The Legal Landscape of Modern Surveillance

Surveillance tools are advancing faster than the laws that govern them. Drones, AI-driven video analytics, biometric recognition, and high-resolution imaging have moved from specialized use cases into routine claims work, investigations, and property assessments. These tools offer a sharper view of events and environments, but they also introduce new questions about privacy, admissibility, and responsibility.

In 2025, regulators at the federal, state, and local levels are actively reshaping how surveillance can be conducted. The Federal Aviation Administration (FAA) continues to define what happens in the air, while states increasingly define what counts as intrusion, trespass, or unlawful monitoring on the ground. At the same time, AI systems now interpret footage rather than simply record it, which has pushed courts and lawmakers to examine not only the video itself but how that video was processed, analyzed, and understood.

Public awareness has risen as well. Concerns about deepfakes, automated profiling, and data misuse have made surveillance practices more visible and more contestable. A tool that once seemed routine can now trigger regulatory scrutiny, reputational consequences, or evidentiary challenges.

Drone Surveillance

Drones have become a standard tool in claims investigations, surveillance, property inspections, and more. They’re fast, efficient, and they can capture angles that would be impossible from the ground. However, just because drones are common doesn’t mean using them is simple. In fact, the legal side is often where surveillance teams slip up.

At the federal level, drones are regulated by the FAA. Requirements commonly include drone registration, operator certification, adherence to airspace restrictions, and compliance with Remote ID, which mandates that most drones broadcast identifying information in flight.

However, the FAA only controls airspace. Capturing footage over private property falls under state and local privacy law, and that’s where things get tricky. States control issues like aerial trespass, privacy expectations, and whether certain footage is considered overly intrusive.

Example:

In Long Lake Township v. Maxon, local officials used drones to repeatedly record a homeowner’s backyard to investigate possible zoning violations without obtaining a warrant. The homeowners challenged the recordings as unlawful surveillance. The case moved through multiple levels of the state courts. While the legal debate continues, the broader principle is increasingly recognized: operating a drone in technically permitted airspace does not automatically make the resulting footage lawful or admissible. Courts have shown greater willingness to treat low-altitude, targeted drone observation as a potential invasion of reasonable privacy expectations.

This is where the “gray zone” comes in. Even though the FAA technically governs the air above private land, many states recognize that the first 100–200 feet above ground may still fall under private property rights. So, a drone hovering low over a backyard may comply with FAA rules yet still violate state privacy or nuisance law.

Because of these overlapping rules, claims teams using drones should treat them as high-value, high-risk tools. For claims and investigative professionals, that means:

• Confirm drone registration, operator certification, and Remote ID compliance
• Check state/local rules on aerial surveillance and consent
• Document flight paths, permissions, and property owner notifications
• Maintain clear chain of custody for footage
• Assume state privacy law determines admissibility, not just FAA rules

Drones provide a valuable view, but they also create exposure. A strong view from above is only useful if it holds up under legal scrutiny.

AI & Automated Surveillance is Rising

Surveillance is no longer as simple as setting up a camera and hitting record. Artificial intelligence, machine-learning analytics, behavioral tracking, and pattern-recognition tools now interpret what the camera sees. That shift has expanded both the capability of surveillance and the legal expectations that come with it.

Across the country, state legislatures are moving quickly. The National Conference of State Legislatures reports that dozens of AI-related bills have been introduced or passed in the last year, many focused on transparency and accountability in automated decision systems. At the federal level, recent White House policy initiatives signal renewed attention on how AI-enhanced surveillance tools are regulated and deployed.

When AI becomes part of a surveillance workflow, whether through facial recognition, movement pattern detection, or automated drone video review, the system changes from passive recorder to active interpreter. That raises new legal concerns.

Example:

Clearview AI scraped billions of images from social media to build a facial-recognition tool sold to law enforcement and private entities. Multiple lawsuits have argued that matching faces without consent violated biometric privacy laws, particularly in states like Illinois under its Biometric Information Privacy Act (BIPA). These cases illustrate how the interpretation of identity, not just the capture of images, can trigger liability.

Accuracy and bias also play a major role. The National Institute of Standards and Technology (NIST) found in large-scale tests that some facial-recognition systems showed false-match rates up to 100 times higher for individuals of certain racial and gender groups. Courts and regulators increasingly point to such data when questioning the reliability of AI-assisted evidence.

Two practical implications follow this shift toward interpretive surveillance:

1. Documentation and Transparency
   Legal challenges may involve explaining not only what the footage shows, but also how the AI system analyzed it. This includes how accuracy was tested, what error rates exist, how bias was addressed, and what validation data supports the output.
2. Consent and Notice
   When technology attempts to identify individuals or infer behavior, such as recognizing a face in a database or detecting physical capability, jurisdictions may classify the act as more intrusive than standard video capture, triggering additional disclosure or consent requirements.

Courts have become far more attuned to these issues. Common questions now include: How did the system decide what it flagged? What was the false-positive rate in this situation? What audit trail shows that the tool functioned as claimed?

Overall, AI makes surveillance more capable and more legally sensitive. Modern surveillance systems do not just “see” events; they interpret them. That interpretive function is where the legal risk increasingly lives, and where documentation, validation, and transparency matter most.

Privacy & Public Expectations Have Shifted

The growing use of drones, AI-driven analytics, and high-resolution surveillance tools has reshaped how privacy is understood. Privacy is no longer a fixed concept; it shifts as technology advances. Practices that once seemed routine may now carry reputational, regulatory, or legal consequences.

Public awareness is increasing as well. In June 2025, state lawmakers opposed a proposed federal 10 year freeze on state-level AI laws, arguing that states must retain the ability to protect residents from risks such as deepfakes, algorithmic discrimination, and unchecked surveillance expansion. This pushback reflects a broader trend: regulators are not stepping back, they are stepping in.

Legal guardrails are expanding, too. Several states have adopted requirements for transparency in automated decision systems, including disclosure when AI is used to evaluate individuals or influence outcomes. These policies are intended to prevent hidden profiling and to ensure that surveillance tools are subject to accountability standards similar to other forms of evidence.

Private-sector surveillance is being scrutinized alongside government oversight. Examples include employer monitoring software that tracks keystrokes and movement, drone-based observation of activity on private property, and AI systems that infer behavior from video footage. Even when technically lawful, these practices are increasingly assessed through the lens of ethics, proportionality, and public perception.

This shift has direct implications for investigative and claims environments:

• Courts and opposing counsel often expect the surveillance process to be documented as thoroughly as the underlying incident.
• Professional credibility may be evaluated not only on what was captured, but on how it was obtained.
• Reputational risk is significant. Overly intrusive or undisclosed surveillance can damage trust, prompt complaints, or trigger regulatory attention, particularly in states with strong privacy protections, such as California, Illinois, and Colorado.

A well-known example is enforcement under Illinois’ BIPA, where private companies have faced substantial penalties for collecting or analyzing biometric data, including facial recognition inputs, without clear consent. These cases send a consistent signal: surveillance tools themselves are now subject to scrutiny, not just the evidence they produce.

Transparency is becoming a core requirement. Effective policies increasingly include investigator training, clear documentation standards, internal review protocols, and legal oversight of how surveillance tools are selected, deployed, and stored.

Modern surveillance involves more than capturing footage, it involves accountability for the methods, technology, and decision-making behind that capture.

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Check out our sources:

“Clearview AI Litigation Overview.” Electronic Frontier Foundation (EFF), https://www.eff.org/cases/clearview-ai.

“Facial Recognition Vendor Test.” National Institute of Standards and Technology (NIST), U.S. Department of Commerce, 2019, https://www.nist.gov.

Federal Aviation Administration. Unmanned Aircraft Systems (UAS) Regulations and Remote ID Requirements. FAA, U.S. Department of Transportation, 2025.

Long Lake Township v. Maxon, 336 Mich. App. 521, 971 N.W.2d 893 (Mich. Ct. App. 2021), reconsideration denied, leave to appeal granted 2023.

National Conference of State Legislatures (NCSL). AI Legislation Tracker. NCSL, 2025, https://www.ncsl.org.

Reuters. “White House Plans to Expand U.S. AI Influence Abroad, Review Restrictions.” Reuters, 22 July 2025.

White & Case. State-Level AI and Automated Decision System Transparency Requirements. White & Case LLP, 2025.