone is above a threshold, without disclosing their identity, birth date, or other information third parties might use to create a marketing profile. The European Commission’s age-verification blueprint challenges companies to verify ages without collecting all that additional information.Privacy-preserving technologies offer promising ways to achieve this. Zero-Knowledge Proofs (ZKPs) can confirm that someone meets an age threshold without revealing their identity or exact date of birth. W3C Verifiable Credentials are cryptographically verifiable digital claims that can disclose only the information needed, such as “over 16.” And device-based age signals can allow a phone or app store to share an age range without revealing a user’s exact birth date. But these methods still require rigorous security testing, common standards, independent oversight, and clear limits on data retention. Otherwise, poorly designed child-safety policies risk creating permanent identity infrastructures in which businesses, not people, control personal data.Where connection goes when a platform closesBlocking access to a platform redirects some young people, but not always where expected. Early anecdotal reports in Australia pointed to teenagers migrating to smaller, less-regulated platforms like Yope, a pattern the Cato Institute flagged as a “whack-a-mole” problem for regulators. But industry data collected two months later found no broad-based shift of that kind, aside from a small uptick in WhatsApp use. Many teens simply found a way to stay on the banned platforms.This points to a deeper gap in current society: the erosion of youth “third places“ physical spaces where young people have room to socialize and build identity outside home and school. As those spaces have diminished, commercial communications platforms have absorbed that role. For many teenagers, social media workarounds are merely inconvenient. But for isolated, marginalized, disabled, or LGBTQ+ youth who depend on online communities for support that’s otherwise unavailable, displacement can mean losing certain kinds of belonging, or having to move to a platform with even weaker oversight. How to design safer online systems for childrenIf blanket social media bans don’t work, then what will? The platforms have created many of the conditions that governments are now trying to contain: engagement-optimized recommenders, intrusive data practices, weak safeguards against unwanted contact, and features such as infinite scroll, autoplay, streaks, and persistent notifications. These design patterns increasingly face regulatory scrutiny, including what’s required under the European Union’s Digital Services Act. A 2026 study from the 5Rights Foundation that tracked children’s device use minute by minute found that the user interfaces shape children’s attention, sleep, and well-being in real time. A more durable response would regulate those interfaces directly, treating children as legitimate users whose privacy, agency, and well-being are required protections, not afterthoughts. That means designing for safety from the outset. One example would be for children’s apps to have high-privacy defaults, such as private accounts and location sharing switched off for minors. They could also have recommender systems that explain the main factors shaping a feed and give young users more control over personalization. The European Commission has published age-appropriate interaction guidelines that limit unsolicited contact and prevent minors from being added to groups without consent. Rules could also prohibit engagement-maximizing features that demand users’ attention, such as autoplay, infinite scroll, usage streaks, read receipts, and push notifications, by disabling or limiting them by default.Governments should define measurable outcomes and fund independent evaluation, platforms should give researchers meaningful data access, and engineers should audit age-assurance systems for bias and data leakage. Schools, parents, and children themselves need a seat in designing the technology that’s designed to protect children. If policymakers still decide to remove an infrastructure for youth connection, they should offer something better in return. Social media bans may reduce some forms of exposure to harmful content and may be justified for particular ages, services, or risks. But they are just one tool, not a comprehensive substitute for safer design, accountable platforms, digital literacy, institutional capacity, and noncommercial digital “third places”—moderated communities, creative spaces, and public-interest platforms designed for youth participation rather than profit.The first wave of social media restrictions isn’t enough to keep children safe. Governments are still measuring what’s easiest to count, while neglecting harder-to-measure outcomes such as children’s access to safe third places and meaningful social connection, both online and offline. Until governments can show evidence that harm has actually declined, they will keep mistaking account removal for safety.

Social Media Bans Aren’t Enough to Make Children Safe

Imagine the same detective working two different cases. In the first, every piece of evidence arrives as a disconnected fragment—names without relationships, events without timelines, motives without context. In the second, the connections among people, events, and evidence are already established before the detective walks in. Which case does the detective solve faster? The detective solves the second case much faster than the first, but not because the detective is more intelligent. Instead, the world is more intelligible. The difference is not reasoning ability; it is the organization of knowledge that represents the world and enables action. Now, imagine two cybersecurity AI agents built on the same frontier language model, with the objective of defending against attacks. Both share a general understanding of vulnerabilities, attackers, and defensive techniques. One, however, operates over fragmented alerts, disconnected data sources, and inconsistent identifiers. The other agent reasons over a structured representation that captures relationships among assets, vulnerabilities, threat actors, detections, and organizational context. Similar to detectives, the difference in performance stems not from greater intelligence but from a more intelligible environment or even a living enterprise model. This is my primary argument: Agentic intelligence is about constructing a representation of the operational world in which correct decisions become the expected outcome rather than a matter of chance. As agents begin taking actions rather than merely producing answers, the quality of those actions depends mostly on the context the agents inherit and whether it’s structured, current, and backed by trustworthy evidence. To understand how this works for machines, it helps to examine human learning. Human progress has never depended on information alone. It has depended on organizing that information—deciding which sources to trust, connecting related facts, preserving context, and passing that knowledge forward. We inherit this structure through education, language, institutions, and shared practice. Remove it, and even sophisticated reasoning becomes little more than educated guesswork. Intelligence doesn’t emerge in isolation. It depends on the quality of the representations we think with. The quality of reasoning depends not only on our ability to draw conclusions, but also on how well the world has been made intelligible before we begin. Trustworthy agents do not emerge from better prompts. They emerge from better operational world models. Humans develop reasoning by building increasingly rich mental models of the world through observation, interaction, and experience. Modern language models follow a different path. They learn statistical representations from vast amounts of data rather than through direct interaction with the environment. As a result, they possess broad knowledge but lack direct access to the current operational state of the world in which they must act. This distinction is particularly important in cybersecurity. Human analysts continuously integrate observations, institutional knowledge, and information about operational state into a coherent mental model of their environment. AI agents must instead be provided with that operational model. Assets, identities, vulnerabilities, dependencies, relationships, policies, and evolving evidence cannot be inferred reliably from language alone—they must be represented explicitly in the world the agent reasons over. Foundation models are increasingly capable of planning, selecting tools, interpreting observations, and adapting their behavior. Their effectiveness, therefore, depends less on reasoning in isolation than on the quality of the representations they can access and the actions they can take. Those representations encompass not only organizational knowledge, like entities, relationships, provenance, policies, governance, and evolving state, but also the tools available to observe and change that state. Again, general models generate general answers. During the development of enterprise AI agents at Recorded Future, we encountered many instructive examples. The first lesson came immediately. Early versions of our agents treated open-source information and Recorded Future’s proprietary intelligence with roughly equal weight. The result was generic, internet-scale responses rather than the expert-level analysis we intended. Through repeated experimentation, we changed the architecture so that the agent reasoned primarily over the Recorded Future Intelligence Graph®1—a structured representation of the cyber threat landscape built from years of analyst research, enriched relationships, and investigative decisions. The agent learned to prioritize high-confidence analytical artifacts, such as Insikt Notes2 and curated graph relationships, over weaker signals derived from open sources. The language model itself had not become more intelligent. What changed was the quality of the world it was given to reason about. The result was an agent that produced more authoritative analyses and acted with substantially greater consistency and confidence. Frontier models are becoming easier to access every month. That means model choice is becoming a weaker source of competitive advantage than many organizations assume. What’s much harder to copy is a trustworthy representation of an organization’s operational knowledge. While production-grade orchestration remains challenging, it is unlikely to provide a durable moat for agentic systems in cybersecurity as frameworks, tooling, and implementation patterns continue to converge. What cannot be commoditized is a trusted operational representation of the organization. Agents struggle wherever knowledge remains implicit—in institutional expertise, undocumented conventions, or relationships no system has captured. Their performance ultimately reflects the quality of the world they can access. Consider a seemingly simple Priority Intelligence Requirement (PIR): “What is a threat to our organization?” The answer is rarely explicit or universally agreed upon. It emerges from understanding the external threat landscape together with the organization’s own high-value assets, priorities, and risk posture. Constructing that representation is a deliberate analytical exercise, not a by-product of deploying an AI model. Once made explicit, however, it provides the foundation on which agents can reason consistently and act effectively. The differentiating question is therefore not, “Which agent framework do we use?” but, “What operational world do our agents reason over?” Organizations that invest in making their knowledge explicit, structured, and trustworthy will consistently derive more value from AI than those that deploy increasingly sophisticated models against fragmented operational knowledge. In this case, intelligence is truly an architecture rather than computation. To build a structured world for cybersecurity agents to operate in, enterprises should align with the following principles: Structure before reasoning. An effective AI system does more than retrieve facts. Too many systems repeatedly reconstruct knowledge from unstructured information, wasting computational resources while producing inconsistent results. Instead, intelligent and trusted agents should inherit an organized representation of the world: relationships between entities, events, organizations, and evidence should already exist within a structured living knowledge system. The agent's responsibility is to reason over this established structure, not rediscover it with every interaction. The result can be greater consistency and reliable outcomes, particularly for tasks that depend on relationships across multiple sources. Whether it also lowers total cost depends on how the representation is constructed, maintained, and reused3. We observed the same pattern when building an autonomous threat hunting capability in our Autonomous Threat Operations (ATO)4. The language model already understood cybersecurity concepts—it knew what YARA rules, Sigma rules, MITRE ATT&CK techniques, and behavioral indicators were. The breakthrough did not come from teaching more cybersecurity data. It came from explicitly representing investigations: translating detection rules into behavioral hunts, preserving relationships between evidence, maintaining provenance, and carrying forward prior investigative decisions. An investigation that once required analysts to repeatedly reconstruct context became dramatically faster because the context itself had become part of the system. Before the change, each run reconstructed the relationship between the rule, the relevant behavior, and prior investigative findings. After those relationships became a durable system state, the agent could begin from the current investigative position rather than restarting from the source material. As a result, the agent could act effectively around the clock at greater speed. Provenance is inseparable from intelligence. Human experts are trained to question sources, recognize outdated information, and detect uncertainty. AI systems cannot make these judgments unless every piece of information carries evidence of its origin, relevance, and reliability. Provenance is what transforms data into trustworthy intelligence5. For operational intelligence, provenance should include the source; the precise evidence used and its freshness, confidence, and known contradictions; and the relationship between that evidence and the resulting claim. Without provenance, even sophisticated reasoning is reasoning built on assumptions rather than verified knowledge. A major pain point for cyber threat intelligence (CTI) teams is that, on any given day, thousands of articles, social media posts, blog entries, and automated alerts may mention an entity of interest to them. Most are operationally irrelevant: news commentary, recycled reporting, speculative claims, or duplicate coverage. The intelligence value lies not in the volume of mentions but in understanding their provenance. Is the information derived from a trusted government advisory, firsthand reporting from an intrusion, or direct observations from a darknet forum? Or is it simply another outlet repeating yesterday’s story? Provenance enables an AI agent to distinguish original evidence from repetition, assess freshness and credibility, and focus attention on developments that materially change the threat landscape rather than amplifying noise. Verification must be easier than investigation. Trust matters more than raw capability. An AI system that produces impressive answers but cannot explain how it reached them creates uncertainty rather than confidence. Every recommendation should be accompanied by sufficient evidence to make verification substantially easier than reproducing the investigation itself. This includes the observations used, material assumptions, uncertainty, contradictory evidence considered, and the conditions under which the recommendation should not be followed. If it doesn't, the technology has merely shifted labor instead of reducing it. Transparency is the foundation on which trust is built. This principle increasingly aligns with emerging thinking in frontier AI governance, where trust depends not only on model capability but on rigorous verification of the evidence, processes, and claims underlying AI systems6. A CISO, good friend, operating under significant resource constraints, once asked whether an AI agent could reliably recommend isolating compromised production servers. My answer was yes—but only under one condition. A threat analyst should never have to reconstruct the entire investigation just to decide whether to trust a recommendation coming from an AI agent (it is also very costly). Instead, the AI agent should present the evidence for the underlying conclusion, like the suspicious process it observed, the connection to known malicious infrastructure, the behavioral detections that fired, the supporting threat intelligence, the confidence of each finding, and any contradictory evidence it considered, but also the reasoning that connects those observations. Evidence alone is insufficient; the same facts can often support multiple competing hypotheses. The goal is not for analysts to reconstruct every investigation from first principles, but to quickly assess whether the agent’s chain of reasoning is sound and whether the recommended action is proportionate to the available evidence. This becomes even more important as agents transition from advisory to autonomous roles. Defensive agents offered by the industry will increasingly be expected to isolate hosts, revoke credentials, or block communications before a human can intervene. In those settings, trust cannot depend on manual verification of every decision. It must be earned through consistently reliable reasoning, calibrated confidence, transparent evidence, and well-defined operational guardrails. Verifying the recommendation should take minutes; reconstructing it from raw telemetry could take hours. Otherwise, the AI has not reduced the workload—it has merely shifted it from investigation to verification. Efficiency is an intellectual discipline. Many assume that more reasoning necessarily produces better intelligence, yet excessive computation often reflects poor system design rather than greater capability. Efficient intelligence is not about thinking longer; it is about thinking more precisely. A well-designed agent retrieves only what is relevant, avoids unnecessary repetition, and allocates computational effort where it genuinely adds value. Efficiency is not merely an economic concern—it is a measure of intellectual discipline7. In my ongoing conversations with security leaders, one concern has surfaced repeatedly: Organizations cannot afford to reproduce the inefficiencies of alert fatigue with AI agents. Replacing thousands of alerts with thousands of autonomous actions would simply automate the noise. Agents are only as effective as the intelligence, policies, and evidence they operate upon. If the underlying representations contain duplicates, false positives, stale context, or conflicting evidence, autonomous systems will amplify those weaknesses at machine speed. The goal is therefore not to maximize agent activity but to maximize decision quality. The most valuable agent consistently performs the right actions while knowing when not to act. Reasoning must be preserved, not just data. As organizations deploy multiple intelligent agents, preserving reasoning becomes as important as preserving data. What must persist is not an unrestricted transcript of prior reasoning, but a governed record of evidence examined, hypotheses considered, assumptions made, decisions taken, unresolved questions, confidence levels, and conclusions that have subsequently become stale or invalid. Intelligence is cumulative. Human collaboration succeeds because people exchange not only conclusions but also assumptions, uncertainties, confidence levels, and unresolved questions. Intelligent agents should communicate in the same manner: Each agent should inherit the complete investigative state rather than merely a final answer. This enables cumulative reasoning—each participant building on prior understanding rather than restarting the thinking process—and creates consistency, transparency, and trust across increasingly complex systems. One of the most common cyber cases I encounter is a ransomware investigation involving multiple analysts, shifts, and, increasingly, specialized AI agents. Whether the next participant is human or machine, they should not begin with only the latest conclusion. They need to inherit the investigative state: the evidence already examined, assumptions made, confidence assigned, competing hypotheses, unresolved questions, and conclusions that have since been revised as new evidence emerged. Without that shared state, every handoff requires reconstructing why earlier decisions were made and which lines of inquiry were already explored. With it, the investigation progresses cumulatively rather than repeatedly restarting, reducing duplication while improving consistency, auditability, and decision quality. Intelligence must outlive the interface. Technology platforms, user interfaces, and AI models will inevitably evolve, but high-quality intelligence should remain portable, structured, and universally accessible. Organizations should not become dependent on a single application or agent. Intelligence should function as a foundational layer that supports any workflow, regardless of where or how it is consumed. Threat intelligence organizations have spent years enriching adversary profiles, linking infrastructure, documenting investigative decisions, and building behavioral knowledge—not to mention identifying all the IT debt organizations have. If that intelligence exists only inside one vendor’s application or is encoded in prompts for a single AI assistant, every platform migration forces the organization to rebuild years of accumulated knowledge. By contrast, when intelligence is represented independently (as structured entities, relationships, evidence, and provenance), it can support any analyst, detection engine, hunting agent, or future AI model without having to be recreated. Customer relationships, business policies, operational knowledge, and historical decisions should not be lost when an organization replaces its CRM, SIEM, or AI provider. The applications may change; the intelligence they rely upon should not. Durable intelligence becomes a shared asset that every future system can inherit. Every major platform migration exposes the same problem: Organizations have preserved their applications better than their intelligence. None of this diminishes the human role; in fact, it centers it. Rather than forcing analysts to build every investigation from scratch, intelligent systems should prepare a structured foundation upon which human expertise can operate. AI becomes a collaborator rather than a substitute. The highest value of an intelligent agent is not that it thinks independently, but that it enables humans to think more effectively. For decades, we have treated intelligence as something people consume: reports, recommendations, indicators, documents. Agentic systems are forcing us to take a different view. Intelligence has become part of the environment in which reasoning occurs. The competitive advantage will not belong to organizations possessing the largest models. It will belong to those who build trustworthy systems where knowledge is structured, evidence is transparent, reasoning is efficient, and human judgment remains central. Models will change. Interfaces will disappear. Workflows will evolve. What remains is the architecture of enterprise intelligence itself.

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