Blog · Review Essay · Last reviewed June 25, 2026

The Internet in Everything and the Control Network

Laura DeNardis's The Internet in Everything: Freedom and Security in a World with No Off Switch is a governance book about the moment when the internet stops being a communications layer and becomes part of cars, homes, medical devices, factories, energy systems, payment rails, toys, cameras, and bodies. Its AI-era value is direct: machine intelligence cannot be governed only at the model layer when the network it acts through has already entered the physical world.

A control network is a connected system that senses a condition, turns it into machine-actionable data, and can change material options through software, devices, alerts, locks, vehicles, meters, robots, medical workflows, pricing, queues, or human operators following a dashboard. Control does not require a robot arm or a conscious machine. It can happen when a sensor record becomes a threshold, a dispatch order, a denial, a risk score, a maintenance priority, or a compliance flag. The issue is the merger of sensing, interpretation, ownership, and actuation.

The practical test is the actuation boundary: what can this connected system change in the world, who authorized that change, what local safe state exists when the network fails, and how can an affected person challenge the record that triggered it?

The Book

The Internet in Everything was published by Yale University Press in 2020. The Yale listing gives the hardcover at 288 pages, with print ISBN 9780300233070 and ebook ISBN 9780300249330. Oxford Academic's Yale Scholarship Online record places the book in public policy and summarizes its central claim: the diffusion of internet connectivity into physical systems escalates governance problems around privacy, discrimination, safety, democracy, and national security.

JSTOR's table of contents shows the structure clearly: "After the Internet," "The Cyber-Physical Disruption," "Privacy Gets Physical," "Cyber-Physical Security," "Interoperability Politics," "The Internet Freedom Oxymoron," "Disruptions to Global Internet Governance," and "The Cyber-Physical Policy Moment." That sequence matters. DeNardis is not writing a gadget book. She is writing about the political architecture of connected matter.

The author is an internet governance scholar whose work has long focused on the hidden technical arrangements that decide public outcomes. Yale's publisher page identified her at publication as a professor in American University's School of Communication and author of The Global War for Internet Governance. Her current author page lists her as an endowed professor of Technology, Ethics, and Society at Georgetown University and director of Georgetown's Center for Digital Ethics. The book sits in that tradition: infrastructure first, politics already inside it.

Current Context

As of June 25, 2026, cyber-physical governance has moved from warning language into a patchwork of standards, labels, guidance, and law. NIST IR 8259r1, published in April 2026, frames foundational cybersecurity activities for IoT product manufacturers across pre-market and post-market work, including maintenance and end-of-life considerations. NIST SP 800-213 gives federal organizations a way to define IoT cybersecurity requirements during acquisition and system risk management. ETSI EN 303 645 V3.1.3 remains a baseline consumer-IoT security standard.

The legal and regulatory picture is uneven by design. The FCC's U.S. Cyber Trust Mark is a voluntary cybersecurity labeling program for wireless consumer IoT products. The UK's Product Security and Telecommunications Infrastructure product-security regime has applied to consumer connectable products since April 29, 2024. The EU Cyber Resilience Act, Regulation (EU) 2024/2847, entered into force on December 10, 2024; official EU materials state that reporting obligations apply from September 11, 2026 and main obligations from December 11, 2027. FDA medical-device cybersecurity guidance treats cybersecurity design, labeling, and premarket documentation as part of device resilience.

NIST's April 2026 concept note for an AI Risk Management Framework profile on trustworthy AI in critical infrastructure makes the AI layer explicit: critical-infrastructure operators increasingly use AI-enabled capabilities across information technology, operational technology, and industrial control systems. That is the exact place where DeNardis's book becomes current policy reading: the model, the network, and the actuator are no longer separable governance objects.

This current context sharpens DeNardis's point. The connected world is not governed by one master rule. It is governed by overlapping device classes, jurisdictions, standards bodies, procurement choices, market labels, vulnerability-disclosure practices, sector regulators, and vendor support lifetimes. A serious review has to keep those categories separate because a voluntary label, a federal acquisition guide, a consumer-product statute, and a medical-device submission requirement give affected people different kinds of leverage.

The most important update is lifecycle governance. A connected product is not finished at sale or installation. Its safety depends on patch delivery, certificate and credential management, vulnerability disclosure, cloud-service continuity, component replacement, end-of-support notices, repair channels, and documented retirement. An abandoned device can remain a sensor, actuator, or attack surface long after the vendor has moved on.

From Communication to Control

The book's strongest move is to treat the Internet of Things as a change in kind, not merely a change in scale. A screen-based internet mostly mediated symbols: messages, documents, pictures, video, money, identity, attention. A cyber-physical internet mediates consequences: door locks, insulin pumps, cars, thermostats, industrial sensors, cameras, drones, logistics systems, farms, hospitals, and energy grids.

That shift changes what governance means. A moderation mistake on a social platform can ruin reputations, distort public knowledge, or incite harm. A failure in a connected medical, mobility, or infrastructure system can injure bodies directly. A compromised camera network, traffic system, building control, or industrial device can become a surveillance layer, attack surface, labor monitor, or coercive instrument.

This is why the phrase "no off switch" is not just dramatic packaging. The old user could log off in theory, even if platform dependence made that hard in practice. The cyber-physical user often cannot. Connected systems are embedded in rented apartments, workplaces, schools, streets, cars, public benefits offices, hospitals, warehouses, and municipal infrastructure. Opting out becomes less like leaving a website and more like refusing the built environment.

A control network also has a lifecycle: sensing, classification, escalation, actuation, logging, review, and update. Each step needs a named owner and a contestable rule. Who may collect the signal? Who decides that a threshold has been crossed? Who can push a firmware change? What happens when the network is down? What local safe state exists if the vendor account, cloud service, or model output fails? DeNardis's argument becomes sharper when treated as a sequence of institutional decisions, not a general mood about connectivity.

That lifecycle also shows why "control" should be read concretely rather than conspiratorially. A connected meter can change billing, a badge reader can change access, a camera can change policing, a fleet tracker can change labor discipline, and a health device can change clinical workflow. The network controls by making some options easier, more visible, faster, cheaper, or mandatory, and by making other options harder to notice or justify.

The governance unit is therefore not "the device" alone. It is the system of device, firmware, cloud service, account, vendor, integrator, data recipient, operator, and affected person. A smart lock with a landlord dashboard is a housing governance system; a warehouse scanner with productivity analytics is a labor governance system; a medical sensor with a cloud dependency is a care governance system. The device is only the visible endpoint.

The Body as Endpoint

DeNardis's examples matter because they move the internet from the abstract public sphere into ordinary vulnerability. Wearables turn bodies into data emitters. Cardiac monitors and medical devices bring network security into care. Smart homes make domestic space visible to vendors, police requests, landlords, abusers, and compromised accounts. Connected cars and drones make mobility and surveillance part of the same technical field.

The LSE Review of Books account emphasizes this loss of a clean boundary between virtual and physical life. That is the hinge for AI governance. A system that classifies text is different from one that classifies a pedestrian, redirects a vehicle, adjusts a medication workflow, flags a worker, unlocks a door, or routes an emergency response. The question is no longer only whether a model knows. It is what the connected environment lets that model do.

This also changes the meaning of legibility. Networked devices do not merely observe. They format reality into machine-actionable traces: location, movement, temperature, pressure, images, heart rhythms, proximity, voice, payment, presence, energy use, and machine state. Once the world is captured this way, institutions can score it, automate it, insure it, police it, price it, deny it, optimize it, or claim it as evidence.

That makes safety inseparable from power. A device attached to a body, a factory line, a vehicle, or a utility is not just an app with worse consequences. It is a dependency that can become unavailable, misconfigured, remotely changed, or interpreted against the person who relies on it. The relevant question is not whether the device is innovative. It is whether the person exposed to it has notice, alternatives, repair, support, redress, and a way to survive its failure.

The body-as-endpoint frame also changes incident severity. A breach is not only stolen data when it changes an insulin workflow, unlocks a door, exposes an abuse survivor's location, disables a vehicle feature, or causes a caregiver to trust a false alert. Cybersecurity becomes bodily safety when the network can alter conditions of care, shelter, movement, and work.

Privacy Gets Physical

The privacy chapter title is exactly right: privacy gets physical. In a browser, privacy harm can already be serious, but the object being tracked is often a profile. In cyber-physical systems, the profile is joined to rooms, bodies, vehicles, tools, workplaces, and neighborhoods. The record is no longer only about what someone read or clicked. It can include where they slept, how they moved, when they opened the door, how fast they drove, which shelf they scanned, what machine they touched, or whether a device inferred distress.

Consent breaks down under those conditions. A visitor does not meaningfully consent to every microphone, camera, sensor, router, badge reader, and smart appliance in a room. A worker does not freely bargain with every wearable, fleet tracker, warehouse scanner, or productivity sensor when employment depends on compliance. A tenant does not control every connected lock or utility sensor. A patient may have no practical alternative to networked care infrastructure.

The International Journal of Communication review is useful here because it notes the book's tension: cyber-physical systems can create real benefits while also producing severe privacy, security, and human-safety complications. The point is not to reject every connected device. The point is to stop pretending individual choice can govern a public infrastructure that surrounds people before they can evaluate it. The stronger standard is contextual integrity: data collected for care, maintenance, safety, or access should not quietly become data for discipline, pricing, advertising, policing, or denial. That connects directly to the site's data minimization and privacy and data work.

A useful privacy review therefore asks about bystanders and secondary subjects, not only registered users. A doorbell camera records neighbors and passersby; a classroom sensor records children who did not choose the contract; a fleet tracker records passengers and locations; a smart speaker records visitors. Cyber-physical privacy is often imposed by someone else's device.

Standards as Politics

One reason the book belongs beside media theory and platform governance is its attention to interoperability. Technical standards can sound neutral until they decide who can connect, who can inspect, who can repair, who can exit, which vendor controls an ecosystem, and which security assumptions travel across devices. In the cyber-physical world, compatibility is not a convenience feature. It is a political settlement.

Closed systems can make people dependent on one vendor for devices that mediate safety, mobility, home life, or work. Open systems can improve repair and competition, but can also enlarge attack surfaces if governance is weak. Fragmentation can limit systemic failure, but it can also trap users in incompatible silos. There is no purely technical answer because every architecture distributes power differently.

This is the institutional lesson: technical governance becomes constitutional before most publics notice. Naming protocols, standards bodies, certification regimes, update policies, security disclosures, procurement rules, and liability arrangements is not boring housekeeping. It is how control over connected reality is allocated. A procurement clause about patch support, a requirement for vulnerability disclosure, or a rule against universal default passwords can matter more than a public slogan about innovation.

The hard cases are not solved by choosing "open" or "closed" once. They require governance at the boundary: documented interfaces, secure update channels, repair and exit rights, responsible disclosure, liability for abandoned devices, and public capacity to evaluate systems that vendors describe as proprietary. That is the same institutional problem discussed in vendor and platform governance and digital infrastructure.

Attestation belongs in that boundary politics. A device may need to prove software state or hardware integrity before joining a network, but an integrity proof is not a blanket license to act on people. The claim should stay narrow: what was attested, by whom, under which policy, for which action, and with what fallback when the proof is missing or wrong. Otherwise the trust layer becomes another private gate over public life.

The AI Reading

Read from 2026, The Internet in Everything is a precondition for serious AI governance. Models act through infrastructure. Agentic systems need accounts, APIs, sensors, databases, permissions, payment rails, robots, cameras, browsers, devices, and institutional workflows. The more connected the world becomes, the more an AI system can become a control surface for that world.

This changes the risk model. A hallucinated answer is one class of failure. A hallucinated answer routed through a connected medical workflow, building system, police dashboard, vehicle interface, industrial controller, or benefits portal is another. When intelligence is attached to cyber-physical infrastructure, errors can become actions; actions can become records; records can become institutional truth.

The recursive loop is straightforward. Sensors make the world legible. Models interpret the legible world. Institutions act through the interpretation. Those actions reshape behavior and infrastructure. The changed world produces new sensor records, which then appear to confirm the system's model of reality. A smart city, warehouse, hospital, school, or border checkpoint can become a feedback machine long before anyone calls it artificial intelligence.

This is also why AI safety cannot be only a matter of model alignment or benchmark performance. It has to include permissions, failover, logging, redress, physical safety, procurement, labor process design, vendor lock-in, incident disclosure, repair rights, public-sector capacity, and the ability to disconnect without losing ordinary life. The site's AI in cybersecurity page treats this as an attack-surface problem; the cybernetics review treats it as a democratic-control problem. DeNardis gives both arguments a physical substrate.

The agent layer adds a specific hazard: tool permissions can turn observation into action. A browser agent, building-management assistant, logistics optimizer, care-coordination bot, or maintenance planner should therefore be governed like a delegated operator. It needs bounded authority, evidence logs, human override, tested failure modes, and a clear distinction between recommendation, queueing, remote actuation, and final institutional decision. That connects this review to AI browsers and computer use, embodied AI and robotics, agent tool permissions, and agent observability.

The minimum AI safety distinction is observe, recommend, queue, command, and execute. A model that observes sensor data is not the same risk as a model that recommends maintenance, queues a work order, changes a thermostat, unlocks a door, disables a machine, or updates a clinical record. Procurement and audit files should name the highest action level the system can reach, not just the model name.

Governance and Safety

Governance starts by naming the object. A thermostat, insulin pump, camera, warehouse scanner, emergency-alert system, traffic sensor, public-benefits portal, fleet tracker, robot, and medical workflow are not one risk class. Each differs by data sensitivity, actuator power, network exposure, support lifetime, user choice, affected population, and available remedy.

Those differences decide which controls matter most. NIST and CISA guidance can shape procurement and professional expectations, but they are not general consumer statutes. ETSI is a standard. The FCC mark is voluntary. The UK and EU regimes create legal duties in their jurisdictions. FDA guidance is specific to medical-device submissions and resilience. "There is a standard" is therefore not the same thing as "a harmed person has an enforceable remedy."

The practical controls are concrete. A connected deployment needs an asset inventory, an accountable owner, data-flow maps, a support end date, secure update mechanisms, no universal default passwords, vulnerability disclosure, logging, incident reporting, network segmentation, least-privilege access, local safe modes, manual override, repair and export rights, and a plan for what happens when the vendor, cloud service, or model output is unavailable. In hospitals, schools, public benefits, workplaces, policing, housing, elder care, transport, and utilities, it also needs notice, appeal, human review, and a public record of serious failures.

For AI systems attached to sensors and actuators, the bar should be higher still. Tool access should be explicit and narrow. Consequential physical actions should require tested authority boundaries, per-action permissions, sandbox or simulation evidence, human approval where harm is plausible, rollback, kill-switch access, provenance logs, and post-incident review. "Smart" should never mean that a vendor can silently change the behavior of a device people depend on.

A control-network dossier should exist before deployment in high-impact settings. It should identify the sensor and actuator inventory, model or automation role, account owners, vendors and subcontractors, data flows, retention rules, update authority, support end date, emergency contact, local fallback, manual override, vulnerability-disclosure channel, incident log, accessibility impact, bystander impact, and appeal path. Without that dossier, the institution has bought a dependency it cannot govern.

Public-sector and essential-service deployments need a stronger public record. A school, hospital, benefits office, transit agency, utility, housing provider, jail, or emergency-management office should be able to say which connected systems make or influence consequential decisions, which failures have occurred, what changed after incidents, and how affected people can reach a human route when the device, network, vendor, or model is wrong.

The Actuation Boundary Dossier

The governance artifact this review takes from DeNardis is an actuation boundary dossier. It should describe, in one place, the highest level of real-world action a connected system can reach: observe, infer, recommend, queue, command, execute, lock, unlock, move, dose, bill, deny, disclose, or write an official record. That boundary should be written before procurement, not discovered during an incident.

The dossier should separate four claims that are often blurred. A security claim says whether the device can resist compromise. A privacy claim says what data is collected, retained, inferred, shared, or deleted. A safety claim says what happens when the device, network, model, or cloud service fails. A governance claim says who may authorize, override, repair, appeal, retire, or publicly account for the system. Passing one category does not satisfy the others.

For AI-enabled control networks, the dossier needs an action receipt for consequential events: sensor source, model or rule version, prompt or policy where relevant, input record, output, human reviewer, tool call, actuator command, local safe-state check, override availability, affected person, notice given, and appeal or correction route. That receipt is the difference between an intelligent environment and an unreviewable environment.

The dossier also forces a hard refusal question. If the system cannot operate safely without a vendor cloud, cannot be patched after support ends, cannot preserve logs without over-collecting bystander data, cannot be manually overridden by trained local staff, or cannot explain why a physical action was taken, then it should not be used in essential services until the boundary is redesigned.

Where the Book Needs Friction

The book is strongest at the infrastructure and governance layer, which means readers should pair it with accounts of labor, race, disability, housing, policing, and environmental extraction. Connected infrastructure does not touch everyone in the same way. A smart thermostat in an owner-occupied home, a surveillance camera in public housing, a delivery scanner in a warehouse, a GPS ankle monitor, and a networked medical device all raise different questions of power and exit.

It also predates the current generative-AI wave. That is not a defect, but it means the reader has to extend the argument. DeNardis gives the networked control environment. Today's AI systems add synthetic interpretation, automated summarization, adaptive interfaces, agentic action, and predictive governance on top of that environment.

Finally, the book can make the cyber-physical shift feel nearly inevitable. A little resistance is useful. Connected devices are often sold as convenience before they become dependency. Procurement, standards, right-to-repair law, public alternatives, security requirements, and refusal can still shape what gets connected and on whose terms. So can environmental accounting: every "smart" object also has materials, energy use, update labor, repair constraints, and end-of-life waste.

The strongest limit is scope. DeNardis gives the governance architecture, not a full account of every domain where connected systems land. A workplace scanner, medical device, smart toy, home camera, farm sensor, border technology, connected car, and grid controller all require domain-specific safety and rights analysis. "Internet of Things" is too broad for remedy; the remedy has to name the setting.

What This Changes

The practical lesson is to audit the physical internet before adding machine intelligence to it. Ask what a connected system can sense, what it can change, who owns the records, who can inspect the code path, who can repair or disable it, what happens when the network fails, and how a harmed person can contest an automated action.

For institutions, the book reframes AI deployment as cyber-physical governance. A model inserted into transport, medicine, housing, policing, logistics, education, elder care, utilities, or workplace management is not just software. It is a decision layer attached to material dependency.

The implementation habit is simple: draw the control path before approving the feature. Signal, model, rule, actuator, record, override, repair, appeal, retirement. If any link is ownerless, invisible, or vendor-only, the system is not ready for a high-impact setting.

The Internet in Everything changes the default question from "is this device smart?" to "what kind of control network does this device join?" That is the right question for the next generation of AI systems. Intelligence that can only speak is already politically complicated. Intelligence connected to everything needs an off switch, an audit trail, and a public theory of who gets to touch the world through the network.

Source Discipline

This review separates four kinds of evidence: book metadata from publishers and scholarly catalogs; reception from review essays; author context from the author's own biography; and current governance claims from standards bodies, regulators, and official legal texts. That separation matters because a book review can explain an argument, but only primary sources can establish the current status of a rule, guidance document, or certification program.

Claims about a connected system should name the device class, connectivity, data recipient, actuator authority, support lifetime, jurisdiction, and enforcement instrument. Marketing language like "smart," "AI-powered," or "secure" is not evidence by itself. Nor is the existence of a standard proof that a person affected by a device can obtain repair, appeal, damages, or a safe alternative.

Source claims should also distinguish standards, labels, guidance, binding law, procurement terms, and sector-specific review. NIST and CISA documents often guide practice; ETSI specifies a standard; the FCC Cyber Trust Mark is voluntary; UK PSTI and the EU Cyber Resilience Act impose legal duties in their jurisdictions; FDA guidance addresses medical-device submissions and resilience. Collapsing those layers makes governance look stronger than it is.

Current claims were rechecked on June 25, 2026. This matters because IoT security labels, EU implementation dates, FDA guidance, NIST profiles, and sector-specific rules can change faster than a book review. The durable argument is DeNardis's infrastructure point; the regulatory details should be checked against the official sources before procurement, compliance, or safety decisions.

This review does not claim that AI systems are conscious, divine, or artificial general intelligence. The narrower claim is enough: once networked devices can sense and change the material environment, AI governance must cover the infrastructure through which model outputs become actions.

Sources

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