When Robots Begin to 'See' Your Home: Data Security Compliance for Embodied Intelligence

A Shift Underway

Imagine a home service robot: it remembers your home's layout, recognizes your face and voice, knows when you wake up, and where you habitually place your keys. This is not science fiction, but the reality of embodied intelligence coming to life—in 2025, China's embodied intelligence market has approached one trillion yuan, with continued growth expected in 2026.

But this robot is also doing something traditional AI applications rarely do: turning the entire physical world into its training data. Cameras, microphones, force sensors, and LiDAR operate around the clock, collecting images, audio, spatial maps, human trajectories... This is entirely different from an app pop-up asking you to click 'agree' a few times.

Why This Time Is Different

The data issues of embodied intelligence are far more challenging than traditional internet data:

• It is 'immersive' collection, not 'interactive' collection. Mobile apps wait for your click before collecting data; robots record everything indiscriminately while performing tasks—including passersby, the appearance of your living room, and conversations of people nearby. The 'minimum necessary' principle has almost nowhere to anchor in physical space.

• Sensitivity is inherently higher. In scenarios like elderly care, home services, and medical rehabilitation, faces, voiceprints, gaits, and health conditions may be incidentally recorded, easily crossing the red line of sensitive personal information if not careful.

• Real-machine data is hard currency; simulation cannot replace it. The industry has reached a consensus: only data from real-world scenarios can train models that 'get the job done.' This means the collection of physical-world data will only deepen and broaden, and compliance pressure will rise accordingly.

• One data chain, five or six responsible parties. Hardware manufacturers, algorithm companies, data annotation outsourcers, scenario operators, cloud training service providers... any weak link can implicate the entire chain.

Regulatory Landscape

The good news is that the rules are no longer a blank slate:

• The Cybersecurity Law—the revised version effective from January 2026 includes dedicated AI provisions for the first time, officially embedding AI security into the national cybersecurity legal framework;

• Data cross-border rules are accelerating and refining: Measures for certification of personal information cross-border processing and cross-border compliance guidelines for the financial sector have been rolled out; the 'Automotive Data Cross-border Security Guidelines' issued in February 2026 refine the criteria for determining 'important data' by scenario—this approach is likely the blueprint for future embodied intelligence data cross-border rules;

• Industry standards are taking shape: The first national industry standard for embodied intelligence was released in 2026, though focused on performance testing, signaling that supporting standards for data collection specifications and dataset classification are not far off;

• Local governments are beginning to build 'sandboxes': Beijing has incorporated embodied intelligence dataset construction and AI data sandboxes into its industrial policies, providing enterprises with a controlled space for compliance experimentation.

Six Actions Enterprises Can Take

• Classify first, then manage. Categorize data collected by robots into three types: personal information/sensitive personal information, important data, and ordinary business data. The classification outcome directly determines storage duration, access permissions, and whether it can be used for model training.

• Bring 'informed consent' into physical spaces. Pop-up windows do not work with robots; use on-site signage and verbal notification instead. Distinguish between 'task-essential' and 'optional optimization' data, allowing users to refuse the latter. For scenarios involving the elderly or children, the consent threshold should be higher.

• Training data must be desensitized and traceable. Blur faces, process voiceprints, and avoid using raw biometric data for general model training unless necessary. Maintain complete records of collection scenarios and authorization basis to facilitate future self-certification.

• Plan for cross-border compliance in advance. Referring to the automotive data cross-border guidelines, identify in advance which data qualifies as 'important data' and which cross-border scenarios require security assessments. When using overseas chips or cloud services for joint training, pay special attention to cross-border nodes 'hidden in the technical architecture.'

• Assign compliance responsibilities to specific roles. Designate a dedicated person or committee to coordinate; field implementation and after-sales operations personnel should receive specialized data handling training and have emergency response plans.

• Proactively apply for pilot programs; do not wait for rules to fall from the sky. Data sandboxes and cross-border pre-assessment pilots are expanding. Early participation not only helps avoid pitfalls but also gives you a voice when rules are finalized.

The competition in embodied intelligence ultimately comes down to 'who can obtain real-world data faster, more abundantly, and more compliantly.' And this data is precisely derived from specific individuals and specific family spaces—making compliance not just a process before product launch, but a foundational capability that runs through design and operations from start to finish.