Internet Lawyer Blog

California businesses that use artificial intelligence, automated scoring, profiling tools, or large-scale consumer data practices cannot afford to treat the California Privacy Protection Agency’s 2026 regulations as a future problem. The rules governing Automated Decision-Making Technology (ADMT), privacy risk assessments, and cybersecurity audits are already reshaping compliance expectations. Businesses that wait until regulators come knocking may discover that they are missing the documentation, internal controls, and governance structure needed to defend their data practices.

This article was drafted to serve as practical guidance for businesses, executives, compliance teams, privacy professionals, and technology counsel trying to understand what California’s 2026 privacy regulations require. While many organizations focus on consumer-facing privacy notices, the more consequential issue in 2026 is operational readiness. Companies need to know when a risk assessment is required, when an automated decisionmaking workflow may trigger opt-out or access obligations, and when an annual cybersecurity audit becomes mandatory.

The reality is that many businesses already use tools that can fall within California’s automated decisionmaking framework. Hiring software, lead-scoring systems, fraud tools, underwriting models, identity verification services, recommendation engines, and internal profiling tools may all create compliance exposure depending on how they are used. In other words, a company does not need to market itself as an “AI business” to face AI-related privacy obligations.

Cryptocurrency fraud has become one of the fastest-growing forms of consumer financial crime. As digital assets gain mainstream adoption, criminals increasingly exploit confusion around blockchain technology, online anonymity, and cross-border transactions. Many consumers assume that once cryptocurrency is stolen, the perpetrators are impossible to identify or pursue. That assumption is often incorrect.

In reality, there are legal, forensic, and investigative methods available to track down cryptocurrency criminals, including those who target consumers in California and throughout the United States. While not every case results in full recovery, modern blockchain transparency and legal tools make crypto fraud far more traceable than many victims realize.

Understanding the Myth of Cryptocurrency Anonymity

Artificial intelligence (AI) has fundamentally transformed drone technology, shifting unmanned aerial systems (UAS) from remotely piloted tools into increasingly autonomous, data-driven platforms. What were once simple flying cameras are now capable of real-time decision-making, object recognition, predictive navigation, swarm coordination, and automated data analysis. This technological shift has not only expanded the commercial and governmental use of drones but has also created new legal, regulatory, privacy, and cybersecurity challenges. Understanding how AI has reshaped drone technology is essential for businesses, government agencies, and individuals operating in airspace, data-intensive environments, or regulated industries.

Evolution of Drones: From Manual Control to Intelligent Systems

Early drones relied almost entirely on human operators for navigation, stabilization, and mission execution. While GPS and basic sensors improved flight control, decision-making remained human-centric. Artificial intelligence introduced a new paradigm: autonomy.

Drones—also called unmanned aircraft systems (UAS)—are no longer niche tools limited to hobbyists. Today, drones are used for real estate marketing, construction progress monitoring, private security, agriculture, filmmaking, inspections, and emergency response. As drone usage increases, so do disputes involving privacy, property rights, cybersecurity, regulatory compliance, and personal injury. For individuals and businesses alike, understanding drone laws and how drone litigation works is essential to managing legal risk. This article provides an overview of major U.S. and California drone legal frameworks and highlights the most common litigation scenarios involving drones.

Federal Law: FAA Rules and Airspace Authority

In the United States, the Federal Aviation Administration (FAA) is the primary regulator of civil drone operations. The FAA’s rules determine where and how drones may fly, and violations can lead to civil penalties, enforcement actions, and operational restrictions. Most commercial drone operations fall under FAA Part 107, which generally requires:

We can confidently say that artificial intelligence law stopped being “emerging” in 2025. This was the year the courts, regulators, and legislators around the world started drawing real lines in the sand on copyright, data use, AI-washing, and high-risk systems—with obligations that will fully bite in 2026 and beyond. For in-house teams, founders, and boards, this year was less about theoretical risk and more about the following issues: what, exactly, is now illegal, what must we document, and how do we keep launching AI products without stepping on a legal landmine?

1. Copyright & IP: The “Fair Use Triangle” Takes Shape

This year gave us the first real cluster of U.S. decisions on whether using copyrighted works to train AI is fair use. The answer so far: it depends heavily on how you got the data and what you do with it.

Artificial intelligence (AI) has revolutionized document review, case analysis, and legal strategy. In the last five years, “technology-assisted review” (TAR) and newer generative AI tools have moved from experimental pilots to mainstream practice in U.S. litigation. For law firms, corporate counsel, and litigation support teams, AI in eDiscovery promises cost savings and efficiency—but it also brings admissibility challenges and ethical duties. This article explains the benefits, the federal and state evidentiary rules you must consider, and best practices for deploying AI in legal case management.

1. Benefits of AI in eDiscovery

Faster Document Review: Machine learning can quickly sort millions of documents, flagging those most likely to be responsive, privileged, or high-risk. Predictive coding drastically reduces attorney hours compared to manual review.

Introduction: AI Security Is the New Frontier

Artificial intelligence systems are no longer experimental and are embedded in financial fraud detection, autonomous vehicles, medical diagnostics, and critical infrastructure. Yet, AI security has lagged behind adoption. Hackers now target machine learning models directly, exploiting weaknesses unfamiliar to traditional IT teams. This article explains the top AI attack methods—adversarial examples, model poisoning, and data exfiltration—and outlines your legal obligations for breach response.

Understanding the AI Attack Surface