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California: New AI laws in California – roundup of the 2025 legislative session

AI & Algorithms, California, Children, Employment Law, Healthcare, Social Media , , , , , , , ,
This article was originally published by OneTrust DataGuidance on November 24, 2025 and can be found on the DataGuidance website here.

California introduces comprehensive AI laws focusing on transparency, children’s safety, healthcare, antitrust, and law enforcement.

California has taken an aggressive stance towards artificial intelligence (AI) legislation and will likely set the standard for other US states. Back in 2024, Governor Newsom vetoed comprehensive AI safety legislation under bill SB 1047 and advised caution on regulations for this nascent and important technology. This year, Governor Newsom pressed ahead with a full slate of new AI laws. The reasons for this change in approach are many, including but not limited to the lack of federal AI legislation, the growing concern over children’s interactions with AI, especially sexualized content, and harmonization with more stringent requirements in the EU and elsewhere.

This year’s legislative session set records for the number and scope of new AI laws. For the roundup this year, Lily Li, of Metaverse Law Corporation, breaks down the new AI laws by scope and sector, noting where this may add on to existing California legislation and rulemaking from 2024-2025.

General AI safety, transparency, and risk assessments

  • SB 53: Transparency in Frontier Artificial Intelligence Act (Wiener) – Starting in January 2026, California will require large frontier AI developers to publish a framework detailing how they incorporate safety, security, and testing standards into their AI models. SB 53 also creates a mechanism for AI developers and the public to report critical safety incidents, and protects internal whistleblowers who report risks posed by frontier AI models. The law establishes significant penalties for companies that fail to comply, with fines of up to $1 million per violation.
  • AB 316: Artificial Intelligence defenses (Krell) – This amends California’s Civil Code. If a party to a lawsuit develops, modifies, or uses AI, this law prohibits them from asserting as a defense that the AI autonomously caused the harm.
  • AB 853: California AI Transparency Act (Wicks) – This bill expands the existing AI Transparency Act and modifies the effective date from January 1, 2026, to August 2, 2026. The California AI Transparency Act requires covered generative AI developers to provide an AI-detection tool to assess whether image, video, or audio content is created or altered by generative AI. This bill adds to the existing law by requiring large online platforms to embed provenance data into generated content. Starting January 1, 2028, users will also have the option to include latent disclosures on ‘capture devices’ such as cameras, video recorders, and other recorders.

This new California approach to AI transparency and safety legislation needs to be read in conjunction with the following existing laws.

  • California Privacy Protection Agency’s (CPPA’s) recently approved Cyber, Risk, ADMT, and Insurance Regulations – The CPPA’s most recently updated 127-page regulation package contains requirements governing cybersecurity audits, risk assessments, and automated decision-making technology. AI developers and systems that process personal information and meet certain California privacy thresholds will now face new cybersecurity audit and risk assessment requirements. In addition, automated and significant decisions concerning the provision or denial of financial or lending services, housing, education enrollment or opportunities, employment or independent contracting opportunities or compensation, or healthcare services will trigger significant notice, opt-out, and risk assessment requirements.
  • AB 2013: AI Training Data Transparency Act (Irwin-2024) – Passed last year, this law will require covered generative AI developers to publish online a high-level summary of the datasets used in the development of the generative AI system or service, including but not limited to whether personal information or copyrighted information is included in the training data. The law is scheduled to go into effect on January 1, 2026.

Children’s safety, age verifications, and companion chatbots

  • SB243: Companion Chatbots (Padilla) – This law applies to chatbots that provide human-like interactions and are capable of sustaining relationships across multiple interactions. Beginning July 1, 2027, developers of these ‘companion chatbots’ will need to develop and report protocols addressing suicidal ideation and self-harm to regulators and the public. The law requires AI disclosures, referrals to suicide hotlines or crisis text lines, and break reminders. SB 243 further requires developers to institute reasonable measures to prevent the chatbot from producing visual material of sexually explicit conduct or directly stating that the minor should engage in sexually explicit conduct. The legislation includes a private right of action to individuals who suffer ‘an injury in fact’ with statutory damages of $1,000 per violation, or actual damages if greater.
  • AB 1043 – Digital Age Assurance Act (Wicks) – Starting January 1, 2027, operating systems and covered application stores will be required to obtain age data from users and pass on age bracket data to developers when users download and launch an application.
  • AB 56: Social Media Warning Law (Bauer-Kahan) – Starting January 1, 2027, covered social media platforms will need to display a warning label to minors the first time a user accesses the platform each day, after three hours of active use, as well as once per hour of cumulative active use after that. The warning label must say ‘The Surgeon General has warned that while social media may have benefits for some young users, social media is associated with significant mental health harms and has not been proven safe for young users.’
  • AB 621: Deepfake pornography (Bauer-Kahan) – This amends California’s Civil Code and expands protections against deepfake pornography. The law explicitly provides a cause of action against individuals who create or disclose deepfake pornography if they know, or reasonably should know, that the depicted individual was a minor and also provides a cause of action against individuals who knowingly facilitate or recklessly aid or abet the creation or disclosure of such nonconsensual deepfake pornography. The bill confirms that a minor cannot consent to the creation or distribution of deepfake pornography.

California’s approach to AI and children has a long and complicated history, and these new laws should be read in conjunction with the following laws on the books.

  • California Age Appropriate Design Code (Wicks) – This law was signed on September 15, 2022, and was scheduled to go into effect on July 1, 2024. Modeled after the UK Age Appropriate Design Code, this law requires businesses to conduct impact assessments, provide Privacy by Default, estimate the age of all users, and restrict dark patterns. The law was enjoined in March 2025, but is being appealed by the California Attorney General.
  • Protecting Our Kids from Social Media Addiction Act (Skinner-2024) – This law is scheduled to go into effect on January 1, 2027, and prohibits covered social media platforms from providing addictive feeds to minors without verifiable parental consent. The law has so far escaped a constitutional challenge, but may face other court challenges prior to the effective date.

Healthcare AI and chatbots

  • AB 489: Health care professions: deceptive terms or letters: artificial intelligence (Bonta) – This law prohibits AI systems from falsely indicating or implying possession of a medical license or certificate through advertising, marketing, or other functionality. AB 489 also makes AI developers directly subject to the healthcare professional licensing board or enforcement agency if they develop such a system. Each use of a prohibited term, letter, or phrase shall constitute a separate violation.

California’s approach to AI in healthcare also needs to be read in conjunction with the following laws and guidance.

  • Legal Advisory on the Application of Existing California Law to Artificial Intelligence in Healthcare – In January 2025, California Attorney General Rob Bonta issued this advisory, setting forth California’s existing consumer protection, civil rights, competition, and data privacy laws governing healthcare AI.
  • SB 1120: Physicians Make Decisions Act (Becker-2024) – This law prohibits covered healthcare service plans from denying, delaying, or changing healthcare services based, in whole or in part, on medical necessity using AI, algorithms, or other software tools. Such determinations shall require a physician or licensed healthcare professional and review of individual circumstances. This law also requires written policies and procedures governing such determinations.
  • AB 3030: Artificial Intelligence in Health Care Services (Calderon – 2024) – This law applies to health facilities, clinics, physicians’ offices, or other health group practices that use generative AI for communications about patient clinical information. Under this bill, generative AI, which pertains to clinical information, must include:
    • a disclaimer that indicates the communication was generated by AI at the beginning of the interaction; and
    • clear instructions on how the patient can contact the appropriate person.

Antitrust and pricing discrimination

  • AB 325: Cartwright Act violations (Aguiar-Curry) – This amends California’s existing antitrust law, the Cartwright Act, to explicitly cover ‘common pricing algorithms.’ The law prohibits:
    • the use or distribution of a ‘common pricing algorithm’ as part of a contract, combination in the form of a trust, or conspiracy to restrain trade or commerce; or
    • coercion to set or adopt a recommended price or term, recommended by the common pricing algorithm for the same or similar products or services.

Complaints shall not be required to allege facts tending to exclude the possibility of independent action.

Law enforcement use of AI

  • SB 524 Law Enforcement Agencies (Arreguín) – SB 524 requires law enforcement to disclose if an official report was written either fully or in part using AI, as well as retain the first draft created by AI and an associated audit trail that, at minimum, identifies both the officer who used AI to create a report and the video and audio footage used to create a report, if any. SB 524 also prohibits AI vendors from sharing, selling, or otherwise using information, except as provided in the bill (e.g., troubleshooting, bias mitigation, quality control, legal purposes, etc.).

Employment and bias

While Governor Newsom vetoed SB 7, the No Robo Bosses Act, the Governor’s veto letter pointed to the CPPA’s ADMT regulations as addressing some of the bill’s requirements. Per Governor Newsom, SB 7 is ‘partially covered’ by these regulations, as they ‘allow employees and independent contractors to better understand how their personal data is used by automated decision technology.’ In addition, the California Civil Rights Council’s recently promulgated regulations state that California’s antidiscrimination laws apply to AI workplace tools. These regulations address another concern raised in SB 7, which sought to prohibit ADS systems from inferring a worker’s protected status.

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EDPB Opinion on AI Models and GDPR Principles: Key Takeaways

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In December 2024, the European Data Protection Board (EDPB) issued an Opinion in response to a request from the Irish supervisory authority, focusing on the application of GDPR principles in the context of AI models. The Irish supervisory authority posed three specific questions:
  1. When and how can an AI model be considered “anonymous”?
  2. What is the appropriateness of legitimate interest as a legal basis for AI deployment and development?
  3. What are the consequences of unlawful processing of personal data on subsequent operations of the AI model?
Through its answers, the EDPB provided key guidance on how AI models interact with fundamental rights to privacy and data protection established in the GDPR.

Anonymous AI Models

According the EDPB, “[f]or a model to be anonymous, it should be very unlikely 1) to directly or indirectly identify individuals whose data was used to create the model, and 2) to extract such personal information from the model through queries.” While anonymous data can help mitigate privacy concerns, it does not automatically make the AI model completely exempt from GDPR compliance. When a model is claimed to be anonymous, supervisory authorities will evaluate the claims of anonymity on a case-by-case basis, considering “all the means likely to be used” by the controller or a user. The Opinion states that supervisory authorities should review the documentation provided by the controller when assessing if the model is truly anonymous. The EDPB outlines methods that the controller may use to demonstrate anonymity, which may include: 1) reducing the amount of personal data used during training, 2) taking steps to ensure this data cannot be identified, and 3) utilizing technical safeguards to prevent data extraction from the AI model using prompts or queries. Key Takeaway: If a business claims an AI model relies on anonymous data, the claims of anonymity should be substantiated on a case-by-case basis with sufficient evidence and documentation. To do this, businesses with allegedly anonymous AI models may need to implement technical measures to limit the collection of data, reduce the likelihood of data being identifiable, protect against that data being extracted by users during deployment, and create documentation capable of demonstrating these efforts.

Legitimate Interest as a Legal Basis

Under the GDPR, a legitimate interest may constitute a legal basis for companies to process personal data when they have a justifiable reason to do so (beyond obtaining consent). However, the legitimate interest should be balanced against the data subject’s rights and interests, which requires careful consideration and justification when processing information from data subjects. The Opinion provides a framework to assess if a legitimate interest can be a valid legal basis for processing personal data in AI development and deployment. The framework is comprised of a three-step test:
  1. Identify the legitimate interest pursued by the controller;
  2. Assess the necessity of the processing for purposes of the legitimate interest; and,
  3. Balance the legitimate interests against the rights and freedoms of the data subjects.
When conducting this test, the controller should be careful to identify an interest that is lawful, clearly articulated, and non-speculative. For example, a legitimate interest may be to develop an AI model’s conversational agent or to improve threat detection in an information system. The controller should also adhere to GDPR data minimization principles, which state that the processing activities must be proportionate and in line with only what is necessary to achieve the legitimate interest. Finally, controllers should conduct a nuanced balancing test. This test considers the unique circumstances of each case, which may include the data subject’s interest in retaining control over their data, personal benefits, or socioeconomic interest. The Opinion notes, the more precisely an interest is defined in relation to the purpose of the processing, the more precise the estimation of benefits and risks will be. By employing this framework, developers and deployers should be able to decrease the likelihood that their AI models are disproportionately infringing on individual privacy rights and better align their AI practices with GDPR requirements. Key Takeaway: The three-step analysis, according to the Opinion, is crucial to improving compliance for organizations relying on legitimate interest as a legal basis for processing in AI development or deployment. Organizations relying on legitimate interest in this AI context should review their processing activities to determine whether they are proportionate, transparent, and aligned with GDPR principles—like data minimization—to justify the reliance on legitimate interest as a legal basis for processing.

Consequences of Unlawful Processing

The Opinion notes that supervisory authorities enjoy discretionary powers to investigate and assess violations, and they can choose appropriate remedial measures based on the context of the case. However, the EDPB also provides guidance for the supervisory authorities, based on three scenarios.
  1. In the first scenario, personal data is retained in the AI model. The Opinion states that supervisory authorities will need to consider the surrounding circumstances of the AI model to determine if the development and deployment phases of the model involve different legitimate purposes for processing. If so, each should be examined separately.
  2. In the second scenario, personal data is retained in the model and is processed by another controller during deployment. In this instance, the supervisory authorities should determine if the deploying controller conducted an appropriate assessment to demonstrate accountability with Articles 5(1)(a) and 6 of the GDPR. This assessment should show that the AI model was not developed by unlawfully processing personal data.
  3. In the final scenario, a controller unlawfully processes personal data to develop the AI model, and then anonymizes the data before processing it in the context of deployment. The Opinion states that, if it can be demonstrated to the supervisory authorities that the deployment of the AI model does not entail the processing of personal data, then the GDPR does not apply. Therefore, the unlawfulness of the initial processing in development should not impact the deployment operation of the model.
While supervisory authorities do have substantial discretion in oversight of processing activities, the scenarios highlighted by the EDPB show that the development and deployment phases, while connected, may need to be evaluated independently. Key Takeaway: Organizations should proactively ensure compliance at both the development and deployment stages of an AI model. Supervisory authorities will likely use the above examples as guidance, emphasizing the important of demonstrating lawful practices through each stage of the model. The EDPB’s Opinion is an important guide for organizations navigating the intersection of AI and data privacy law. By addressing issues around anonymous AI models, legitimate interest, and lawful processing in development and deployment stages, the Opinion emphasizes responsible AI development. As AI technologies continue to advance, businesses should be aware of the ways supervisory authorities are overseeing their AI models. The insights provided by the EDPB provide a foundation to help businesses to advance and develop new AI models, while also helping to safeguard and protect the rights of individuals.
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CCPA Board Meeting: Key Takeaways from November 8, 2024

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In a vote of 4-1, the California Privacy Protection Agency (CPPA) has decided to move forward with rulemaking of its draft regulations concerning AI, cyber audits, profiling and risk assessments, despite complaints of regulatory overreach.

 

On Friday, November 8, the CCPA held a public meeting to discuss proposed updates to the California Consumer Privacy Act (CCPA) regulations. The hybrid meeting included public comments from a broad range of stakeholders – nearly 45 public comments were heard from business representatives, privacy advocates, and industry experts. While the passing vote would have typically triggered a 45-day public comment period on the draft regulations, Chairperson Urban requested flexibility, considering the upcoming holidays.

 

Legal Challenges

During the meeting, the CPPA stated that it was sued for failing to promulgate regulations, specifically on opt-out rights of information processed by automated decisionmaking tools (ADMTs). At the same time, commentators argued that the breadth of the proposed rules overstepped the intent of the CCPA.

 

Board Member Alastair Mactaggart–who helped draft the CCPA–voiced concerns about the regulations, arguing that the current proposed regulation is excessively broad to the point of being unworkable. He pointed out that these regulations, as written, apply to nearly all businesses that use any kind of software to generate any type of output–whether it’s AI-powered or not. For example, a simple tool like a spreadsheet or a school admission application could fall under these rules, forcing a large swath of low-risk businesses to conduct risk assessments. Mactaggart referred to this as statutory overreach and claimed that regulations should be focused on issues that genuinely impact privacy or security.

 

Economic Forecasts

The CPPA also issued a Standardized Regulatory Impact Assessment (SRIA) which was discussed during the meeting. In this assessment, the CPPA estimates the total cost of this regulatory initiative to be around $3.5 billion for the first year of implementation, with an average of $1 billion each subsequent year for the first ten years. The CPPA justifies this cost, asserting that the direct benefits to California businesses will be $1.5 billion in 2027, and $66.3 billion in 2036.

 

However, the California Chamber of Commerce states that “[b]usinesses, consumers and governments in California will suffer net losses from the proposed rules pending before the [CPPA] this week.” This statement stems from a report prepared for the Chamber of Commerce by Capitol Matrix Consulting, which concludes that the regulations are likely to “result in a substantial net losses to businesses, consumers, and governments in this state, both in the near and long term.”

 

Industry groups including TechNet, the Civil Justice Association of California, and the Interactive Advertising Bureau voiced concern about the heavy compliance burden that regulations place on businesses–especially small businesses that may not have the recourses to implement the required risk assessments or redesign their services to accommodate opt-out provisions.

 

Behavioral Advertising & Opt-Out Provisions

Another key point of contention during the meeting was the opt-out provision for consumers related to decisions made by AI systems.

 

The draft regulations govern a large range of AI. Under the draft, AI is defined as a “machine-based system that infers, from the input it receives, how to generate outputs that can influence physical or virtual environments.” Additionally, the draft defines ADMTs as “any technology that processes personal information and uses computation to execute a decision, replace human decisionmaking, or substantially facilitate human decisionmaking.”

 

Together, these definitions are more expansive than the definition of the high-risk automated processing addressed in Article 22 of the EU’s GDPR, the source of the original opt-out language. Under Article 22, a consumer has the right to opt out of decisions made by solely automated systems. The intent of this provision is to give consumers the ability to opt out of decisions that may be made on solely automated processes, such as targeted advertising.

 

However, critics argue that including the opt-out language in the draft in combination with an expansive definition of AI and ADMTs could have unintended consequences, especially for small businesses. Mactaggart, for instance, is concerned that applying this opt-out rule too broadly could lead to a breakdown of essential services. For example, online booking services for airlines and automated reservation software for hotels may rely on software that would be categorized as “AI” under this definition. Allowing users to opt out of using AI when asking for these services may be untenable, which could cause friction in these industries and ultimately could cause harm to consumers by limiting access to these services or increasing costs.

 

Risk Assessments

A central component of the draft regulation is for businesses who use AI, as defined above, to conduct risk assessments. While the goal of this requirement is to ensure that businesses are aware of and mitigate any potential privacy risks that arise from these technologies, critics believe the regulations go too far by applying the requirement to low risk, everyday activities.

 

For example, a representative from the California Grocery Association expressed concerns about how the opt-out provision would impact a chain of small rural grocery stores with whom she conducts business. While these AI tools could be used to help consumers save money, the cost of compliance to integrate these tools might not be within reach, especially given the thin profit margins within the grocery industry.

 

Again, Mactaggart questioned the scope of the draft. He and other advocates called for a narrower focus for risk assessments that centers on significant decisions–such as those that deny individuals access to essential goods and services. This could include the denial of a loan application, exclusion from an online platform, or an adverse employment decision. One commenter stated that there have been no public comments against regulating high-risk systems, and by focusing on these issues, the CPPA could better mitigate potential harms. At the same time, this would free low-risk systems from potential overregulation.

 

Additionally, a commentor suggested that risk assessments should be streamlined and aligned with other state standards to reduce compliance costs.  Mactaggart notes that accepting risk standards from other US jurisdictions could help businesses avoid duplicative efforts, cut compliance costs, and reduce the overall regulatory burden.

 

AI Training

The ability to opt out of training for AI datasets was of lesser concern but was still addressed by a number of commentors. For example, a representative from the Software and Data Industry Association argued that requiring an opt-out from consumers from AI dataset training could create a substantial burden on small businesses who already have trouble accumulating representative training data. Other commentors shares concerns that these opt-outs could compromise the quality and effectiveness for AI systems.

 

Ultimately, California faces a delicate balance in regulating AI and ADMT. On one hand, the state must work toward protecting consumers from privacy risks, potential discrimination, and other adverse impacts of AI. At the same time, the CPPA must ensure that rulemaking does not stifle innovation, create excessive compliance costs, or diminish competition between businesses that rely on AI.

 

As formal rulemaking moves forward, it will be crucial for the CPPA to consider feedback from the public comment period and to refine the regulations to ensure that they strike a balance between privacy concerns and costs to consumers and businesses alike.